Tootfinder

A robust SVM-based approach with feature selection and outliers detection for classification problems
Marta Baldomero-Naranjo, Luisa I. Mart\'inez-Merino, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2403.07753

A robust SVM-based approach with feature selection and outliers detection for classification problems
This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic ap…

Backdoor Attack with Mode Mixture Latent Modification
Hongwei Zhang, Xiaoyin Xu, Dongsheng An, Xianfeng Gu, Min Zhang
https://arxiv.org/abs/2403.07463 http…

Backdoor Attack with Mode Mixture Latent Modification
Backdoor attacks become a significant security concern for deep neural networks in recent years. An image classification model can be compromised if malicious backdoors are injected into it. This corruption will cause the model to function normally on clean images but predict a specific target label when triggers are present. Previous research can be categorized into two genres: poisoning a portion of the dataset with triggered images for users to train the model from scratch, or training a bac…

Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models
Joshua C Chang, Xiangting Li, Shixin Xu, Hao-Ren Yao, Julia Porcino, Carson Chow
https://arxiv.org/abs/2402.08151

Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models
We introduce a set of gradient-flow-guided adaptive importance sampling (IS) transformations to stabilize Monte-Carlo approximations of point-wise leave one out cross-validated (LOO) predictions for Bayesian classification models. One can leverage this methodology for assessing model generalizability by for instance computing a LOO analogue to the AIC or computing LOO ROC/PRC curves and derived metrics like the AUROC and AUPRC. By the calculus of variations and gradient flow, we derive two simp…

Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clustering
Yanting Yang, Beidi Zhao, Zhuohao Ni, Yize Zhao, Xiaoxiao Li
https://arxiv.org/abs/2403.08203 https://arxiv.org/pdf/2403.08203
arXiv:2403.08203v1 Announce Type: new
Abstract: Neuroscientific research has revealed that the complex brain network can be organized into distinct functional communities, each characterized by a cohesive group of regions of interest (ROIs) with strong interconnections. These communities play a crucial role in comprehending the functional organization of the brain and its implications for neurological conditions, including Autism Spectrum Disorder (ASD) and biological differences, such as in gender. Traditional models have been constrained by the necessity of predefined community clusters, limiting their flexibility and adaptability in deciphering the brain's functional organization. Furthermore, these models were restricted by a fixed number of communities, hindering their ability to accurately represent the brain's dynamic nature. In this study, we present a token clustering brain transformer-based model ($\texttt{TC-BrainTF}$) for joint community clustering and classification. Our approach proposes a novel token clustering (TC) module based on the transformer architecture, which utilizes learnable prompt tokens with orthogonal loss where each ROI embedding is projected onto the prompt embedding space, effectively clustering ROIs into communities and reducing the dimensions of the node representation via merging with communities. Our results demonstrate that our learnable community-aware model $\texttt{TC-BrainTF}$ offers improved accuracy in identifying ASD and classifying genders through rigorous testing on ABIDE and HCP datasets. Additionally, the qualitative analysis on $\texttt{TC-BrainTF}$ has demonstrated the effectiveness of the designed TC module and its relevance to neuroscience interpretations.

This https://arxiv.org/abs/2401.14287 has been replaced.
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Modelling Micro-Doppler Signature of Drone Propellers in Distributed ISAC
Integrated Sensing and Communication (ISAC) comprises detection and analysis of non-cooperative targets by exploiting the resources of the mobile radio system. In this context, micro-Doppler is of great importance for target classification, in order to distinguish objects with local movements. For developing algorithms for target classification, it is necessary to have a large amount of target signatures. Aiming to generate these data, this paper proposes a mathematical model for the micro-Dopp…

A robust SVM-based approach with feature selection and outliers detection for classification problems
Marta Baldomero-Naranjo, Luisa I. Mart\'inez-Merino, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2403.07753

A robust SVM-based approach with feature selection and outliers detection for classification problems
This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic ap…

ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
Zhiwei Liu, Boyang Liu, Paul Thompson, Kailai Yang, Raghav Jain, Sophia Ananiadou
https://arxiv.org/abs/2403.06765

ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
The internet has brought both benefits and harms to society. A prime example of the latter is misinformation, including conspiracy theories, which flood the web. Recent advances in natural language processing, particularly the emergence of large language models (LLMs), have improved the prospects of accurate misinformation detection. However, most LLM-based approaches to conspiracy theory detection focus only on binary classification and fail to account for the important relationship between mi…

Classification Using Global and Local Mahalanobis Distances
Annesha Ghosh, Anil K. Ghosh, Rita SahaRay, Soham Sarkar
https://arxiv.org/abs/2402.08283 https…

Classification Using Global and Local Mahalanobis Distances
We propose a novel semi-parametric classifier based on Mahalanobis distances of an observation from the competing classes. Our tool is a generalized additive model with the logistic link function that uses these distances as features to estimate the posterior probabilities of the different classes. While popular parametric classifiers like linear and quadratic discriminant analyses are mainly motivated by the normality of the underlying distributions, the proposed classifier is more flexible an…

Classification properties for some ternary structures
Alberto Miguel-G\'omez
https://arxiv.org/abs/2404.04381 https://arxiv.org/p…

Classification properties for some ternary structures
We provide a model-theoretic classification of the countable homogeneous $\mathbf{H}_4$-free 3-hypertournament studied by Cherlin, Hubička, Konečný, and Nešetřil. Our main result is that the theory of this structure is $\mathrm{SOP}_3$, $\mathrm{TP}_2$, and $\mathrm{NSOP}_4$. We offer two proofs of this fact: one is a direct proof, and the other employs part of the abstract machinery recently developed by Mutchnik.

