pwc-archive/papers-with-abstracts huggingface dataset:
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
Authors: Yun Chu, Qiuhao Wang, Enze Zhou, Ling Fu, Qian Liu, Gang Zheng
Date: 2025-02-02 00:00:00
Tasks: Audio Classification, Clustering, Constrained Clustering, Contrastive Learning, Deep Clustering, Sound Classification

Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components in both types. Moreover, different abnormal respiratory sounds exhibit similar anomalous features, hindering their differentiation. Besides, existing state-of-the-art models suffer from excessive parameter size, impeding deployment on resource-constrained mobile platforms. To address these issues, we design a lightweight network CycleGuardian and propose a framework based on an improved deep clustering and contrastive learning. We first generate a hybrid spectrogram for feature diversity and grouping spectrograms to facilitating intermittent abnormal sound capture.Then, CycleGuardian integrates a deep clustering module with a similarity-constrained clustering component to improve the ability to capture abnormal features and a contrastive learning module with group mixing for enhanced abnormal feature discernment. Multi-objective optimization enhances overall performance during training. In experiments we use the ICBHI2017 dataset, following the official split method and without any pre-trained weights, our method achieves Sp: 82.06 $\%$, Se: 44.47$\%$, and Score: 63.26$\%$ with a network model size of 38M, comparing to the current model, our method leads by nearly 7$\%$, achieving the current best performances. Additionally, we deploy the network on Android devices, showcasing a comprehensive intelligent respiratory sound auscultation system.
Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through Preference-based Exploration
Authors: Mingyu Chen, Yiding Chen, Wen Sun, Xuezhou Zhang
Date: 2025-02-02 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for large language model (LLM) alignment. This paper studies the setting of online RLHF and focus on improving sample efficiency. All existing algorithms in online RLHF, whether doing passive exploration or active exploration, suffer from a sample complexity that scales exponentially with the scale of the reward function. This fundamental limitation hinders their effectiveness in scenarios with heavily skewed preferences, e.g. questions with a unique correct solution. To address this, we introduce Self-Exploring Preference-Incentive Online Preference Optimization (SE-POPO), an online RLHF algorithm that for the first time achieves a sample complexity that scales polynomially with the reward scale, answering an open problem raised by Xie et al. (2024).. Theoretically, we demonstrate that the sample complexity of SE-POPO dominates that of existing exploration algorithms. Empirically, our systematic evaluation confirms that SE-POPO is more sample-efficient than both exploratory and non-exploratory baselines, in two primary application scenarios of RLHF as well as on public benchmarks, marking a significant step forward in RLHF algorithm design.
Comprehensive Analysis of Bioactive Peptides from Cuminum cyminum L. Seeds: Sequence Identification and Pharmacological Evaluation
Authors: Ermatov Ismoil, Sumairemu subuer, Rena awuti, Haili qian Taurtaihon, Ahmidin wali, Yasen Mijiti
Date: 2025-02-02 00:00:00

