Tootfinder

Training Differentially Private Ad Prediction Models with Semi-Sensitive Features
Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash Varadarajan, Chiyuan Zhang
https://arXiv.org/abs/2401.15246<…

Training Differentially Private Ad Prediction Models with Semi-Sensitive Features
Motivated by problems arising in digital advertising, we introduce the task of training differentially private (DP) machine learning models with semi-sensitive features. In this setting, a subset of the features is known to the attacker (and thus need not be protected) while the remaining features as well as the label are unknown to the attacker and should be protected by the DP guarantee. This task interpolates between training the model with full DP (where the label and all features should be…

MEA-Defender: A Robust Watermark against Model Extraction Attack
Peizhuo Lv, Hualong Ma, Kai Chen, Jiachen Zhou, Shengzhi Zhang, Ruigang Liang, Shenchen Zhu, Pan Li, Yingjun Zhang
https://arXiv.org/abs/2401.15239

MEA-Defender: A Robust Watermark against Model Extraction Attack
Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained using deep learning algorithms. To protect the Intellectual Property (IP) of the original owners over such DNN models, backdoor-based watermarks have been extensively studied. However, most of such watermarks fail upon model extraction attack, which utilizes input samples to query the target model and obtains the corresponding outputs, thus training a substitute model using such input-output pairs. In this paper, w…

Introducing cosmosGPT: Monolingual Training for Turkish Language Models
H. Toprak Kesgin, M. Kaan Yuce, Eren Dogan, M. Egemen Uzun, Atahan Uz, H. Emre Seyrek, Ahmed Zeer, M. Fatih Amasyali
https://arxiv.org/abs/2404.17336

Introducing cosmosGPT: Monolingual Training for Turkish Language Models
The number of open source language models that can produce Turkish is increasing day by day, as in other languages. In order to create the basic versions of such models, the training of multilingual models is usually continued with Turkish corpora. The alternative is to train the model with only Turkish corpora. In this study, we first introduce the cosmosGPT models that we created with this alternative method. Then, we introduce new finetune datasets for basic language models to fulfill user r…

This https://arxiv.org/abs/2203.12476 has been replaced.
link: https://scholar.google.com/scholar?q=a

Stable Optimization for Large Vision Model Based Deep Image Prior in Cone-Beam CT Reconstruction
Large Vision Model (LVM) has recently demonstrated great potential for medical imaging tasks, potentially enabling image enhancement for sparse-view Cone-Beam Computed Tomography (CBCT), despite requiring a substantial amount of data for training. Meanwhile, Deep Image Prior (DIP) effectively guides an untrained neural network to generate high-quality CBCT images without any training data. However, the original DIP method relies on a well-defined forward model and a large-capacity backbone netw…

"... the attention pattern of a single layer can be ``nearly randomized'', while preserving the functionality of the network. We also show via extensive experiments that these constructions are not merely a theoretical artifact: even after severely constraining the architecture of the model, vastly different solutions can be reached via standard training."

Asymptotic Behavior of Adversarial Training Estimator under $\ell_\infty$-Perturbation
Yiling Xie, Xiaoming Huo
https://arXiv.org/abs/2401.15262 https://…

Asymptotic Behavior of Adversarial Training Estimator under $\ell_\infty$-Perturbation
Adversarial training has been proposed to hedge against adversarial attacks in machine learning and statistical models. This paper focuses on adversarial training under $\ell_\infty$-perturbation, which has recently attracted much research attention. The asymptotic behavior of the adversarial training estimator is investigated in the generalized linear model. The results imply that the limiting distribution of the adversarial training estimator under $\ell_\infty$-perturbation could put a posit…

This https://arxiv.org/abs/2401.13034 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Locality Sensitive Sparse Encoding for Learning World Models Online
Acquiring an accurate world model online for model-based reinforcement learning (MBRL) is challenging due to data nonstationarity, which typically causes catastrophic forgetting for neural networks (NNs). From the online learning perspective, a Follow-The-Leader (FTL) world model is desirable, which optimally fits all previous experiences at each round. Unfortunately, NN-based models need re-training on all accumulated data at every interaction step to achieve FTL, which is computationally expe…

This https://arxiv.org/abs/2211.13912 has been replaced.
link: https://scholar.google.com/scholar?q=a

Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact
In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs). Effective and efficient negative sampling methods that suit these models, however, remain underdeveloped. One challenge is that existing hard negative samplers tend to suffer from severer over-fitting in model training. In this work, we first study the reason behind the over-fitting, and illustrate it with the inco…

This https://arxiv.org/abs/2401.09720 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting
In this work, we propose a novel clothed human reconstruction method called GaussianBody, based on 3D Gaussian Splatting. Compared with the costly neural radiance based models, 3D Gaussian Splatting has recently demonstrated great performance in terms of training time and rendering quality. However, applying the static 3D Gaussian Splatting model to the dynamic human reconstruction problem is non-trivial due to complicated non-rigid deformations and rich cloth details. To address these challeng…

This https://arxiv.org/abs/2401.10282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

BioDiffusion: A Versatile Diffusion Model for Biomedical Signal Synthesis
Machine learning tasks involving biomedical signals frequently grapple with issues such as limited data availability, imbalanced datasets, labeling complexities, and the interference of measurement noise. These challenges often hinder the optimal training of machine learning algorithms. Addressing these concerns, we introduce BioDiffusion, a diffusion-based probabilistic model optimized for the synthesis of multivariate biomedical signals. BioDiffusion demonstrates excellence in producing high-…

Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment
Joachim Grimstad, Andrey Morozov
https://arxiv.org/abs/2402.18246 https:…

Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment
This paper presents a new approach to the solution of Probabilistic Risk Assessment (PRA) models using the combination of Reinforcement Learning (RL) and Graph Neural Networks (GNNs). The paper introduces and demonstrates the concept using one of the most popular PRA models - Fault Trees. This paper's original idea is to apply RL algorithms to solve a PRA model represented with a graph model. Given enough training data, or through RL, such an approach helps train generic PRA solvers that can op…

This https://arxiv.org/abs/2309.10718 has been replaced.
link: https://scholar.google.com/scholar?q=a