This https://arxiv.org/abs/2305.13860 has been replaced.
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Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
Large Language Models (LLMs), like ChatGPT, have demonstrated vast potential but also introduce challenges related to content constraints and potential misuse. Our study investigates three key research questions: (1) the number of different prompt types that can jailbreak LLMs, (2) the effectiveness of jailbreak prompts in circumventing LLM constraints, and (3) the resilience of ChatGPT against these jailbreak prompts. Initially, we develop a classification model to analyze the distribution of …

The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models
Abeba Birhane, Sepehr Dehdashtian, Vinay Uday Prabhu, Vishnu Boddeti
https://arxiv.org/abs/2405.04623

The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models
Scale the model, scale the data, scale the GPU farms is the reigning sentiment in the world of generative AI today. While model scaling has been extensively studied, data scaling and its downstream impacts on model performance remain under-explored. This is particularly important in the context of multimodal datasets whose main source is the World Wide Web, condensed and packaged as the Common Crawl dump, which is known to exhibit numerous drawbacks. In this paper, we evaluate the downstream im…

This https://arxiv.org/abs/2301.06046 has been replaced.
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A two-tensor model with order-three
We construct a two-tensor model with order-3 and present its $W$-representation. Moreover we derive the compact expressions of correlators from the $W$-representation and analyze the free energy in large $N$ limit. In addition, we establish the correspondence between two colored Dyck walks in the Fredkin spin chain and tree operators in the ring. Based on the classification Dyck walks, we give the number of tree operators with the given level. Furthermore, we show the entanglement scaling of Fr…

AFEN: Respiratory Disease Classification using Ensemble Learning
Rahul Nadkarni, Emmanouil Nikolakakis, Razvan Marinescu
https://arxiv.org/abs/2405.05467 h…

AFEN: Respiratory Disease Classification using Ensemble Learning
We present AFEN (Audio Feature Ensemble Learning), a model that leverages Convolutional Neural Networks (CNN) and XGBoost in an ensemble learning fashion to perform state-of-the-art audio classification for a range of respiratory diseases. We use a meticulously selected mix of audio features which provide the salient attributes of the data and allow for accurate classification. The extracted features are then used as an input to two separate model classifiers 1) a multi-feature CNN classifier a…

'One size doesn't fit all': Learning how many Examples to use for In-Context Learning for Improved Text Classification
Manish Chandra, Debasis Ganguly, Yiwen Li, Iadh Ounis
https://arxiv.org/abs/2403.06402

'One size doesn't fit all': Learning how many Examples to use for In-Context Learning for Improved Text Classification
Predictive models in natural language processing (NLP) have evolved from training models from scratch to fine-tuning pre-trained models with labelled data. An extreme form of this fine-tuning involves in-context learning (ICL), where the output of a pre-trained generative model (frozen decoder parameters) is controlled only with variations in the input strings (called instructions or prompts). An important component of ICL is the use of a small number of labelled data instances as examples in t…

This https://arxiv.org/abs/2210.03859 has been replaced.
link: https://scholar.google.com/scholar?q=a

Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model
This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA). This method integrates the design ideas of the sample spectrally-corrected covariance matrix and the regularized discriminant analysis. With the support of a large-dimensional random matrix analysis framework, it is proved that SRLDA has a linear classification global optimal solution under the spiked model assumption. According to simulation data analysis, the SRLDA classifier …

This https://arxiv.org/abs/2309.08395 has been replaced.
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Learning by Self-Explaining
Current AI research mainly treats explanations as a means for model inspection. Yet, this neglects findings from human psychology that describe the benefit of self-explanations in an agent's learning process. Motivated by this, we introduce a novel approach in the context of image classification, termed Learning by Self-Explaining (LSX). LSX utilizes aspects of self-refining AI and human-guided explanatory machine learning. The underlying idea is that a learner model, in addition to optimizing …

Fair Graph Neural Network with Supervised Contrastive Regularization
Mahdi Tavassoli Kejani (UT3), Fadi Dornaika (IMT), Jean-Michel Loubes (IMT)
https://arxiv.org/abs/2404.06090 <…

Fair Graph Neural Network with Supervised Contrastive Regularization
In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation. However, challenges arise from biases that can be hidden not only in the node attributes but also in the connections between entities. Therefore, ensuring fairness in graph neural network learning has become a critical problem. To address this issue, we propose a novel model for training fairness-aware GNN, which enhances…

Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
https://arxiv.org/abs/2405.05255

Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo
Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks involving regression or classification problems. The cornerstone of modern cosmology is the ability to generate predictions for observed astrophysical fields from theory and constrain physical models from observations using these predictions. This work uses a single diffusion generative model to address these interlinke…

RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data
Yupeng Cao, Zhi Chen, Qingyun Pei, Fabrizio Dimino, Lorenzo Ausiello, Prashant Kumar, K. P. Subbalakshmi, Papa Momar Ndiaye
https://arxiv.org/abs/2404.07452

RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data
The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering (Q$\&$A), and stock movement prediction (binary classification), with a notable gap in the application of LLMs for financial risk prediction. Addressing this gap, in this paper, we introduce \textbf{RiskLabs}, a novel frame…

Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
Ricardo Bigolin Lanfredi, Pritam Mukherjee, Ronald Summers
https://arxiv.org/abs/2403.04024

Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports, but concerns exist about label quality. These datasets typically offer only presence labels, sometimes with binary uncertainty indicators, which limits their usefulness. In this work, we present MAPLEZ (Medical report Annotations with Privacy-preserving Large language model using Expeditious Zero shot answers), a novel approach leveraging a locally executable Large Language Model (LLM) t…

A Rate-Distortion-Classification Approach for Lossy Image Compression
Yuefeng Zhang
https://arxiv.org/abs/2405.03500 https://arxiv.or…

A Rate-Distortion-Classification Approach for Lossy Image Compression
In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classification (RDC) model for lossy image compression, offering a unified framework to op…

One-dimensional $\mathbb{Z}_4$ topological superconductor
Max Tymczyszyn, Edward McCann
https://arxiv.org/abs/2404.07633 https://arxi…

One-dimensional $\mathbb{Z}_4$ topological superconductor
We describe the mean-field model of a one-dimensional topological superconductor with two orbitals per unit cell. Time-reversal symmetry is absent, but a nonsymmorphic symmetry, involving a translation by a fraction of the unit cell, mimics the role of time-reversal symmetry. As a result, the topological superconductor has $\mathbb{Z}_4$ topological phases, two which support Majorana bound states and two which do not, in agreement with a prediction based on K-theory classification [K. Shiozaki …

This https://arxiv.org/abs/2402.16430 has been replaced.
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Improving behavior based authentication against adversarial attack using XAI
In recent years, machine learning models, especially deep neural networks, have been widely used for classification tasks in the security domain. However, these models have been shown to be vulnerable to adversarial manipulation: small changes learned by an adversarial attack model, when applied to the input, can cause significant changes in the output. Most research on adversarial attacks and corresponding defense methods focuses only on scenarios where adversarial samples are directly generat…