Cuminum cyminum L. (cumin) is a medicinal and edible plant widely used in traditional Chinese medicine (TCM) for treating various ailments, including diarrhea, abdominal pain, inflammation, asthma, and diabetes. While previous research has primarily focused on its essential oils, studies on its protein-derived bioactive peptides remain limited. In this study, we employed an innovative extraction method to isolate peptides from cumin seeds for the first time and screened their biological activities, revealing significant antimicrobial, antioxidant, and hypoglycemic properties. Guided by bioactivity, we utilized advanced separation and structural identification techniques, including Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF/TOF MS/MS), to systematically purify and characterize cumin-derived peptides. A total of 479 unique peptide sequences were identified using Mascot software and the SwissProt/UniProt_Bos databases. Among these, 15 highly bioactive peptides were selected for further analysis based on bioactivity and toxicity predictions using PeptideRanker and ToxinPred. Structural characterization revealed key features, such as {\alpha}-helices and \b{eta}-sheets, associated with their multifunctional activities. This study provides the first comprehensive analysis of bioactive peptides from Cuminum cyminum L. seeds, elucidating their potential as antimicrobial, antioxidant, and hypoglycemic agents. These findings not only clarify the pharmacological basis of cumin's traditional uses but also lay a theoretical foundation for the development of novel therapeutic agents from this medicinal plant.
Sagalee: an Open Source Automatic Speech Recognition Dataset for Oromo Language
Authors: Turi Abu, Ying Shi, Thomas Fang Zheng, Dong Wang
Date: 2025-02-01 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We present a novel Automatic Speech Recognition (ASR) dataset for the Oromo language, a widely spoken language in Ethiopia and neighboring regions. The dataset was collected through a crowd-sourcing initiative, encompassing a diverse range of speakers and phonetic variations. It consists of 100 hours of real-world audio recordings paired with transcriptions, covering read speech in both clean and noisy environments. This dataset addresses the critical need for ASR resources for the Oromo language which is underrepresented. To show its applicability for the ASR task, we conducted experiments using the Conformer model, achieving a Word Error Rate (WER) of 15.32% with hybrid CTC and AED loss and WER of 18.74% with pure CTC loss. Additionally, fine-tuning the Whisper model resulted in a significantly improved WER of 10.82%. These results establish baselines for Oromo ASR, highlighting both the challenges and the potential for improving ASR performance in Oromo. The dataset is publicly available at https://github.com/turinaf/sagalee and we encourage its use for further research and development in Oromo speech processing.
Detecting Backdoor Samples in Contrastive Language Image Pretraining
Authors: Hanxun Huang, Sarah Erfani, Yige Li, Xingjun Ma, James Bailey
Date: 2025-02-03 00:00:00

Contrastive language-image pretraining (CLIP) has been found to be vulnerable to poisoning backdoor attacks where the adversary can achieve an almost perfect attack success rate on CLIP models by poisoning only 0.01\% of the training dataset. This raises security concerns on the current practice of pretraining large-scale models on unscrutinized web data using CLIP. In this work, we analyze the representations of backdoor-poisoned samples learned by CLIP models and find that they exhibit unique characteristics in their local subspace, i.e., their local neighborhoods are far more sparse than that of clean samples. Based on this finding, we conduct a systematic study on detecting CLIP backdoor attacks and show that these attacks can be easily and efficiently detected by traditional density ratio-based local outlier detectors, whereas existing backdoor sample detection methods fail. Our experiments also reveal that an unintentional backdoor already exists in the original CC3M dataset and has been trained into a popular open-source model released by OpenCLIP. Based on our detector, one can clean up a million-scale web dataset (e.g., CC3M) efficiently within 15 minutes using 4 Nvidia A100 GPUs. The code is publicly available in our \href{https://github.com/HanxunH/Detect-CLIP-Backdoor-Samples}{GitHub repository}.
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application
Authors: Gonzalo Iñaki Quintana, Laurence Vancamberg, Vincent Jugnon, Agnès Desolneux, Mathilde Mougeot
Date: 2025-01-28 00:00:00
Tasks: Contrastive Learning, Domain Adaptation

This work studies the relationship between Contrastive Learning and Domain Adaptation from a theoretical perspective. The two standard contrastive losses, NT-Xent loss (Self-supervised) and Supervised Contrastive loss, are related to the Class-wise Mean Maximum Discrepancy (CMMD), a dissimilarity measure widely used for Domain Adaptation. Our work shows that minimizing the contrastive losses decreases the CMMD and simultaneously improves class-separability, laying the theoretical groundwork for the use of Contrastive Learning in the context of Domain Adaptation. Due to the relevance of Domain Adaptation in medical imaging, we focused the experiments on mammography images. Extensive experiments on three mammography datasets - synthetic patches, clinical (real) patches, and clinical (real) images - show improved Domain Adaptation, class-separability, and classification performance, when minimizing the Supervised Contrastive loss.
Common Foundations for SHACL, ShEx, and PG-Schema
Authors: S. Ahmetaj, I. Boneva, J. Hidders, K. Hose, M. Jakubowski, J. E. Labra-Gayo, W. Martens, F. Mogavero, F. Murlak, C. Okulmus, A. Polleres, O. Savkovic, M. Simkus, D. Tomaszuk
Date: 2025-02-03 00:00:00
Tasks: Data Integration