DRIVE: Data-driven Robot Input Vector Exploration
An accurate motion model is a fundamental component of most autonomous navigation systems. While much work has been done on improving model formulation, no standard protocol exists for gathering empirical data required to train models. In this work, we address this issue by proposing Data-driven Robot Input Vector Exploration (DRIVE), a protocol that enables characterizing uncrewed ground vehicles (UGVs) input limits and gathering empirical model training data. We also propose a novel learned s…

A Unified CPU-GPU Protocol for GNN Training
Yi-Chien Lin, Gangda Deng, Viktor Prasanna
https://arxiv.org/abs/2403.17092 https://arxiv…

A Unified CPU-GPU Protocol for GNN Training
Training a Graph Neural Network (GNN) model on large-scale graphs involves a high volume of data communication and compu- tations. While state-of-the-art CPUs and GPUs feature high computing power, the Standard GNN training protocol adopted in existing GNN frameworks cannot efficiently utilize the platform resources. To this end, we propose a novel Unified CPU-GPU protocol that can improve the resource utilization of GNN training on a CPU-GPU platform. The Unified CPU-GPU protocol instantiates …

Deep generative modelling of canonical ensemble with differentiable thermal properties
Shuo-Hui Li, Yao-Wen Zhang, Ding Pan
https://arxiv.org/abs/2404.18404

Deep generative modelling of canonical ensemble with differentiable thermal properties
We propose a variational modelling method with differentiable temperature for canonical ensembles. Using a deep generative model, the free energy is estimated and minimized simultaneously in a continuous temperature range. At optimal, this generative model is a Boltzmann distribution with temperature dependence. The training process requires no dataset, and works with arbitrary explicit density generative models. We applied our method to study the phase transitions (PT) in the Ising and XY mode…

This https://arxiv.org/abs/2402.10251 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Brant-2: Foundation Model for Brain Signals
Foundational models benefit from pre-training on large amounts of unlabeled data and enable strong performance in a wide variety of applications with a small amount of labeled data. Such models can be particularly effective in analyzing brain signals, as this field encompasses numerous application scenarios, and it is costly to perform large-scale annotation. In this work, we present the largest foundation model in brain signals, Brant-2. Compared to Brant, a foundation model designed for intra…

This https://arxiv.org/abs/2403.08715 has been replaced.
link: https://scholar.google.com/scholar?q=a

SOTOPIA-$π$: Interactive Learning of Socially Intelligent Language Agents
Humans learn social skills through both imitation and social interaction. This social learning process is largely understudied by existing research on building language agents. Motivated by this gap, we propose an interactive learning method, SOTOPIA-$π$, improving the social intelligence of language agents. This method leverages behavior cloning and self-reinforcement training on filtered social interaction data according to large language model (LLM) ratings. We show that our training method…

TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modality
Tiantian Feng, Xuan Shi, Rahul Gupta, Shrikanth S. Narayanan
https://arxiv.org/abs/2404.17983

TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modality
Automatic Speech Understanding (ASU) aims at human-like speech interpretation, providing nuanced intent, emotion, sentiment, and content understanding from speech and language (text) content conveyed in speech. Typically, training a robust ASU model relies heavily on acquiring large-scale, high-quality speech and associated transcriptions. However, it is often challenging to collect or use speech data for training ASU due to concerns such as privacy. To approach this setting of enabling ASU whe…

This https://arxiv.org/abs/2404.15620 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However, most of them request supervised pre-training on labelled datasets. This paper proposes an unsupervised kernel estimation model, named dynamic kernel prior (DKP), to realize an unsupervised and pre-training-free learning-based algorithm for solving the BSR problem. DKP can adaptively learn dynamic kernel priors to realize real-time kernel estimation, and thereby enables s…

Training exercise:
Try to replace all `$foo->isBeep()/$foo->canBar()` etc. calls with an instanceof check. $foo instanceof Beepable, $foo instanceof Bars, etc.
What does that do to your data model? If you leverage parameter types instead of manual instanceof checks, how does that simplify your logic flow?
I don't expect it to work for every use case, especially in PHP, but it would be a valuable exercise to try.

This https://arxiv.org/abs/2401.03451 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Optimization Over Trained Neural Networks: Taking a Relaxing Walk
Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compression, and optimization with learned constraints. However, solving these formulations soon becomes difficult as the network size grows due to the weak linear relaxation and dense constraint matrix. We have seen improvements in recent years with cutting plane algorithms, reformulations, and an heuristic based on Mixed-Inte…

ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, Yonghong Tian
https://arxiv.org/abs/2402.16445 https://arxiv.org/pdf/2402.16445
arXiv:2402.16445v1 Announce Type: new
Abstract: Large Language Models (LLMs), including GPT-x and LLaMA2, have achieved remarkable performance in multiple Natural Language Processing (NLP) tasks. Under the premise that protein sequences constitute the protein language, Protein Large Language Models (ProLLMs) trained on protein corpora excel at de novo protein sequence generation. However, as of now, unlike LLMs in NLP, no ProLLM is capable of multiple tasks in the Protein Language Processing (PLP) field. This prompts us to delineate the inherent limitations in current ProLLMs: (i) the lack of natural language capabilities, (ii) insufficient instruction understanding, and (iii) high training resource demands. To address these challenges, we introduce a training framework to transform any general LLM into a ProLLM capable of handling multiple PLP tasks. Specifically, our framework utilizes low-rank adaptation and employs a two-stage training approach, and it is distinguished by its universality, low overhead, and scalability. Through training under this framework, we propose the ProLLaMA model, the first known ProLLM to handle multiple PLP tasks simultaneously. Experiments show that ProLLaMA achieves state-of-the-art results in the unconditional protein sequence generation task. In the controllable protein sequence generation task, ProLLaMA can design novel proteins with desired functionalities. In the protein property prediction task, ProLLaMA achieves nearly 100\% accuracy across many categories. The latter two tasks are beyond the reach of other ProLLMs. Code is available at \url{https://github.com/Lyu6PosHao/ProLLaMA}.

Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
Yujia Yin, Xinyi Chen, Chenxiang Ma, Jibin Wu, Kay Chen Tan
https://arxiv.org/abs/2402.15969 https://arxiv.org/pdf/2402.15969
arXiv:2402.15969v1 Announce Type: new
Abstract: The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals. Recently, a novel multi-compartment spiking neuron model, namely the Two-Compartment LIF (TC-LIF) model, has been proposed and exhibited a remarkable capacity for sequential modelling. However, training the TC-LIF model presents challenges stemming from the large memory consumption and the issue of gradient vanishing associated with the Backpropagation Through Time (BPTT) algorithm. To address these challenges, online learning methodologies emerge as a promising solution. Yet, to date, the application of online learning methods in SNNs has been predominantly confined to simplified Leaky Integrate-and-Fire (LIF) neuron models. In this paper, we present a novel online learning method specifically tailored for networks of TC-LIF neurons. Additionally, we propose a refined TC-LIF neuron model called Adaptive TC-LIF, which is carefully designed to enhance temporal information integration in online learning scenarios. Extensive experiments, conducted on various sequential benchmarks, demonstrate that our approach successfully preserves the superior sequential modeling capabilities of the TC-LIF neuron while incorporating the training efficiency and hardware friendliness of online learning. As a result, it offers a multitude of opportunities to leverage neuromorphic solutions for processing temporal signals.

An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets
Jonathan Katzy, R\u{a}zvan-Mihai Popescu, Arie van Deursen, Maliheh Izadi
https://arxiv.org/abs/2403.15230

An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets
Does the training of large language models potentially infringe upon code licenses? Furthermore, are there any datasets available that can be safely used for training these models without violating such licenses? In our study, we assess the current trends in the field and the importance of incorporating code into the training of large language models. Additionally, we examine publicly available datasets to see whether these models can be trained on them without the risk of legal issues in the f…

This https://arxiv.org/abs/2401.14211 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy. Despite its potential benefits, FL is hindered by excessive communication costs due to repeated server-client communication during training. To address this challenge, model compression techniques, such as sparsification and weight clustering are applied, which often require modifying the underlying model aggregation schemes or involve cu…

This https://arxiv.org/abs/2311.07550 has been replaced.
link: https://scholar.google.com/scholar?q=a

Tabdoor: Backdoor Vulnerabilities in Transformer-based Neural Networks for Tabular Data
Deep Neural Networks (DNNs) have shown great promise in various domains. Alongside these developments, vulnerabilities associated with DNN training, such as backdoor attacks, are a significant concern. These attacks involve the subtle insertion of triggers during model training, allowing for manipulated predictions. More recently, DNNs for tabular data have gained increasing attention due to the rise of transformer models. Our research presents a comprehensive analysis of backdoor attacks on ta…

Why does music source separation benefit from cacophony?
Chang-Bin Jeon, Gordon Wichern, Fran\c{c}ois G. Germain, Jonathan Le Roux
https://arxiv.org/abs/2402.18407

Why does music source separation benefit from cacophony?
In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs. These random mixes have mismatched characteristics compared to real music, e.g., the different stems do not have consistent beat or tonality, resulting in a cacophony. In this work, we investigate why random mixing is effective when training a state-of-the-art music source separation model in spite of the apparent distribution…

OpenAI expands its Custom Model training program with "assisted fine-tuning", letting organizations set up data training pipelines, evaluation systems, and more (Kyle Wiggers/TechCrunch)
https://techcrunch.com/2024/04/04/openai-expands…

OpenAI expands its custom model training program | TechCrunch
OpenAI is expanding a program, Custom Model, to help enterprise customers develop tailored generative AI models using its technology for specific use

Out-of-distribution Rumor Detection via Test-Time Adaptation
Xiang Tao, Mingqing Zhang, Qiang Liu, Shu Wu, Liang Wang
https://arxiv.org/abs/2403.17735 http…

Out-of-distribution Rumor Detection via Test-Time Adaptation
Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Existing methods for rumor detection have achieved good performance, as they have collected enough corpus from the same data distribution for model training. However, significant distribution shifts between the training data and real-world test data occur due to differences in news topics, social media platforms, languages and the variance in propagation scale caused by news popularit…

"It also seems incoherent to me to isolate and pathologize “distraction” as a force that somehow obliterates rather than redirects attention."
Rob Horning on Sora and AI critihype.
https://robhorning.substack.com/p/a-photo-of-a-hamburger

A photo of a hamburger
Images are just videos with a single frame Last week, OpenAI posted this “technical report” (a.k.a an advertisement) for its video generation model in development, named Sora, complete with cherrypicked examples of its capabilities and bombastic claims of how it will someday overcome its intrinsic limitations and become a “world simulator” rather than a more capable mimic of its training data. At this stage in the hype cycle, we have been invited to ooh and aah about the model without b…

Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach
https://arxiv.org/abs/2402.18334

Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
We introduce Bonito, an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. Our goal is to enable zero-shot task adaptation of large language models on users' specialized, private data. We train Bonito on a new large-scale dataset with 1.65M examples created by remixing existing instruction tuning datasets into meta-templates. The meta-templates for a dataset produce training examples where the i…

From the Rundown newsletter interesting highlights from the Nvidia Keynote,
The Blackwell B200 GPU delivers 30x the performance of its H100 GPU predecessor while using 25x less energy.
Nvidia said the Blackwell innovations will allow for training for up to 10T parameter models.
Huang also revealed that GPT-4 contains 1.8T parameters and that 2000 Blackwell chips could finish training the model in 90 days.
The last point illustrates the enormous training costs of a model l…

This https://arxiv.org/abs/2305.15645 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

ConvGQR: Generative Query Reformulation for Conversational Search
In conversational search, the user's real search intent for the current turn is dependent on the previous conversation history. It is challenging to determine a good search query from the whole conversation context. To avoid the expensive re-training of the query encoder, most existing methods try to learn a rewriting model to de-contextualize the current query by mimicking the manual query rewriting. However, manually rewritten queries are not always the best search queries. Training a rewriti…

Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI
Jingyun Chen, Yading Yuan
https://arXiv.org/abs/2401.15434 …

Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI
Federated Learning (FL) enables collaborative model training among medical centers without sharing private data. However, traditional FL risks on server failures and suboptimal performance on local data due to the nature of centralized model aggregation. To address these issues, we present Gossip Mutual Learning (GML), a decentralized framework that uses Gossip Protocol for direct peer-to-peer communication. In addition, GML encourages each site to optimize its local model through mutual learni…