Uncovering Tidal Treasures: Automated Classification of Faint Tidal Features in DECaLS Data
Alexander J. Gordon, Annette M. N. Ferguson, Robert G. Mann
https://arxiv.org/abs/2404.06487

Uncovering Tidal Treasures: Automated Classification of Faint Tidal Features in DECaLS Data
Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will revolutionise the study of tidal features. However, the volume of data will far surpass the capacity to inspect each galaxy to identify the feature visually, thereby motivating an urgent need to develop automated detection methods. This paper presents a visual classifi…

Classification of permanence and impermanence for a Lotka-Volterra model of three competing species with seasonal succession
Lei Niu, Xizhuang Xie
https://arxiv.org/abs/2402.19213

Classification of permanence and impermanence for a Lotka-Volterra model of three competing species with seasonal succession
In this paper, we are concerned with the permanence of a Lotka-Volterra model of three competing species with seasonal succession. Based on the existence of a carrying simplex, that is a globally attracting hypersurface of codimension one, we provide a complete classification of the permanence and impermanence in terms of inequalities on the parameters of this model. Moreover, we numerically show that invariant closed curves can occur in the permanent classes, which means that the positive fixe…

Efficient Online Set-valued Classification with Bandit Feedback
Zhou Wang, Xingye Qiao
https://arxiv.org/abs/2405.04393 https://arxiv…

Efficient Online Set-valued Classification with Bandit Feedback
Conformal prediction is a distribution-free method that wraps a given machine learning model and returns a set of plausible labels that contain the true label with a prescribed coverage rate. In practice, the empirical coverage achieved highly relies on fully observed label information from data both in the training phase for model fitting and the calibration phase for quantile estimation. This dependency poses a challenge in the context of online learning with bandit feedback, where a learner …

This https://arxiv.org/abs/2311.05824 has been replaced.
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Towards a systematic study of non-thermal leptogenesis from inflaton decays
This paper investigates non-thermal leptogenesis from inflaton decays in the minimal extension of the canonical type-I seesaw model, where a complex singlet scalar $ϕ$ is introduced to generate the Majorana masses of right-handed neutrinos (RHNs) and to play the role of inflaton. First, we systematically study non-thermal leptogenesis with the least model dependence. We give a general classification of the parameter space and find four characteristic limits by carefully examining the interplay…

Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation
Yizhu Liu, Ran Tao, Shengyu Guo, Yifan Yang
https://arxiv.org/abs/2404.02616

Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation
Topic relevance between query and document is a very important part of social search, which can evaluate the degree of matching between document and user's requirement. In most social search scenarios such as Dianping, modeling search relevance always faces two challenges. One is that many documents in social search are very long and have much redundant information. The other is that the training data for search relevance model is difficult to get, especially for multi-classification relevance …

Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification
Tarik Crnovrsanin, Calvin Yu, Dane Hankamer, Cody Dunne
https://arxiv.org/abs/2403.00565

Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification
Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles…

Forget NLI, Use a Dictionary: Zero-Shot Topic Classification for Low-Resource Languages with Application to Luxembourgish
Fred Philippy, Shohreh Haddadan, Siwen Guo
https://arxiv.org/abs/2404.03912

Forget NLI, Use a Dictionary: Zero-Shot Topic Classification for Low-Resource Languages with Application to Luxembourgish
In NLP, zero-shot classification (ZSC) is the task of assigning labels to textual data without any labeled examples for the target classes. A common method for ZSC is to fine-tune a language model on a Natural Language Inference (NLI) dataset and then use it to infer the entailment between the input document and the target labels. However, this approach faces certain challenges, particularly for languages with limited resources. In this paper, we propose an alternative solution that leverages d…

This https://arxiv.org/abs/2404.02282 has been replaced.
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Smooth Deep Saliency
In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling, with the purpose of explaining how a deep learning model detects tumors in scanned histological tissue samples. Those methods make the investigated models more interpretable for gradient-based saliency maps, computed in hidden layers. We test our approach on different models trained for image classification on ImageNet1K, and models trained for tumor detection on Camelyon16 and…

Technical report on target classification in SAR track
Haonan Xu, Han Yinan, Haotian Si, Yang Yang
https://arxiv.org/abs/2405.02361 https://

Technical report on target classification in SAR track
This report proposes a robust method for classifying oceanic and atmospheric phenomena using synthetic aperture radar (SAR) imagery. Our proposed method leverages the powerful pre-trained model Swin Transformer v2 Large as the backbone and employs carefully designed data augmentation and exponential moving average during training to enhance the model's generalization capability and stability. In the testing stage, a method called ReAct is utilized to rectify activation values and utilize Energy…

ATAT: Astronomical Transformer for time series And Tabular data
G. Cabrera-Vives, D. Moreno-Cartagena, N. Astorga, I. Reyes-Jainaga, F. F\"orster, P. Huijse, J. Arredondo, A. M. Mu\~noz Arancibia, A. Bayo, M. Catelan, P. A. Est\'evez, P. S\'anchez-S\'aez, A. \'Alvarez, P. Castellanos, P. Gallardo, A. Moya, D. Rodriguez-Mancini
ht…

ATAT: Astronomical Transformer for time series And Tabular data
The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broke…

The sandpile model on the complete split graph: $q,t$-Schr\"oder polynomials, sawtooth polyominoes, and a cyclic lemma
Henri Derycke, Mark Dukes, Yvan Le Borgne
https://arxiv.org/abs/2402.15372

The sandpile model on the complete split graph: $q,t$-Schröder polynomials, sawtooth polyominoes, and a cyclic lemma
This paper builds on work initiated in Dukes (2021) that considered the classification of recurrent configurations of the Abelian sandpile model on the complete split graph. We introduce two statistics, wtopple$_{CTI}$ and wtopple$_{ITC}$, on sorted recurrent configurations. These statistics arise from two natural but different toppling conventions, CTI and ITC, for Dhar's burning algorithm as it is applied to the complete split graph. In addition, we introduce the bivariate $q,t$-CTI and $q,t$…