Graphs have emerged as an important foundation for a variety of applications, including capturing and reasoning over factual knowledge, semantic data integration, social networks, and providing factual knowledge for machine learning algorithms. To formalise certain properties of the data and to ensure data quality, there is a need to describe the schema of such graphs. Because of the breadth of applications and availability of different data models, such as RDF and property graphs, both the Semantic Web and the database community have independently developed graph schema languages: SHACL, ShEx, and PG-Schema. Each language has its unique approach to defining constraints and validating graph data, leaving potential users in the dark about their commonalities and differences. In this paper, we provide formal, concise definitions of the core components of each of these schema languages. We employ a uniform framework to facilitate a comprehensive comparison between the languages and identify a common set of functionalities, shedding light on both overlapping and distinctive features of the three languages.
Neuro-Symbolic AI for Analytical Solutions of Differential Equations
Authors: Orestis Oikonomou, Levi Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas
Date: 2025-02-03 00:00:00
Tasks: Differential equation solution

Analytical solutions of differential equations offer exact insights into fundamental behaviors of physical processes. Their application, however, is limited as finding these solutions is difficult. To overcome this limitation, we combine two key insights. First, constructing an analytical solution requires a composition of foundational solution components. Second, iterative solvers define parameterized function spaces with constraint-based updates. Our approach merges compositional differential equation solution techniques with iterative refinement by using formal grammars, building a rich space of candidate solutions that are embedded into a low-dimensional (continuous) latent manifold for probabilistic exploration. This integration unifies numerical and symbolic differential equation solvers via a neuro-symbolic AI framework to find analytical solutions of a wide variety of differential equations. By systematically constructing candidate expressions and applying constraint-based refinement, we overcome longstanding barriers to extract such closed-form solutions. We illustrate advantages over commercial solvers, symbolic methods, and approximate neural networks on a diverse set of problems, demonstrating both generality and accuracy.
PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation
Authors: Desen Zhou, Qian He
Date: 2020-01-07 00:00:00
Proceeding: IEEE Access 2020 1
Tasks: Human Instance Segmentation, Instance Segmentation, Pose Estimation, Scene Understanding, Segmentation, Semantic Segmentation

Human instance segmentation is a core problem for human-centric scene understanding and segmenting human instances poses a unique challenge to vision systems due to large intra-class variations in both appearance and shape, and complicated occlusion patterns. In this paper, we propose a new pose-aware human instance segmentation method. Compared to the previous pose-aware methods which first predict bottom-up poses and then estimate instance segmentation on top of predicted poses, our method integrates both top-down and bottom-up cues for an instance: it adopts detection results as human proposals and jointly estimates human pose and instance segmentation for each proposal. We develop a modular recurrent deep network that utilizes pose estimation to refine instance segmentation in an iterative manner. Our refinement modules exploit pose cues in two levels: as a coarse shape prior and local part attention. We evaluate our approach on two public multi-person benchmarks: OCHuman dataset and COCOPersons dataset. The proposed method surpasses the state-of-the-art methods on OCHuman dataset by 3.0 mAP and on COCOPersons by 6.4 mAP, demonstrating the effectiveness of our approach.
Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods
Authors: Oussama Zekri, Nicolas Boullé
Date: 2025-02-03 00:00:00
Tasks: Language Modeling, Language Modelling, Policy Gradient Methods