This https://arxiv.org/abs/2311.03628 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Reinforcement Twinning: from digital twins to model-based reinforcement learning
We propose a novel framework for simultaneously training the digital twin of an engineering system and an associated control agent. The training of the twin combines methods from adjoint-based data assimilation and system identification, while the training of the control agent combines model-based optimal control and model-free reinforcement learning. The training of the control agent is achieved by letting it evolve independently along two paths: one driven by a model-based optimal control and…

This https://arxiv.org/abs/2302.04914 has been replaced.
initial toot: https://mastoxiv.page/@a…

Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models
Accurate and comprehensive material databases extracted from research papers are critical for materials science and engineering but require significant human effort to develop. In this paper we present a simple method of extracting materials data from full texts of research papers suitable for quickly developing modest-sized databases. The method requires minimal to no coding, prior knowledge about the extracted property, or model training, and provides high recall and almost perfect precision …

Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
Chen Liu, Shibo He, Qihang Zhou, Shizhong Li, Wenchao Meng
https://arXiv.org/abs/2401.15123

Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations. Nevertheless, they typically demand extensive training data to acquire a generalizable representation map, which conflicts with scenarios of a few available samples, thereby limiting their performance. To overcome the limitation, we propose \textbf{AnomalyLLM}, a knowledge distillation-based time series anomaly detection approach where the student network is trained to m…

This https://arxiv.org/abs/2404.14212 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Toward Routing River Water in Land Surface Models with Recurrent Neural Networks
Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic characteristics. Training of such a model over the continental United States has demonstrated that a single set of model parameters can be used across independent catchments, and that RNNs can outperform physics-based models. In this work, we take a n…

This https://arxiv.org/abs/2312.02493 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Flexible Communication for Optimal Distributed Learning over Unpredictable Networks
Gradient compression alleviates expensive communication in distributed deep learning by sending fewer values and its corresponding indices, typically via Allgather (AG). Training with high compression ratio (CR) achieves high accuracy like DenseSGD, but has lower parallel scaling due to high communication cost (i.e., parallel efficiency). Using lower CRs improves parallel efficiency by lowering synchronization cost, but degrades model accuracy as well (statistical efficiency). Further, speedup …

This https://arxiv.org/abs/2211.15223 has been replaced.
link: https://scholar.google.com/scholar?q=a

Gamma-convergence of a nonlocal perimeter arising in adversarial machine learning
In this paper we prove Gamma-convergence of a nonlocal perimeter of Minkowski type to a local anisotropic perimeter. The nonlocal model describes the regularizing effect of adversarial training in binary classifications. The energy essentially depends on the interaction between two distributions modelling likelihoods for the associated classes. We overcome typical strict regularity assumptions for the distributions by only assuming that they have bounded $BV$ densities. In the natural topology …

This https://arxiv.org/abs/2311.07550 has been replaced.
link: https://scholar.google.com/scholar?q=a

Tabdoor: Backdoor Vulnerabilities in Transformer-based Neural Networks for Tabular Data
Deep Neural Networks (DNNs) have shown great promise in various domains. Alongside these developments, vulnerabilities associated with DNN training, such as backdoor attacks, are a significant concern. These attacks involve the subtle insertion of triggers during model training, allowing for manipulated predictions.More recently, DNNs for tabular data have gained increasing attention due to the rise of transformer models. Our research presents a comprehensive analysis of backdoor attacks on t…

This https://arxiv.org/abs/2403.14864 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Quadruped Locomotion Using Differentiable Simulation
While most recent advancements in legged robot control have been driven by model-free reinforcement learning, we explore the potential of differentiable simulation. Differentiable simulation promises faster convergence and more stable training by computing low-variant first-order gradients using the robot model, but so far, its use for legged robot control has remained limited to simulation. The main challenge with differentiable simulation lies in the complex optimization landscape of robotic …

Didn't hear about this. Interesting.
https://www.cultofmac.com/852660/apple-licenses-shutterstock-image-library-ai-model-training/

Apple licenses Shutterstock's massive image library for AI model training
To train its AI models, Apple reportedly struck a deal with Shutterstock to gain access to its library of photos and videos.

An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets
Jonathan Katzy, R\u{a}zvan-Mihai Popescu, Arie van Deursen, Maliheh Izadi
https://arxiv.org/abs/2403.15230

An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets
Does the training of large language models potentially infringe upon code licenses? Furthermore, are there any datasets available that can be safely used for training these models without violating such licenses? In our study, we assess the current trends in the field and the importance of incorporating code into the training of large language models. Additionally, we examine publicly available datasets to see whether these models can be trained on them without the risk of legal issues in the f…

That's not... those are not the units you want for that.

Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks
Hamid Reza Hashempour, Gilberto Berardinelli, Ramoni Adeogun, Shashi Raj Pandey
https://arxiv.org/abs/2404.18010

Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks
This paper investigates energy-efficient transmission protocols in relay-assisted federated learning (FL) setup within industrial subnetworks, considering latency and power constraints. In the subnetworks, devices collaborate to train a global model by transmitting their local models at the edge-enabled primary access (pAP) directly or via secondary access points (sAPs), which act as relays to optimize the training latency. We begin by formulating the energy efficiency problem for our proposed …

This https://arxiv.org/abs/2310.19046 has been replaced.
link: https://scholar.google.com/scholar?q=a

Large Language Models as Evolutionary Optimizers
Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers. The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model. This app…

Deep Learning with Tabular Data: A Self-supervised Approach
Tirth Kiranbhai Vyas
https://arXiv.org/abs/2401.15238 https://arXiv.org/p…

Deep Learning with Tabular Data: A Self-supervised Approach
We have described a novel approach for training tabular data using the TabTransformer model with self-supervised learning. Traditional machine learning models for tabular data, such as GBDT are being widely used though our paper examines the effectiveness of the TabTransformer which is a Transformer based model optimised specifically for tabular data. The TabTransformer captures intricate relationships and dependencies among features in tabular data by leveraging the self-attention mechanism of…

Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni, Prashant J. Nair, Divya Mahajan
https://arxiv.org/abs/2404.14632

Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
In this paper, we present a novel technique to search for hardware architectures of accelerators optimized for end-to-end training of deep neural networks (DNNs). Our approach addresses both single-device and distributed pipeline and tensor model parallel scenarios, latter being addressed for the first time. The search optimized accelerators for training relevant metrics such as throughput/TDP under a fixed area and power constraints. However, with the proliferation of specialized architectures…

#MindEye2: Shared-subject models enable fMRI-to-image with 1 hour of data https://arxiv.org/abs/2403.11207 Brain decoding of images using fMRI.;

MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are trained independently per subject where each subject requires dozens of hours of expensive fMRI training data to attain high-quality results. The present work showcases high-quality reconstructions using only 1 hour of fMRI training data. We pretrain our model across 7 subjects and then fine-tune on minimal data from a n…

Distributionally Robust Safe Screening
Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu, Noriaki Hashimoto, Taro Murayama, Lee Hanju, Shinya Kojima, Ichiro Takeuchi
https://arxiv.org/abs/2404.16328

Distributionally Robust Safe Screening
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating th…

This https://arxiv.org/abs/2403.16516 has been replaced.
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Visually Guided Generative Text-Layout Pre-training for Document Intelligence
Prior study shows that pre-training techniques can boost the performance of visual document understanding (VDU), which typically requires models to gain abilities to perceive and reason both document texts and layouts (e.g., locations of texts and table-cells). To this end, we propose visually guided generative text-layout pre-training, named ViTLP. Given a document image, the model optimizes hierarchical language and layout modeling objectives to generate the interleaved text and layout sequen…

MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction
Mahendra Gurve, Sankar Behera, Satyadev Ahlawat, Yamuna Prasad
https://arxiv.org/abs/2403.18580

MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction
The rise of Machine Learning as a Service (MLaaS) has led to the widespread deployment of machine learning models trained on diverse datasets. These models are employed for predictive services through APIs, raising concerns about the security and confidentiality of the models due to emerging vulnerabilities in prediction APIs. Of particular concern are model cloning attacks, where individuals with limited data and no knowledge of the training dataset manage to replicate a victim model's functio…

This https://arxiv.org/abs/2312.16554 has been replaced.
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A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Federated learning (FL) enables multiple clients to collaboratively learn a shared model without sharing their individual data. Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention. Differential privacy has emerged as a prevalent technique in FL, safeguarding the privacy of individual user data while impacting utility and training efficiency. Within Differential Privacy Federated Learning (DPFL), previous studies have primarily focused on t…

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Ravi Hassanaly, Camille Brianceau, Maëlys Solal, Olivier Colliot, Ninon Burgos
https://arXiv.org/abs/2401.16363

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has gained in popularity. This approach has the great advantage of not requiring tedious pixel-wise data annotation and offers possibility to generalize to any kind of anomalies, including that corresponding to rare diseases. By training a deep generative model with only images from healthy subjects, the model will learn to reconstruct pseudo-healthy images. This pseudo-healthy reconstruction is then compared …

Online Ecological Gearshift Strategy via Neural Network with Soft-Argmax Operator
Xi Luo, Shiying Dong, Jinlong Hong, Bingzhao Gao, Hong Chen
https://arxiv.org/abs/2402.18076

Online Ecological Gearshift Strategy via Neural Network with Soft-Argmax Operator
This paper presents a neural network optimizer with soft-argmax operator to achieve an ecological gearshift strategy in real-time. The strategy is reformulated as the mixed-integer model predictive control (MIMPC) problem to minimize energy consumption. Then the outer convexification is introduced to transform integer variables into relaxed binary controls. To approximate binary solutions properly within training, the soft-argmax operator is applied to the neural network with the fact that all …

This https://arxiv.org/abs/2404.13016 has been replaced.
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Optimizing Calibration by Gaining Aware of Prediction Correctness
Model calibration aims to align confidence with prediction correctness. The Cross-Entropy CE) loss is widely used for calibrator training, which enforces the model to increase confidence on the ground truth class. However, we find the CE loss has intrinsic limitations. For example, for a narrow misclassification, a calibrator trained by the CE loss often produces high confidence on the wrongly predicted class (e.g., a test sample is wrongly classified and its softmax score on the ground truth c…

This https://arxiv.org/abs/2402.15725 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Text-guided HuBERT: Self-Supervised Speech Pre-training via Generative Adversarial Networks
Human language can be expressed in either written or spoken form, i.e. text or speech. Humans can acquire knowledge from text to improve speaking and listening. However, the quest for speech pre-trained models to leverage unpaired text has just started. In this paper, we investigate a new way to pre-train such a joint speech-text model to learn enhanced speech representations and benefit various speech-related downstream tasks. Specifically, we propose a novel pre-training method, text-guided H…

This https://arxiv.org/abs/2401.05363 has been replaced.
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Generalizable Sleep Staging via Multi-Level Domain Alignment
Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from the same dataset. In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets. Inspired by existing domai…

(continued from previous post)...blackwell GPU will cost $ 30.000 (minimum), so training a GPT4 model with 2000 GPUs costs approx. $ 60 million ? (in 90 days, at a minimum because there are also other costs)
#training #GPT4

Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM
Xuan Zhang, Wei Gao
https://arxiv.org/abs/2404.17283

Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM
Retrieval-augmented language models have exhibited promising performance across various areas of natural language processing (NLP), including fact-critical tasks. However, due to the black-box nature of advanced large language models (LLMs) and the non-retrieval-oriented supervision signal of specific tasks, the training of retrieval model faces significant challenges under the setting of black-box LLM. We propose an approach leveraging Fine-grained Feedback with Reinforcement Retrieval (FFRR) …

A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters
Chunyu Xue, Weihao Cui, Han Zhao, Quan Chen, Shulai Zhang, Pengyu Yang, Jing Yang, Shaobo Li, Minyi Guo
https://arxiv.org/abs/2403.16125

A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters
Joint consideration of scheduling and adaptive parallelism offers great opportunities for improving the training efficiency of large models on heterogeneous GPU clusters. However, integrating adaptive parallelism into a cluster scheduler expands the cluster scheduling space. The new space is the product of the original scheduling space and the parallelism exploration space of adaptive parallelism (also a product of pipeline, data, and tensor parallelism). The exponentially enlarged scheduling s…

Generative diffusion model for surface structure discovery
Nikolaj R{\o}nne, Al\'an Aspuru-Guzik, Bj{\o}rk Hammer
https://arxiv.org/abs/2402.17404 http…

Generative diffusion model for surface structure discovery
We present a generative diffusion model specifically tailored to the discovery of surface structures. The generative model takes into account substrate registry and periodicity by including masked atoms and $z$-directional confinement. Using a rotational equivariant neural network architecture, we design a method that trains a denoiser-network for diffusion alongside a force-field for guided sampling of low-energy surface phases. An effective data-augmentation scheme for training the denoiser-n…

Didn't hear about this. Interesting.
https://www.cultofmac.com/852660/apple-licenses-shutterstock-image-library-ai-model-training/

Apple licenses Shutterstock's massive image library for AI model training
To train its AI models, Apple reportedly struck a deal with Shutterstock to gain access to its library of photos and videos.