This https://arxiv.org/abs/2308.01392 has been replaced.
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Quantum Classical Algorithm for the Study of Phase Transitions in the Hubbard Model via Dynamical Mean-Field Theory
Simulating quantum many-body systems is believed to be one of the most promising applications of near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials science or strongly correlated systems. A promising avenue of research is to combine many-body physics with machine learning for the classification of distinct phases. In this paper, we propose a workflow that synergizes quantum computing, many-body theory, …

This https://arxiv.org/abs/2305.03357 has been replaced.
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Persistent homology of directed spaces
In this work, we explore links between natural homology and persistent homology for the classification of directed spaces. The former is an algebraic invariant of directed spaces, a semantic model of concurrent programs. The latter was developed in the context of topological data analysis, in which topological properties of point-cloud data sets are extracted while eliminating noise. In both approaches, the evolution homological properties are tracked through a sequence of inclusions of usual t…

This https://arxiv.org/abs/2404.02772 has been replaced.
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FPT: Feature Prompt Tuning for Few-shot Readability Assessment
Prompt-based methods have achieved promising results in most few-shot text classification tasks. However, for readability assessment tasks, traditional prompt methods lackcrucial linguistic knowledge, which has already been proven to be essential. Moreover, previous studies on utilizing linguistic features have shown non-robust performance in few-shot settings and may even impair model performance.To address these issues, we propose a novel prompt-based tuning framework that incorporates rich l…

NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification
Hanna Abi Akl
https://arxiv.org/abs/2402.16910

NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification
We present a neuro-symbolic (NeSy) workflow combining a symbolic-based learning technique with a large language model (LLM) agent to generate synthetic data for code comment classification in the C programming language. We also show how generating controlled synthetic data using this workflow fixes some of the notable weaknesses of LLM-based generation and increases the performance of classical machine learning models on the code comment classification task. Our best model, a Neural Network, ac…

This https://arxiv.org/abs/2306.07948 has been replaced.
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Optimal Inference in Contextual Stochastic Block Models
The contextual stochastic block model (cSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the cSBM has been widely used as a synthetic dataset for evaluating the performance of graph-neural networks (GNNs) for semi-supervised node classification. We consider a probabilistic Bayes-optimal formulation of the inference problem and we der…

This https://arxiv.org/abs/2403.14179 has been replaced.
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AdaProj: Adaptively Scaled Angular Margin Subspace Projections for Anomalous Sound Detection with Auxiliary Classification Tasks
The state-of-the-art approach for semi-supervised anomalous sound detection is to first learn an embedding space by using auxiliary classification tasks based on meta information or self-supervised learning and then estimate the distribution of normal data. In this work, AdaProj a novel loss function for training the embedding model is presented. In contrast to commonly used angular margin losses, which project data of each class as close as possible to their corresponding class centers, AdaPro…

Geometric Insights into Focal Loss: Reducing Curvature for Enhanced Model Calibration
Masanari Kimura, Hiroki Naganuma
https://arxiv.org/abs/2405.00442 htt…

Geometric Insights into Focal Loss: Reducing Curvature for Enhanced Model Calibration
The key factor in implementing machine learning algorithms in decision-making situations is not only the accuracy of the model but also its confidence level. The confidence level of a model in a classification problem is often given by the output vector of a softmax function for convenience. However, these values are known to deviate significantly from the actual expected model confidence. This problem is called model calibration and has been studied extensively. One of the simplest techniques …

This https://arxiv.org/abs/2403.16418 has been replaced.
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An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules
The increasing advancements in the field of machine learning have led to the development of numerous applications that effectively address a wide range of problems with accurate predictions. However, in certain cases, accuracy alone may not be sufficient. Many real-world problems also demand explanations and interpretability behind the predictions. One of the most popular interpretable models that are classification rules. This work aims to propose an incremental model for learning interpretabl…

This https://arxiv.org/abs/2309.01847 has been replaced.
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A Unified Geometric Model of Repeating and Non-Repeating Fast Radio Bursts
Fast radio bursts (FRBs) are millisecond-duration extragalactic radio transients. They apparently fall into repeaters and non-repeaters. However, such a classification has lacked a motivation on the physical picture. Here we propose a unified geometric model to distinguish between the repeaters and non-repeaters, in which the quasi-tangential (QT) propagation effect within the magnetospheric polar cap of a neutron star is considered. In this model, the non-repeaters arise from the sources whose…

MedMamba: Vision Mamba for Medical Image Classification
Yubiao Yue, Zhenzhang Li
https://arxiv.org/abs/2403.03849 https://arxiv.org/p…

MedMamba: Vision Mamba for Medical Image Classification
Medical image classification is a very fundamental and crucial task in the field of computer vision. These years, CNN-based and Transformer-based models are widely used in classifying various medical images. Unfortunately, The limitation of CNNs in long-range modeling capabilities prevent them from effectively extracting fine-grained features in medical images , while Transformers are hampered by their quadratic computational complexity. Recent research has shown that the state space model (SSM…

This https://arxiv.org/abs/2306.08543 has been replaced.
link: https://scholar.google.com/scholar?q=a

MiniLLM: Knowledge Distillation of Large Language Models
Knowledge Distillation (KD) is a promising technique for reducing the high computational demand of large language models (LLMs). However, previous KD methods are primarily applied to white-box classification models or training small models to imitate black-box model APIs like ChatGPT. How to effectively distill the knowledge of white-box LLMs into small models is still under-explored, which becomes more important with the prosperity of open-source LLMs. In this work, we propose a KD approach th…

Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning
Daixin Wang, Zhiqiang Zhang, Yeyu Zhao, Kai Huang, Yulin Kang, Jun Zhou
https://arxiv.org/abs/2403.06482

Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning
User financial default prediction plays a critical role in credit risk forecasting and management. It aims at predicting the probability that the user will fail to make the repayments in the future. Previous methods mainly extract a set of user individual features regarding his own profiles and behaviors and build a binary-classification model to make default predictions. However, these methods cannot get satisfied results, especially for users with limited information. Although recent efforts …

This https://arxiv.org/abs/2402.16896 has been replaced.
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On Trojan Signatures in Large Language Models of Code
Trojan signatures, as described by Fields et al. (2021), are noticeable differences in the distribution of the trojaned class parameters (weights) and the non-trojaned class parameters of the trojaned model, that can be used to detect the trojaned model. Fields et al. (2021) found trojan signatures in computer vision classification tasks with image models, such as, Resnet, WideResnet, Densenet, and VGG. In this paper, we investigate such signatures in the classifier layer parameters of large la…

