Tootfinder

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…

This https://arxiv.org/abs/2409.14453 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Self-Attention Assistant Classification of Non-Hermitian Phases in Two-Dimensional Lattice
Classification of the non-Hermitian phases in high-dimensional lattice becomes challenging due to interplay of the band topology and non-Hermiticity. The significant increase in data dimensions and the number of categories has rendered traditional supervised learning and unsupervised manifold learning failed. Here, we propose the self-attention assistant machine learning for clustering non-Hermitian phases in two-dimensional lattice. By incorporating the self-attention mechanism, the model can …

UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
Md Rakibul Hasan, Md Zakir Hossain, Aneesh Krishna, Shafin Rahman, Tom Gedeon
https://arxiv.org/abs/2508.03520

UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
Supervised learning for empathy regression is challenged by noisy self-reported empathy scores. While many algorithms have been proposed for learning with noisy labels in textual classification problems, the regression counterpart is relatively under-explored. We propose UPLME, an uncertainty-aware probabilistic language modelling framework to capture label noise in the regression setting of empathy detection. UPLME includes a probabilistic language model that predicts both empathy score and he…

RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
Terry Yi Zhong, Cristian Tejedor-Garcia, Martha Larson, Bastiaan R. Bloem
https://arxiv.org/abs/2507.03594

RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification
Parkinson's Disease (PD) affects over 10 million people globally, with speech impairments often preceding motor symptoms by years, making speech a valuable modality for early, non-invasive detection. While recent deep-learning models achieve high accuracy, they typically lack the explainability required for clinical use. To address this, we propose RECA-PD, a novel, robust, and explainable cross-attention architecture that combines interpretable speech features with self-supervised representati…

AICO: Feature Significance Tests for Supervised Learning
Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn
https://arxiv.org/abs/2506.23396 https:…

AICO: Feature Significance Tests for Supervised Learning
The opacity of many supervised learning algorithms remains a key challenge, hindering scientific discovery and limiting broader deployment -- particularly in high-stakes domains. This paper develops model- and distribution-agnostic significance tests to assess the influence of input features in any regression or classification algorithm. Our method evaluates a feature's incremental contribution to model performance by masking its values across samples. Under the null hypothesis, the distributio…

Bi-Level Optimization for Self-Supervised AI-Generated Face Detection
Mian Zou, Nan Zhong, Baosheng Yu, Yibing Zhan, Kede Ma
https://arxiv.org/abs/2507.22824 https://

Bi-Level Optimization for Self-Supervised AI-Generated Face Detection
AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative techniques. To overcome this limitation, we introduce a self-supervised method based on bi-level optimization. In the inner loop, we pretrain a vision encoder only on photographic face images using a set of linearly weighted pretext tasks: classification of categorical exchangeable image file format (EXIF) tags, ranking o…

RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder
Yunfei Liu, Mingxuan Liu, Wupeng Xie, Xinzhu Liu, Wenxue Liu, Yangang Sun, Xin Qiu, Cui Yuan, Jinhai Li
https://arxiv.org/abs/2508.00274

RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder
Automatic modulation classification (AMC) is a basic technology in intelligent wireless communication systems. It is important for tasks such as spectrum monitoring, cognitive radio, and secure communications. In recent years, deep learning methods have made great progress in AMC. However, mainstream methods still face two key problems. First, they often use time-frequency images instead of raw signals. This causes loss of key modulation features and reduces adaptability to different communicat…

Identifying Radio Active Galactic Nuclei with Machine Learning and Large-Area Surveys
Xu-Liang Fan, Jie Li
https://arxiv.org/abs/2508.01596 https://arxiv.o…

Identifying Radio Active Galactic Nuclei with Machine Learning and Large-Area Surveys
Context. Active galactic nuclei (AGNs) and star forming galaxies (SFGs) are the primary sources of extragalactic radio sky. But it is difficult to distinguish the radio emission produced by AGNs from that by SFGs, especially when the radio sources are faint. Best et al. (2023) classified the radio sources in LoTSS Deep Fields DR1 through multiwavelength SED fitting. With the classification results of them, we perform a supervised machine learning to distinguish radio AGNs and radio SFGs. Aims…

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Xintong Hu, Yixuan Chen, Rui Yang, Wenxiang Guo, Changhao Pan
https://arxiv.org/abs/2507.06116

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
Automatic speech quality assessment plays a crucial role in the development of speech synthesis systems, but existing models exhibit significant performance variations across different granularity levels of prediction tasks. This paper proposes an enhanced MOS prediction system based on self-supervised learning speech models, incorporating a Mixture of Experts (MoE) classification head and utilizing synthetic data from multiple commercial generation models for data augmentation. Our method buil…

This https://arxiv.org/abs/2406.12007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor
Here we present the results of benchmarking a quantum processor based on trapped $^{171}$Yb$^{+}$ ions by performing basic quantum machine learning algorithms. Using a quantum-enhanced support vector machine algorithm with up to five qubits we perform a supervised binary classification on two types of datasets: small binary digit images and weighted graphs with a ring topology. For the first dataset, images are intentionally selected so that they could be classified with 100% accuracy. This all…