CPR: Retrieval Augmented Generation for Copyright Protection
Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto
https://arxiv.org/abs/2403.18920

CPR: Retrieval Augmented Generation for Copyright Protection
Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training, to handle credit attribution, and to allow efficient machine unlearning at scale. However, RAG techniques for image generation may lead to parts of the retrieved samples being copied in the model's output. To reduce risks of leaking private information contained in the retrieved set, we introduce Copy-Protected generation with Retrieval (CPR), a new method …

GLA-Grad: A Griffin-Lim Extended Waveform Generation Diffusion Model
Haocheng LiuIP Paris, LTCI, IDS, S2A, Teysir BaouebIP Paris, LTCI, IDS, S2A, Mathieu FontaineIP Paris, LTCI, IDS, S2A, Jonathan Le RouxMERL, Gael RichardIP Paris, LTCI, IDS, S2A
https://arxiv.org/abs/2402.15516

GLA-Grad: A Griffin-Lim Extended Waveform Generation Diffusion Model
Diffusion models are receiving a growing interest for a variety of signal generation tasks such as speech or music synthesis. WaveGrad, for example, is a successful diffusion model that conditionally uses the mel spectrogram to guide a diffusion process for the generation of high-fidelity audio. However, such models face important challenges concerning the noise diffusion process for training and inference, and they have difficulty generating high-quality speech for speakers that were not seen …

Deep Learning for Traffic Flow Prediction using Cellular Automata-based Model and CNN-LSTM architecture
Zhaohui Yang, Kshitij Jerath
https://arxiv.org/abs/2403.18710

Deep Learning for Traffic Flow Prediction using Cellular Automata-based Model and CNN-LSTM architecture
Recent works have attempted to use deep learning to predict future states of traffic flow, but have met with mixed results. These approaches face two key challenges. First, training deep learning neural networks requires large amounts of training data which are not yet easily available for traffic flow systems. Second, even when data is available, the neural networks require access to historical data that covers most possible traffic flow dynamics to successfully predict future traffic states. …

Dynamics-Guided Diffusion Model for Robot Manipulator Design
Xiaomeng Xu, Huy Ha, Shuran Song
https://arxiv.org/abs/2402.15038 https://

Dynamics-Guided Diffusion Model for Robot Manipulator Design
We present Dynamics-Guided Diffusion Model, a data-driven framework for generating manipulator geometry designs for a given manipulation task. Instead of training different design models for each task, our approach employs a learned dynamics network shared across tasks. For a new manipulation task, we first decompose it into a collection of individual motion targets which we call target interaction profile, where each individual motion can be modeled by the shared dynamics network. The design o…

Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization
Shuo Yang, Gjergji Kasneci
https://arxiv.org/abs/2402.18284

Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization
Wide usage of ChatGPT has highlighted the potential of reinforcement learning from human feedback. However, its training pipeline relies on manual ranking, a resource-intensive process. To reduce labor costs, we propose a self-supervised text ranking approach for applying Proximal-Policy-Optimization to fine-tune language models while eliminating the need for human annotators. Our method begins with probabilistic sampling to encourage a language model to generate diverse responses for each inpu…

A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters
Chunyu Xue, Weihao Cui, Han Zhao, Quan Chen, Shulai Zhang, Pengyu Yang, Jing Yang, Shaobo Li, Minyi Guo
https://arxiv.org/abs/2403.16125

A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters
Joint consideration of scheduling and adaptive parallelism offers great opportunities for improving the training efficiency of large models on heterogeneous GPU clusters. However, integrating adaptive parallelism into a cluster scheduler expands the cluster scheduling space. The new space is the product of the original scheduling space and the parallelism exploration space of adaptive parallelism (also a product of pipeline, data, and tensor parallelism). The exponentially enlarged scheduling s…

CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans
Aissam Djahnine, Alexandre Popoff, Emilien Jupin-Delevaux, Vincent Cottin, Olivier Nempont, Loic Boussel
https://arxiv.org/abs/2403.18514

CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans
Unsupervised pathology detection can be implemented by training a model on healthy data only and measuring the deviation from the training set upon inference, for example with CNN-based feature extraction and one-class classifiers, or reconstruction-score-based methods such as AEs, GANs and Diffusion models. Normalizing Flows (NF) have the ability to directly learn the probability distribution of training examples through an invertible architecture. We leverage this property in a novel 3D NF-ba…

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Natalie Lang, Alejandro Cohen, Nir Shlezinger
https://arxiv.org/abs/2403.18375

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and a…

This https://arxiv.org/abs/2402.15861 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MATHWELL: Generating Educational Math Word Problems at Scale
Math word problems are critical K-8 educational tools, but writing them is time-consuming and requires domain expertise. We suggest that language models can support K-8 math education by automatically generating problems at scale. To be educational, generated problems must be 1) solvable, 2) accurate, and 3) appropriate. Existing datasets are unlabeled for these criteria, making them ill-suited for training problem generators. We introduce MATHWELL, a Llama-2 (70B) model iteratively finetuned t…

This https://arxiv.org/abs/2404.11766 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
Deep learning has been widely applied to solve partial differential equations (PDEs) in computational fluid dynamics. Recent research proposed a PDE correction framework that leverages deep learning to correct the solution obtained by a PDE solver on a coarse mesh. However, end-to-end training of such a PDE correction model over both solver-dependent parameters such as mesh parameters and neural network parameters requires the PDE solver to support automatic differentiation through the iterativ…