Truth Factors
Robert E. Kent
https://arxiv.org/abs/2404.14470 https://arxiv.org/pdf/2404.14470<…

Truth Factors
Truth refers to the satisfaction relation used to define the semantics of model-theoretic languages. The satisfaction relation for first order languages (truth classification), and the preservation of truth by first order interpretations (truth infomorphism), is a motivating example in the theory of Information Flow (IF) (Barwise and Seligman 1997). The abstract theory of satisfaction is the basis for the theory of institutions (Goguen and Burstall 1992). Factoring refers to categorical factori…

Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models
Hyeonggeun Yun
https://arxiv.org/abs/2404.09828 http…

Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models
In computer vision, explainable AI (xAI) methods seek to mitigate the 'black-box' problem by making the decision-making process of deep learning models more interpretable and transparent. Traditional xAI methods concentrate on visualizing input features that influence model predictions, providing insights primarily suited for experts. In this work, we present an interaction-based xAI method that enhances user comprehension of image classification models through their interaction. Thus, we devel…

Puzzle game: Prediction and Classification of Wordle Solution Words
Haidong Xin, Fang Wu, Zhitong Zhou, Dongping Liu
https://arxiv.org/abs/2403.19433 https…

Puzzle game: Prediction and Classification of Wordle Solution Words
With the popularity of the Wordle game launched by the New York Times, more and more players are getting involved in this challenging game. Submitting the correct answer not only requires your luck, but is also influenced by various attributes of the word. For question 1, we preprocessed the original data by removing and replacing the abnormal data firstly. Then, we established an ARIMA-based prediction model for the number of reported results, with the parameters p=0, d=1, q=1 of the model d…

This https://arxiv.org/abs/2404.02282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Smooth Deep Saliency
In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling, with the purpose of explaining how a deep learning model detects tumors in scanned histological tissue samples. Those methods make the investigated models more interpretable for gradient-based saliency maps, computed in hidden layers. We test our approach on different models trained for image classification on ImageNet1K, and models trained for tumor detection on Camelyon16 and…

CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data
Shreya Sharma, Dana Hughes, Katia Sycara
https://arxiv.org/abs/2403.15974

CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data
This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains. Unlike traditional neural network models, which either generate an output for each provided input, or an output after a fixed sequence of inputs, the CBGT-Net learns to produce an output after a sufficient criteria for evidence is achieved from a stream of observed data. For each observation, the CBGT-Net generates a vector that explicitly represents th…

Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification
Sonal Joshi, Thomas Thebaud, Jes\'us Villalba, Najim Dehak
https://arxiv.org/abs/2402.19355

Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification
Adversarial examples have proven to threaten speaker identification systems, and several countermeasures against them have been proposed. In this paper, we propose a method to detect the presence of adversarial examples, i.e., a binary classifier distinguishing between benign and adversarial examples. We build upon and extend previous work on attack type classification by exploring new architectures. Additionally, we introduce a method for identifying the victim model on which the adversarial a…

A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images
Ganesh Babu, Aoife Gowen, Michael Fop, Isobel Claire Gormley
https://arxiv.org/abs/2403.03349

A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images
The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of spectroscopy instrumentation. Food engineers use hyperspectral images to classify the type and quality of a food sample, typically using classification methods. In order to train these methods, every pixel in each training image needs to be labelled. Typically, computationally cheap threshold-based approaches are used to label the pixels, and classification methods are traine…

KGAMC: A Novel Knowledge Graph Driven Automatic Modulation Classification Scheme
ike Li, Lu Yua, Fuhui Zhou, Qihui Wu, Naofal Al-Dhahir, Kai-Kit Wong
https://arxiv.org/abs/2402.19188

KGAMC: A Novel Knowledge Graph Driven Automatic Modulation Classification Scheme
Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven AMC schemes have achieved high accuracy. However, most of these schemes focus on generating additional prior knowledge or features of blind signals, which consumes longer computation time and ignores the interpretability of the model learning process. To solve these problems,…

mALBERT: Is a Compact Multilingual BERT Model Still Worth It?
Christophe Servan (ILES, STL), Sahar Ghannay (LISN), Sophie Rosset (LISN)
https://arxiv.org/abs/2403.18338

mALBERT: Is a Compact Multilingual BERT Model Still Worth It?
Within the current trend of Pretained Language Models (PLM), emerge more and more criticisms about the ethical andecological impact of such models. In this article, considering these critical remarks, we propose to focus on smallermodels, such as compact models like ALBERT, which are more ecologically virtuous than these PLM. However,PLMs enable huge breakthroughs in Natural Language Processing tasks, such as Spoken and Natural LanguageUnderstanding, classification, Question--Answering tasks. P…

Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model
Wei Xu, Jianlong Chen, Zhicheng Ding, Jinyin Wang
https://arxiv.org/abs/2404.17123

Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model
This paper explores the importance of text sentiment analysis and classification in the field of natural language processing, and proposes a new approach to sentiment analysis and classification based on the bidirectional gated recurrent units (GRUs) model. The study firstly analyses the word cloud model of the text with six sentiment labels, and then carries out data preprocessing, including the steps of removing special symbols, punctuation marks, numbers, stop words and non-alphabetic parts.…

M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values
Adam Catto, Nan Jia, Ansaf Salleb-Aouissi, Anita Raja
https://arxiv.org/abs/2405.00182

M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values
Missing value imputation is a crucial preprocessing step for many machine learning problems. However, it is often considered as a separate subtask from downstream applications such as classification, regression, or clustering, and thus is not optimized together with them. We hypothesize that treating the imputation model and downstream task model together and optimizing over full pipelines will yield better results than treating them separately. Our work describes a novel AutoML technique for m…

This https://arxiv.org/abs/2109.02473 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Robust Cybersecurity Topic Classification Tool
In this research, we use user defined labels from three internet text sources (Reddit, Stackexchange, Arxiv) to train 21 different machine learning models for the topic classification task of detecting cybersecurity discussions in natural text. We analyze the false positive and false negative rates of each of the 21 model's in a cross validation experiment. Then we present a Cybersecurity Topic Classification (CTC) tool, which takes the majority vote of the 21 trained machine learning models as…