Discrete diffusion models have recently gained significant attention due to their ability to process complex discrete structures for language modeling. However, fine-tuning these models with policy gradient methods, as is commonly done in Reinforcement Learning from Human Feedback (RLHF), remains a challenging task. We propose an efficient, broadly applicable, and theoretically justified policy gradient algorithm, called Score Entropy Policy Optimization (SEPO), for fine-tuning discrete diffusion models over non-differentiable rewards. Our numerical experiments across several discrete generative tasks demonstrate the scalability and efficiency of our method. Our code is available at https://github.com/ozekri/SEPO.
Fundamental limits of learning in sequence multi-index models and deep attention networks: High-dimensional asymptotics and sharp thresholds
Authors: Emanuele Troiani, Hugo Cui, Yatin Dandi, Florent Krzakala, Lenka Zdeborová
Date: 2025-02-02 00:00:00
Tasks: Deep Attention

In this manuscript, we study the learning of deep attention neural networks, defined as the composition of multiple self-attention layers, with tied and low-rank weights. We first establish a mapping of such models to sequence multi-index models, a generalization of the widely studied multi-index model to sequential covariates, for which we establish a number of general results. In the context of Bayesian-optimal learning, in the limit of large dimension $D$ and commensurably large number of samples $N$, we derive a sharp asymptotic characterization of the optimal performance as well as the performance of the best-known polynomial-time algorithm for this setting --namely approximate message-passing--, and characterize sharp thresholds on the minimal sample complexity required for better-than-random prediction performance. Our analysis uncovers, in particular, how the different layers are learned sequentially. Finally, we discuss how this sequential learning can also be observed in a realistic setup.
Position: More Rigorous Software Engineering Would Improve Reproducibility in Machine Learning Research
Authors: Moritz Wolter, Lokesh Veeramacheneni
Date: 2025-02-02 00:00:00
Tasks: Position

Experimental verification and falsification of scholarly work are part of the scientific method's core. To improve the Machine Learning (ML)-communities' ability to verify results from prior work, we argue for more robust software engineering. We estimate the adoption of common engineering best practices by examining repository links from all recently accepted International Conference on Machine Learning (ICML), International Conference on Learning Representations (ICLR) and Neural Information Processing Systems (NeurIPS) papers as well as ICML papers over time. Based on the results, we recommend how we, as a community, can improve reproducibility in ML-research.
Minimalistic Video Saliency Prediction via Efficient Decoder & Spatio Temporal Action Cues
Authors: Rohit Girmaji, Siddharth Jain, Bhav Beri, Sarthak Bansal, Vineet Gandhi
Date: 2025-02-01 00:00:00
Tasks: Action Classification, Action Localization, Decoder, Saliency Prediction, Spatio-Temporal Action Localization, Temporal Action Localization, Video Saliency Prediction

This paper introduces ViNet-S, a 36MB model based on the ViNet architecture with a U-Net design, featuring a lightweight decoder that significantly reduces model size and parameters without compromising performance. Additionally, ViNet-A (148MB) incorporates spatio-temporal action localization (STAL) features, differing from traditional video saliency models that use action classification backbones. Our studies show that an ensemble of ViNet-S and ViNet-A, by averaging predicted saliency maps, achieves state-of-the-art performance on three visual-only and six audio-visual saliency datasets, outperforming transformer-based models in both parameter efficiency and real-time performance, with ViNet-S reaching over 1000fps.
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning
Authors: Ankur Samanta, Rohan Gupta, Aditi Misra, Christian McIntosh Clarke, Jayakumar Rajadas
Date: 2025-02-03 00:00:00
Tasks: Graph-to-Sequence, Molecular Property Prediction, molecular representation, Property Prediction, Representation Learning, valid