Leveraging power of deep learning for fast and efficient elite pixel selection in time series SAR interferometry
Ashutosh Tiwari, Nitheshnirmal Sadhashivam, Leonard O. Ohenhen, Manoochehr Shirzaei
https://arxiv.org/abs/2402.17069

Leveraging power of deep learning for fast and efficient elite pixel selection in time series SAR interferometry
This work proposes an improved convolutional long short-term memory (ConvLSTM) based architecture for selection of elite pixels (i.e., less noisy) in time series interferometric synthetic aperture radar (TS-InSAR). Compared to previous version, the model can process InSAR stacks of variable time steps and select both persistent (PS) and distributed scatterers (DS). We trained the model on ~20,000 training images (interferograms), each of size 100 by 100 pixels, extracted from InSAR time series …

Debiasing Cardiac Imaging with Controlled Latent Diffusion Models
Grzegorz Skorupko, Richard Osuala, Zuzanna Szafranowska, Kaisar Kushibar, Nay Aung, Steffen E Petersen, Karim Lekadir, Polyxeni Gkontra
https://arxiv.org/abs/2403.19508

Debiasing Cardiac Imaging with Controlled Latent Diffusion Models
The progress in deep learning solutions for disease diagnosis and prognosis based on cardiac magnetic resonance imaging is hindered by highly imbalanced and biased training data. To address this issue, we propose a method to alleviate imbalances inherent in datasets through the generation of synthetic data based on sensitive attributes such as sex, age, body mass index, and health condition. We adopt ControlNet based on a denoising diffusion probabilistic model to condition on text assembled fr…

This https://arxiv.org/abs/2311.13226 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models
We introduce an approach to building a custom model from ready-made self-supervised models via their associating instead of training and fine-tuning. We demonstrate it with an example of a humanoid robot looking at the mirror and learning to detect the 3D pose of its own body from the image it perceives. To build our model, we first obtain features from the visual input and the postures of the robot's body via models prepared before the robot's operation. Then, we map their corresponding latent…

This https://arxiv.org/abs/2301.03553 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

FedDebug: Systematic Debugging for Federated Learning Applications
In Federated Learning (FL), clients independently train local models and share them with a central aggregator to build a global model. Impermissibility to access clients' data and collaborative training make FL appealing for applications with data-privacy concerns, such as medical imaging. However, these FL characteristics pose unprecedented challenges for debugging. When a global model's performance deteriorates, identifying the responsible rounds and clients is a major pain point. Developers …

Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator
Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka
https://arxiv.org/abs/2403.16464

Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator
A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-quality characteristics. However, this data-driven model requires a large amount of training data incurring high data-collection costs. This fact motivates us to train a GAN-based vocoder on limited data. A promising solution is to augment the training data to avoid overfitting. However, a standard discriminator is uncond…

Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training
Yebo Wu, Li Li, Chunlin Tian, Chengzhong Xu
https://arxiv.org/abs/2404.13349

Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training
This paper presents ProFL, a novel progressive FL framework to effectively break the memory wall. Specifically, ProFL divides the model into different blocks based on its original architecture. Instead of updating the full model in each training round, ProFL first trains the front blocks and safely freezes them after convergence. Training of the next block is then triggered. This process iterates until the training of the whole model is completed. In this way, the memory footprint is effectivel…

This https://arxiv.org/abs/2104.10561 has been replaced.
link: https://scholar.google.com/scholar?q=a

Turning Federated Learning Systems Into Covert Channels
Federated learning (FL) goes beyond traditional, centralized machine learning by distributing model training among a large collection of edge clients. These clients cooperatively train a global, e.g., cloud-hosted, model without disclosing their local, private training data. The global model is then shared among all the participants which use it for local predictions. In this paper, we put forward a novel attacker model aiming at turning FL systems into covert channels to implement a stealth co…

This https://arxiv.org/abs/2403.16432 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

$\textit{LinkPrompt}$: Natural and Universal Adversarial Attacks on Prompt-based Language Models
Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks across various natural language processing (NLP) tasks. Instead of using a fixed prompt template to fine-tune the model, some research demonstrates the effectiveness of searching for the prompt via optimization. Such prompt optimization process of prompt-based learning on PLMs also gives insight into generating adver…

This https://arxiv.org/abs/2302.01622 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates that DP has negative implications on model accuracy and fairness, which are unacceptable in medicine and represent a main barrier to the widespread use of privacy-preserving techniq…

This https://arxiv.org/abs/2403.17636 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Mix-Initiative Response Generation with Dynamic Prefix Tuning
Mixed initiative serves as one of the key factors in controlling conversation directions. For a speaker, responding passively or leading proactively would result in rather different responses. However, most dialogue systems focus on training a holistic response generation model without any distinction among different initiatives. It leads to the cross-contamination problem, where the model confuses different initiatives and generates inappropriate responses. Moreover, obtaining plenty of human …

This https://arxiv.org/abs/2403.15721 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Radical-Cylon: A Heterogeneous Data Pipeline for Scientific Computing
Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficulties, impacting scientific fields like genomics, climate modeling, and astronomy. A large-scale solution like Google Pathways with a distributed execution environment for deep learning models exists but is proprietary. Integrating existing open-source, scalable runtime tools and data frameworks on high-performance comp…

A Study in Dataset Pruning for Image Super-Resolution
Brian B. Moser, Federico Raue, Andreas Dengel
https://arxiv.org/abs/2403.17083 https://

A Study in Dataset Pruning for Image Super-Resolution
In image Super-Resolution (SR), relying on large datasets for training is a double-edged sword. While offering rich training material, they also demand substantial computational and storage resources. In this work, we analyze dataset pruning as a solution to these challenges. We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model. By focusing the training on just 50% of the origin…

Bayesian Learned Models Can Detect Adversarial Malware For Free
Bao Gia Doan, Dang Quang Nguyen, Paul Montague, Tamas Abraham, Olivier De Vel, Seyit Camtepe, Salil S. Kanhere, Ehsan Abbasnejad, Damith C. Ranasinghe
https://arxiv.org/abs/2403.18309