This https://arxiv.org/abs/2304.14988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Topology of critical points in boundary matrix duals
Computation of topological charges of the Schwarzschild and charged black holes in AdS in canonical and grand canonical ensembles allows for a classification of the phase transition points via the Bragg-Williams off-shell free energy. We attempt a topological classification of the critical points and the equilibrium phases of the dual gauge theory via a phenomenological matrix model, which captures the features of the ${\cal{N}}=4$, $SU(N)$ Super Yang-Mills theory on $S^3$ at finite temperature…

Monoidal model structures on filtered chain complexes relating to spectral sequences
James A. Brotherston
https://arxiv.org/abs/2402.09207 https://<…

Monoidal model structures on filtered chain complexes relating to spectral sequences
We establish monoidal model structures on model categories of filtered chain complexes constructed by Cirici, Egas Santander, Livernet and Whitehouse whose weak equivalences are the quasi-isomorphisms on the $r$-page of the associated spectral sequences. In doing so we provide a partial classification of cofibrant objects and cofibrations of the model structures involving a boundedness restriction on the filtration. As a consequence we also obtain, by results of Schwede and Shipley, cofibrantly…

A model-free subdata selection method for classification
Rakhi Singh
https://arxiv.org/abs/2404.19127 https://arxiv.org/pdf/2404.1912…

A model-free subdata selection method for classification
Subdata selection is a study of methods that select a small representative sample of the big data, the analysis of which is fast and statistically efficient. The existing subdata selection methods assume that the big data can be reasonably modeled using an underlying model, such as a (multinomial) logistic regression for classification problems. These methods work extremely well when the underlying modeling assumption is correct but often yield poor results otherwise. In this paper, we propose …

This https://arxiv.org/abs/2402.19188 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

KGAMC: A Novel Knowledge Graph Driven Automatic Modulation Classification Scheme
Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven AMC schemes have achieved high accuracy. However, most of these schemes focus on generating additional prior knowledge or features of blind signals, which consumes longer computation time and ignores the interpretability of the model learning process. To solve these problems,…

M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values
Adam Catto, Nan Jia, Ansaf Salleb-Aouissi, Anita Raja
https://arxiv.org/abs/2405.00182

M-DEW: Extending Dynamic Ensemble Weighting to Handle Missing Values
Missing value imputation is a crucial preprocessing step for many machine learning problems. However, it is often considered as a separate subtask from downstream applications such as classification, regression, or clustering, and thus is not optimized together with them. We hypothesize that treating the imputation model and downstream task model together and optimizing over full pipelines will yield better results than treating them separately. Our work describes a novel AutoML technique for m…

Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification
Bidur Khanal, Prashant Shrestha, Sanskar Amgain, Bishesh Khanal, Binod Bhattarai, Cristian A. Linte
https://arxiv.org/abs/2402.16734

Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification
Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability. The test performance of a model tends to decrease as the label noise rate increases. Over recent years, several methods have been proposed to mitigate the impact of label noise in medical image classification and enhance the robustness of the model. Predominantly, these works have employed CNN-based architectures as the backbone of thei…

Explainable Risk Classification in Financial Reports
Xue Wen Tan, Stanley Kok
https://arxiv.org/abs/2405.01881 https://arxiv.org/pdf/…

Explainable Risk Classification in Financial Reports
Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different le…

This https://arxiv.org/abs/2309.13734 has been replaced.
link: https://scholar.google.com/scholar?q=a

Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification
Stance classification, the task of predicting the viewpoint of an author on a subject of interest, has long been a focal point of research in domains ranging from social science to machine learning. Current stance detection methods rely predominantly on manual annotation of sentences, followed by training a supervised machine learning model. However, this manual annotation process requires laborious annotation effort, and thus hampers its potential to generalize across different contexts. In th…

RankSHAP: a Gold Standard Feature Attribution Method for the Ranking Task
Tanya Chowdhury, Yair Zick, James Allan
https://arxiv.org/abs/2405.01848 https://…

RankSHAP: a Gold Standard Feature Attribution Method for the Ranking Task
Several works propose various post-hoc, model-agnostic explanations for the task of ranking, i.e. the task of ordering a set of documents, via feature attribution methods. However, these attributions are seen to weakly correlate and sometimes contradict each other. In classification/regression, several works focus on \emph{axiomatic characterization} of feature attribution methods, showing that a certain method uniquely satisfies a set of desirable properties. However, no such efforts have been…

What makes a small-world network? Leveraging machine learning for the robust prediction and classification of networks
Raima Carol Appaw, Nicholas Fountain-Jones, Michael A. Charleston
https://arxiv.org/abs/2403.13215

What makes a small-world network? Leveraging machine learning for the robust prediction and classification of networks
The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often simulation approaches involve selecting a suitable network generative model such as Erdös-Rényi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization. We utilize advances in interpretable machine learning to classify simulate…

Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise
Hyeonsu Jeong, Hye Won Chung
https://arxiv.org/abs/2402.10482

Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise
Self-distillation (SD) is the process of training a student model using the outputs of a teacher model, with both models sharing the same architecture. Our study theoretically examines SD in multi-class classification with cross-entropy loss, exploring both multi-round SD and SD with refined teacher outputs, inspired by partial label learning (PLL). By deriving a closed-form solution for the student model's outputs, we discover that SD essentially functions as label averaging among instances wi…

This https://arxiv.org/abs/2403.17767 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Asymptotic Bayes risk of semi-supervised learning with uncertain labeling
This article considers a semi-supervised classification setting on a Gaussian mixture model, where the data is not labeled strictly as usual, but instead with uncertain labels. Our main aim is to compute the Bayes risk for this model. We compare the behavior of the Bayes risk and the best known algorithm for this model. This comparison eventually gives new insights over the algorithm.