Molecular property prediction uses molecular structure to infer chemical properties. Chemically interpretable representations that capture meaningful intramolecular interactions enhance the usability and effectiveness of these predictions. However, existing methods often rely on atom-based or rule-based fragment tokenization, which can be chemically suboptimal and lack scalability. We introduce FragmentNet, a graph-to-sequence foundation model with an adaptive, learned tokenizer that decomposes molecular graphs into chemically valid fragments while preserving structural connectivity. FragmentNet integrates VQVAE-GCN for hierarchical fragment embeddings, spatial positional encodings for graph serialization, global molecular descriptors, and a transformer. Pre-trained with Masked Fragment Modeling and fine-tuned on MoleculeNet tasks, FragmentNet outperforms models with similarly scaled architectures and datasets while rivaling larger state-of-the-art models requiring significantly more resources. This novel framework enables adaptive decomposition, serialization, and reconstruction of molecular graphs, facilitating fragment-based editing and visualization of property trends in learned embeddings - a powerful tool for molecular design and optimization.
Quasi-Unimodal Distributions for Ordinal Classification
Authors: Tomé Albuquerque, Ricardo Cruz, Jaime S. Cardoso
Date: 2022-03-18 00:00:00
Proceeding: Mathematics 2022 3
Tasks: Classification, Ordinal Classification

Ordinal classification tasks are present in a large number of different domains. However, common losses for deep neural networks, such as cross-entropy, do not properly weight the relative ordering between classes. For that reason, many losses have been proposed in the literature, which model the output probabilities as following a unimodal distribution. This manuscript reviews many of these losses on three different datasets and suggests a potential improvement that focuses the unimodal constraint on the neighborhood around the true class, allowing for a more flexible distribution, aptly called quasi-unimodal loss. For this purpose, two constraints are proposed: A first constraint concerns the relative order of the top-three probabilities, and a second constraint ensures that the remaining output probabilities are not higher than the top three. Therefore, gradient descent focuses on improving the decision boundary around the true class in detriment to the more distant classes. The proposed loss is found to be competitive in several cases.
Transformers trained on proteins can learn to attend to Euclidean distance
Authors: Isaac Ellmen, Constantin Schneider, Matthew I. J. Raybould, Charlotte M. Deane
Date: 2025-02-03 00:00:00

While conventional Transformers generally operate on sequence data, they can be used in conjunction with structure models, typically SE(3)-invariant or equivariant graph neural networks (GNNs), for 3D applications such as protein structure modelling. These hybrids typically involve either (1) preprocessing/tokenizing structural features as input for Transformers or (2) taking Transformer embeddings and processing them within a structural representation. However, there is evidence that Transformers can learn to process structural information on their own, such as the AlphaFold3 structural diffusion model. In this work we show that Transformers can function independently as structure models when passed linear embeddings of coordinates. We first provide a theoretical explanation for how Transformers can learn to filter attention as a 3D Gaussian with learned variance. We then validate this theory using both simulated 3D points and in the context of masked token prediction for proteins. Finally, we show that pre-training protein Transformer encoders with structure improves performance on a downstream task, yielding better performance than custom structural models. Together, this work provides a basis for using standard Transformers as hybrid structure-language models.
Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction
Authors: Daniel Herrera-Esposito, Johannes Burge
Date: 2025-01-31 00:00:00
Tasks: Dimensionality Reduction, Metric Learning, Supervised dimensionality reduction

Supervised dimensionality reduction aims to map labeled data to a low-dimensional feature space while maximizing class discriminability. Directly computing discriminability is often impractical, so an alternative approach is to learn features that maximize a distance or dissimilarity measure between classes. The Fisher-Rao distance is an important information geometry distance in statistical manifolds. It is induced by the Fisher information metric, a tool widely used for understanding neural representations. Despite its theoretical and pratical appeal, Fisher-Rao distances between classes have not been used as a maximization objective in supervised feature learning. Here, we present Supervised Quadratic Feature Analysis (SQFA), a linear dimensionality reduction method that maximizes Fisher-Rao distances between class distributions, by exploiting the information geometry of the symmetric positive definite manifold. SQFA maximizes distances using first- and second-order statistics, and its features allow for quadratic discriminability (i.e. QDA performance) matching or surpassing state-of-the-art methods on real-world datasets. We theoretically motivate Fisher-Rao distances as a proxy for quadratic discriminability, and compare its performance to other popular distances (e.g. Wasserstein distances). SQFA provides a flexible state-of-the-art method for dimensionality reduction. Its successful use of Fisher-Rao distances between classes motivates future research directions.