Synthetic Speech Source Tracing using Metric Learning
Dimitrios Koutsianos, Stavros Zacharopoulos, Yannis Panagakis, Themos Stafylakis
https://arxiv.org/abs/2506.02590

Synthetic Speech Source Tracing using Metric Learning
This paper addresses source tracing in synthetic speech-identifying generative systems behind manipulated audio via speaker recognition-inspired pipelines. While prior work focuses on spoofing detection, source tracing lacks robust solutions. We evaluate two approaches: classification-based and metric-learning. We tested our methods on the MLAADv5 benchmark using ResNet and self-supervised learning (SSL) backbones. The results show that ResNet achieves competitive performance with the metric le…

CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays
Rajesh Madhipati, Sheethal Bhat, Lukas Buess, Andreas Maier
https://arxiv.org/abs/2507.19398

CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays
Chest radiography (CXR) plays a crucial role in the diagnosis of various diseases. However, the inherent class imbalance in the distribution of clinical findings presents a significant challenge for current self-supervised deep learning models. These models often fail to accurately classify long-tailed classes. Current Vision-Language models such as Contrastive Language Image Pre-training (CLIP) models effectively model the manifold distribution of the latent space, enabling high zero-shot clas…

Jet Reconstruction with Mamba Networks in Collider Events
Jinmian Li, Peng Li, Bingwei Long, Rao Zhang
https://arxiv.org/abs/2506.18336 https://

Jet Reconstruction with Mamba Networks in Collider Events
We introduce a novel end-to-end framework for jet reconstruction in high-energy collider events, leveraging the efficiency and long-range modeling capabilities of the Mamba architecture. Our model unifies instance segmentation, classification, and kinematic regression into a single multi-task learning system, enabling a sophisticated multi-level reconstruction that simultaneously identifies primary heavy jets ($t$, $H$, $W/Z$) and their constituent sub-jets. To facilitate supervised learning fo…

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
https://arxiv.org/abs/2507.14072

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution, but the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained by the scarcity of labeled spectroscopic data. We aim to develop a scalable, label-efficient method for astronomical classification by leveraging semi-supervised learning (SSL) to overcome the limitations of fully supervised approaches. We propose a novel SSL fram…

Clustering-based hard negative sampling for supervised contrastive speaker verification
Piotr Masztalski, Micha{\l} Romaniuk, Jakub \.Zak, Mateusz Matuszewski, Konrad Kowalczyk
https://arxiv.org/abs/2507.17540

Clustering-based hard negative sampling for supervised contrastive speaker verification
In speaker verification, contrastive learning is gaining popularity as an alternative to the traditionally used classification-based approaches. Contrastive methods can benefit from an effective use of hard negative pairs, which are different-class samples particularly challenging for a verification model due to their similarity. In this paper, we propose CHNS - a clustering-based hard negative sampling method, dedicated for supervised contrastive speaker representation learning. Our approach c…

Focus on Texture: Rethinking Pre-training in Masked Autoencoders for Medical Image Classification
Chetan Madan, Aarjav Satia, Soumen Basu, Pankaj Gupta, Usha Dutta, Chetan Arora
https://arxiv.org/abs/2507.10869

Focus on Texture: Rethinking Pre-training in Masked Autoencoders for Medical Image Classification
Masked Autoencoders (MAEs) have emerged as a dominant strategy for self-supervised representation learning in natural images, where models are pre-trained to reconstruct masked patches with a pixel-wise mean squared error (MSE) between original and reconstructed RGB values as the loss. We observe that MSE encourages blurred image re-construction, but still works for natural images as it preserves dominant edges. However, in medical imaging, when the texture cues are more important for classific…

Scaling Self-Supervised Representation Learning for Symbolic Piano Performance
Louis Bradshaw, Honglu Fan, Alexander Spangher, Stella Biderman, Simon Colton
https://arxiv.org/abs/2506.23869

Scaling Self-Supervised Representation Learning for Symbolic Piano Performance
We study the capabilities of generative autoregressive transformer models trained on large amounts of symbolic solo-piano transcriptions. After first pretraining on approximately 60,000 hours of music, we use a comparatively smaller, high-quality subset, to finetune models to produce musical continuations, perform symbolic classification tasks, and produce general-purpose contrastive MIDI embeddings by adapting the SimCLR framework to symbolic music. When evaluating piano continuation coherence…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- DSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification
Cao, Cheng, Li, Zhou, Zhang, Li, Li, Gu, Zhang, Liu, Wu

General-purpose audio representation learning for real-world sound scenes
Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
https://arxiv.org/abs/2506.00934

General-purpose audio representation learning for real-world sound scenes
While audio foundation models perform well on myriad of tasks from sound classification to speech analysis, these models are trained and tested on dry, non-spatial, single-source audio clips. This limits their success in real-world situations and results in spatially unaware audio embeddings. To address these limitations, we propose a novel self-supervised training approach for General-Purpose, Real-world Audio Models (GRAMs). The GRAM training approach enables robust spatial audio representati…