This https://arxiv.org/abs/2403.03849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

MedMamba: Vision Mamba for Medical Image Classification
Medical image classification is a very fundamental and crucial task in the field of computer vision. These years, CNN-based and Transformer-based models have been widely used to classify various medical images. Unfortunately, The limitation of CNNs in long-range modeling capabilities prevents them from effectively extracting features in medical images, while Transformers are hampered by their quadratic computational complexity. Recent research has shown that the state space model (SSM) represen…

This https://arxiv.org/abs/2405.01881 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Explainable Risk Classification in Financial Reports
Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different le…

This https://arxiv.org/abs/2310.13001 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Conversational Financial Information Retrieval Model (ConFIRM)
With the exponential growth in large language models (LLMs), leveraging their emergent properties for specialized domains like finance merits exploration. However, regulated fields such as finance pose unique constraints, requiring domain-optimized frameworks. We present ConFIRM, an LLM-based conversational financial information retrieval model tailored for query intent classification and knowledge base labeling. ConFIRM comprises two modules: 1) a method to synthesize finance domain-specif…

UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
Juhwan Choi, Yeonghwa Kim, Seunguk Yu, JungMin Yun, YoungBin Kim
https://arxiv.org/abs/2405.01022

UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited applicability for inference. Recent studies have suggested that PLMs be used as dataset generators and a tiny task-specific model be trained to achieve efficient inference. However, their applicability to various domains is limited because they tend to generate domain-specific datasets. In this work, we propose a nov…

This https://arxiv.org/abs/2402.01188 has been replaced.
link: https://scholar.google.com/scholar?q=a

Segment Any Change
Visual foundation models have achieved remarkable results in zero-shot image classification and segmentation, but zero-shot change detection remains an open problem. In this paper, we propose the segment any change models (AnyChange), a new type of change detection model that supports zero-shot prediction and generalization on unseen change types and data distributions. AnyChange is built on the segment anything model (SAM) via our training-free adaptation method, bitemporal latent matching. By…

Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification
Agus Hartoyo, Jan Argasi\'nski, Aleksandra Trenk, Kinga Przybylska, Anna B{\l}asiak, Alessandro Crimi
https://arxiv.org/abs/2402.09281

Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification
Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning model to achieve optimal class separability in binary classification tasks. Our approach is substanti…

Asymptotic Bayes risk of semi-supervised learning with uncertain labeling
Victor Leger, Romain Couillet
https://arxiv.org/abs/2403.17767 https://

Asymptotic Bayes risk of semi-supervised learning with uncertain labeling
This article considers a semi-supervised classification setting on a Gaussian mixture model, where the data is not labeled strictly as usual, but instead with uncertain labels. Our main aim is to compute the Bayes risk for this model. We compare the behavior of the Bayes risk and the best known algorithm for this model. This comparison eventually gives new insights over the algorithm.

This https://arxiv.org/abs/2403.03849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

MedMamba: Vision Mamba for Medical Image Classification
Medical image classification is a very fundamental and crucial task in the field of computer vision. These years, CNN-based and Transformer-based models have been widely used to classify various medical images. Unfortunately, The limitation of CNNs in long-range modeling capabilities prevents them from effectively extracting features in medical images, while Transformers are hampered by their quadratic computational complexity. Recent research has shown that the state space model (SSM) represen…

Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification
Garima Chhikara, Anurag Sharma, Kripabandhu Ghosh, Abhijnan Chakraborty
https://arxiv.org/abs/2402.18502

Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification
Employing Large Language Models (LLM) in various downstream applications such as classification is crucial, especially for smaller companies lacking the expertise and resources required for fine-tuning a model. Fairness in LLMs helps ensure inclusivity, equal representation based on factors such as race, gender and promotes responsible AI deployment. As the use of LLMs has become increasingly prevalent, it is essential to assess whether LLMs can generate fair outcomes when subjected to consider…

This https://arxiv.org/abs/2310.13001 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Conversational Financial Information Retrieval Model (ConFIRM)
With the exponential growth in large language models (LLMs), leveraging their emergent properties for specialized domains like finance merits exploration. However, regulated fields such as finance pose unique constraints, requiring domain-optimized frameworks. We present ConFIRM, an LLM-based conversational financial information retrieval model tailored for query intent classification and knowledge base labeling. ConFIRM comprises two modules: 1) a method to synthesize finance domain-specif…

This https://arxiv.org/abs/2112.02712 has been replaced.
link: https://scholar.google.com/scholar?q=a

Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease
We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estimat…

This https://arxiv.org/abs/2203.11155 has been replaced.
link: https://scholar.google.com/scholar?q=a

Quantum Neural Network with Density Matrix for Question Answering and Classical Image Classification
Quantum density matrix represents all the information of the entire quantum system, and novel models of meaning employing density matrices naturally model linguistic phenomena such as hyponymy and linguistic ambiguity, among others in quantum question answering tasks. Naturally, we argue that applying the quantum density matrix into classical Question Answering (QA) tasks can show more effective performance. Specifically, we (i) design a new mechanism based on Long Short-Term Memory (LSTM) to a…

A multi-stage semi-supervised learning for ankle fracture classification on CT images
Hongzhi Liu, Guicheng Li, Jiacheng Nie, Hui Tang, Chunfeng Yang, Qianjin Feng, Hailin Xu, Yang Chen
https://arxiv.org/abs/2403.19983

A multi-stage semi-supervised learning for ankle fracture classification on CT images
Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the corresponding segmentation dataset is established on the basis of fracture data. Secondly, the image registration method is used to register the bone …

One-Bit Quantization and Sparsification for Multiclass Linear Classification via Regularized Regression
Reza Ghane, Danil Akhtiamov, Babak Hassibi
https://arxiv.org/abs/2402.10474

One-Bit Quantization and Sparsification for Multiclass Linear Classification via Regularized Regression
We study the use of linear regression for multiclass classification in the over-parametrized regime where some of the training data is mislabeled. In such scenarios it is necessary to add an explicit regularization term, $λf(w)$, for some convex function $f(\cdot)$, to avoid overfitting the mislabeled data. In our analysis, we assume that the data is sampled from a Gaussian Mixture Model with equal class sizes, and that a proportion $c$ of the training labels is corrupted for each class. Under…

This https://arxiv.org/abs/2405.01022 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited applicability for inference. Recent studies have suggested that PLMs be used as dataset generators and a tiny task-specific model be trained to achieve efficient inference. However, their applicability to various domains is limited because they tend to generate domain-specific datasets. In this work, we propose a nov…

A multi-stage semi-supervised learning for ankle fracture classification on CT images
Hongzhi Liu, Guicheng Li, Jiacheng Nie, Hui Tang, Chunfeng Yang, Qianjin Feng, Hailin Xu, Yang Chen
https://arxiv.org/abs/2403.19983