Bayesian Learned Models Can Detect Adversarial Malware For Free
The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at the cost of sacrificing model performance for robustness. We hypothesize that adversarial malware exploits the low-confidence regions of models and can be identified using epistemic uncertainty of ML approaches -- epistemic uncertainty in a machine l…

How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
Andr\'e Ferreira, Naida Solak, Jianning Li, Philipp Dammann, Jens Kleesiek, Victor Alves, Jan Egger
https://arxiv.org/abs/2402.17317

How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for brain tumour segmentation, the first…

This https://arxiv.org/abs/2311.14153 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs
Imitation learning (IL) can train computationally-efficient sensorimotor policies from a resource-intensive Model Predictive Controller (MPC), but it often requires many samples, leading to long training times or limited robustness. To address these issues, we combine IL with a variant of robust MPC that accounts for process and sensing uncertainties, and we design a data augmentation (DA) strategy that enables efficient learning of vision-based policies. The proposed DA method, named Tube-NeRF…

Balanced Data Sampling for Language Model Training with Clustering
Yunfan Shao, Linyang Li, Zhaoye Fei, Hang Yan, Dahua Lin, Xipeng Qiu
https://arxiv.org/abs/2402.14526

Balanced Data Sampling for Language Model Training with Clustering
Data plays a fundamental role in the training of Large Language Models (LLMs). While attention has been paid to the collection and composition of datasets, determining the data sampling strategy in training remains an open question. Most LLMs are trained with a simple strategy, random sampling. However, this sampling strategy ignores the unbalanced nature of training data distribution, which can be sub-optimal. In this paper, we propose ClusterClip Sampling to balance the text distribution of t…

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
Hamza Saleem, Amir Ziashahabi, Muhammad Naveed, Salman Avestimehr
https://arxiv.org/abs/2403.17296

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
Training machine learning models on data from multiple entities without direct data sharing can unlock applications otherwise hindered by business, legal, or ethical constraints. In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models. We adopt a two-server model where data owners secret-share their data between two servers that train and evaluate the model on the joint data. A significant source of inefficiency a…

Convergence Analysis of Split Federated Learning on Heterogeneous Data
Pengchao Han, Chao Huang, Geng Tian, Ming Tang, Xin Liu
https://arxiv.org/abs/2402.15166

Convergence Analysis of Split Federated Learning on Heterogeneous Data
Split federated learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, where clients train one part in a parallel federated manner, and a main server trains the other. Despite the recent research on SFL algorithm development, the convergence analysis of SFL is missing in the literature, and this paper aims to fill this gap. The analysis of SFL can be more challenging than that of federate…

Robust Training of Federated Models with Extremely Label Deficiency
Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han
https://arxiv.org/abs/2402.14430 <…

Robust Training of Federated Models with Extremely Label Deficiency
Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Advanced FSSL methods predominantly focus on training a single model on each client. However, this approach could lead to a discrepancy between the objective functions of labeled and unlabeled data, resulting in gradient conflicts. To alleviate gradient conflict, we propose a novel twin-model paradigm, called Twin-sight, …

Leverage Variational Graph Representation For Model Poisoning on Federated Learning
Kai Li, Xin Yuan, Jingjing Zheng, Wei Ni, Falko Dressler, Abbas Jamalipour
https://arxiv.org/abs/2404.15042

Leverage Variational Graph Representation For Model Poisoning on Federated Learning
This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but also remains elusive to detection. VGAE-MP attack extracts graph structural correlations among the be…

This https://arxiv.org/abs/2402.16458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

D-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection
Swear words are a common proxy to collect datasets with cyberbullying incidents. Our focus is on measuring and mitigating biases derived from spurious associations between swear words and incidents occurring as a result of such data collection strategies. After demonstrating and quantifying these biases, we introduce ID-XCB, the first data-independent debiasing technique that combines adversarial training, bias constraints and debias fine-tuning approach aimed at alleviating model attention to …

Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering
Kosuke Akimoto, Kunihiro Takeoka, Masafumi Oyamada
https://arxiv.org/abs/2403.14197

Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering
Retrieval-augmented generation models augment knowledge encoded in a language model by providing additional relevant external knowledge (context) during generation. Although it has been shown that the quantity and quality of context impact the performance of retrieval-augmented generation models during inference, limited research explores how these characteristics affect model training. This paper explores how context quantity and quality during model training affect the performance of Fusion-i…

Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Christopher Wiedeman, Chuang Niu, Mengzhou Li, Bruno De Man, Jonathan S Maltz, Ge Wang
https://arxiv.org/abs/2402.16212

Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing. Deep learning is a possible method for super-resolution (SR), but sourcing paired training data that adequately models the target task is difficult. Additionally, SR algorithms can distort noise texture, which is an important in many clinical diagnostic scenarios. Here, we train conditional denoising diffusion probabilistic models (DDPMs) for PCCT…

This https://arxiv.org/abs/2403.16335 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipeline
The effectiveness of Deep Neural Networks (DNNs) heavily relies on the abundance and accuracy of available training data. However, collecting and annotating data on a large scale is often both costly and time-intensive, particularly in medical cases where practitioners are already occupied with their duties. Moreover, ensuring that the model remains robust across various scenarios of image capture is crucial in medical domains, especially when dealing with ultrasound images that vary based on t…

This https://arxiv.org/abs/2403.05030 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Defending Against Unforeseen Failure Modes with Latent Adversarial Training
AI systems sometimes exhibit harmful unintended behaviors post-deployment. This is often despite extensive diagnostics and debugging by developers. Minimizing risks from models is challenging because the attack surface is so large. It is not tractable to exhaustively search for inputs that may cause a model to fail. Red-teaming and adversarial training (AT) are commonly used to make AI systems more robust. However, they have not been sufficient to avoid many real-world failure modes that differ…

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…

$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Kl\"aser, B{\l}a\.zej Banaszewski, Samuel Maddrell-Mander, Callum McLean, Luis M\"uller, Ali Parviz, Shenyang Huang, Andrew Fitzgibbon
https://arxiv.org/abs/2404.14986

$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data and then transfer to low-data downstream tasks is a promising direction. However, how to design effective foundation models for molecular learning remains an open question, with existing approaches typically focusing on models with large parameter capacities. In this work, we propose $\texttt{MiniMol}$, a foundational …