A multi-stage semi-supervised learning for ankle fracture classification on CT images
Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the corresponding segmentation dataset is established on the basis of fracture data. Secondly, the image registration method is used to register the bone …

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Yi Hao Chan, Deepank Girish, Sukrit Gupta, Jing Xia, Chockalingam Kasi, Yinan He, Conghao Wang, Jagath C. Rajapakse
https://arxiv.org/abs/2405.00577

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. In this review, we provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks, w…

Pneumonia Diagnosis through pixels -- A Deep Learning Model for detection and classification
Amit Karanth Gurpur, Janani S, Ajeetha B, Brintha Therese A, Rajeswaran Rangasami
https://arxiv.org/abs/2404.12405

Pneumonia Diagnosis through pixels -- A Deep Learning Model for detection and classification
Manual identification and classification of pneumonia and COVID-19 infection is a cumbersome process that, if delayed can cause irreversible damage to the patient. We have compiled CT scan images from various sources, namely, from the China Consortium of Chest CT Image Investigation (CC-CCII), the Negin Radiology located at Sari in Iran, an open access COVID-19 repository from Havard dataverse, and Sri Ramachandra University, Chennai, India. The images were preprocessed using various methods su…

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Yi Hao Chan, Deepank Girish, Sukrit Gupta, Jing Xia, Chockalingam Kasi, Yinan He, Conghao Wang, Jagath C. Rajapakse
https://arxiv.org/abs/2405.00577

Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. In this review, we provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks, w…

This https://arxiv.org/abs/2402.12819 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
When solving NLP tasks with limited labelled data, researchers can either use a general large language model without further update, or use a small number of labelled examples to tune a specialised smaller model. In this work, we address the research gap of how many labelled samples are required for the specialised small models to outperform general large models, while taking the performance variance into consideration. By observing the behaviour of fine-tuning, instruction-tuning, prompting an…

Breast Cancer Image Classification Method Based on Deep Transfer Learning
Weimin Wang, Min Gao, Mingxuan Xiao, Xu Yan, Yufeng Li
https://arxiv.org/abs/2404.09226

Breast Cancer Image Classification Method Based on Deep Transfer Learning
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. This algorithm is based on the DenseNet structure of deep neural networks, and constructs a network model by introducing attention mechanisms, and trains the enhanced dataset using multi-level transfer learning. Experimental re…

ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents
Hoang-Thang Ta, Abu Bakar Siddiqur Rahman, Lotfollah Najjar, Alexander Gelbukh
#SMM4H (Social Media Mining for Health) 2024 Workshop, explicitly targeting the classification challenges within tweet data. Task 3 is a multi-class classification task centered on tweets discussing the impact of outdoor environments on symptoms of social anxiety. Task 5 involves a binary classification task focusing on tweets reporting medical disorders in children. We applied transfer learning from pre-trained encoder-decoder models such as BART-base and T5-small to identify the labels of a set of given tweets. We also presented some data augmentation methods to see their impact on the model performance. Finally, the systems obtained the best F1 score of 0.627 in Task 3 and the best F1 score of 0.841 in Task 5.

Generalizing deep learning models for medical image classification
Matta Sarah, Lamard Mathieu, Zhang Philippe, Alexandre Le Guilcher, Laurent Borderie, B\'eatrice Cochener, Gwenol\'e Quellec
https://arxiv.org/abs/2403.12167

Generalizing deep learning models for medical image classification
Numerous Deep Learning (DL) models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, some fundamental questions remain: are the DL models capable of generalizing? What causes a drop in DL model performances? How to overcome the DL model performance drop? Medical data are dynamic and p…

Generalizing deep learning models for medical image classification
Matta Sarah, Lamard Mathieu, Zhang Philippe, Alexandre Le Guilcher, Laurent Borderie, B\'eatrice Cochener, Gwenol\'e Quellec
https://arxiv.org/abs/2403.12167

Generalizing deep learning models for medical image classification
Numerous Deep Learning (DL) models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, some fundamental questions remain: are the DL models capable of generalizing? What causes a drop in DL model performances? How to overcome the DL model performance drop? Medical data are dynamic and p…

This https://arxiv.org/abs/2403.15454 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Emotion Detection with Transformers: A Comparative Study
In this study, we explore the application of transformer-based models for emotion classification on text data. We train and evaluate several pre-trained transformer models, on the Emotion dataset using different variants of transformers. The paper also analyzes some factors that in-fluence the performance of the model, such as the fine-tuning of the transformer layer, the trainability of the layer, and the preprocessing of the text data. Our analysis reveals that commonly applied techniques lik…

Single-Shared Network with Prior-Inspired Loss for Parameter-Efficient Multi-Modal Imaging Skin Lesion Classification
Peng Tang, Tobias Lasser
https://arxiv.org/abs/2403.19203

Single-Shared Network with Prior-Inspired Loss for Parameter-Efficient Multi-Modal Imaging Skin Lesion Classification
In this study, we introduce a multi-modal approach that efficiently integrates multi-scale clinical and dermoscopy features within a single network, thereby substantially reducing model parameters. The proposed method includes three novel fusion schemes. Firstly, unlike current methods that usually employ two individual models for for clinical and dermoscopy modalities, we verified that multimodal feature can be learned by sharing the parameters of encoder while leaving the individual modal-s…

This https://arxiv.org/abs/2404.01210 has been replaced.
link: https://scholar.google.com/scholar?q=a

AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis
In this paper, we present our team's submissions for SemEval-2024 Task-6 - SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The participants were asked to perform binary classification to identify cases of fluent overgeneration hallucinations. Our experimentation included fine-tuning a pre-trained model on hallucination detection and a Natural Language Inference (NLI) model. The most successful strategy involved creating an ensemble of these models, result…

LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
Masato Fujitake
https://arxiv.org/abs/2403.14252 https://

LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents. Visually Rich Document Understanding tasks, such as document image classification and information extraction, have gained significant attention due to their importance. Existing methods have been developed to enhance document comprehension by incorporating pre-training awareness of images, text, and layout structure. However, these methods require fine-tuning for each task and dataset, an…

This https://arxiv.org/abs/2403.12167 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Generalizing deep learning models for medical image classification
Numerous Deep Learning (DL) models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, some fundamental questions remain: are the DL models capable of generalizing? What causes a drop in DL model performances? How to overcome the DL model performance drop? Medical data are dynamic and p…