Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch selection methods typically rely on a single metric to assess instance importance and use static label weights to distinguish label significance, neglecting the dynamic evolution of metric utility and label significance during training. In addition, the method that explicitly exploits label correlations is largely affected by abundant irrelevant labels and insensitive to local label distributions. To address these issues, we propose D2ACE, a novel multi-label batch selection method guided by Dual Dynamics and Adaptive Correlation Enhancement. D2ACE explicitly captures metric and label-level training dynamics by combining stage-wise Bernoulli mixture sampling, which balances uncertainty and noise-resistant hardness, with dynamic label weighting to recalibrate label priorities at each epoch based on current metric statistics. Furthermore, D2ACE introduces a local context-aware correlation enhancement to focus on relevant labels with instance-adaptive dependencies. Extensive experiments on tabular and image benchmarks demonstrate that D2ACE outperforms existing batch selection approaches across various deep MLC models, achieving stronger predictive performance and more efficient correlation modeling.
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Dynamic Graph Neural Networks (DyGNNs) are facing challenges with distribution shifts between training and test data that are similar but not identical. Existing DyGNNs for out-of-distribution scenarios primarily focus on discovering invariant patterns in the spatial domain, overlooking its impact on the structural properties in the spectral domain. In this paper, we propose a spectral-based graph augmentation framework designed to investigate and improve generalization behavior in dynamic graphs under distribution shifts. Specifically, we augment the input graph spectra into a mixture of shift components by maximizing the variance of spectral distance and propose an efficient approximation to reduce the computational cost brought by eigen-decomposition. Building on this, we develop a multi-encoder architecture in which each encoder targets a specific spectral shift component to generate referential representations. Our method adopts a new learning objective to encourage the model to rely on robust spectral properties for better generalization under distribution shifts. Extensive experiments on both real-world and synthetic datasets demonstrate that our proposed method significantly outperforms state-of-the-art approaches in node classification and link prediction tasks. Source codes are available at https://github.com/SSQiana/DSPA.
Gradient boosting remains a strong and widely used method for tabular data learning, but its performance often degrades when training labels are noisy. This behavior is largely related to the way boosting algorithms emphasize samples with large gradients, without explicitly accounting for whether such errors originate from informative hard cases or from unreliable labels. We address this issue by reconsidering how sample reliability is evaluated during boosting. Instead of relying on instantaneous error, we examine the evolution of each sample’s residuals across iterations. Based on this insight, we propose Information-Theoretic Trust Boosting (ITBoost), which uses the Minimum Description Length principle to measure the complexity of residual trajectories. Samples whose residual patterns fluctuate in an irregular manner are treated as less trustworthy and are down-weighted during learning. Theoretically, we derive a tighter generalization bound for ITBoost under label noise. Empirical results on various tabular benchmarks indicate that ITBoost provides improved robustness in noisy environments over leading boosting and deep tabular models, while retaining best average performance on clean data.
Recent efforts have successfully scaled plain Vision Transformers (ViTs) to unprecedented sizes, ranging from 6B to 22B parameters. However, hierarchical ViTs, which are inherently better suited for multi-scale representation and dense prediction tasks, have largely remained constrained to under 2B parameters. This scaling gap prevents the community from leveraging high-capacity, multi-scale foundation models for complex visual scenes. To bridge this gap, we propose EHV, an Efficient Hierarchical ViT architecture designed for massive scaling. Our dense models scale from 200M to 5B parameters, and we further introduce a Sparse Mixture-of-Experts (SMoE) variant, pushing the scale to an industry-leading 30B parameters. The training pipeline follows a two-stage pretraining strategy: (1) MAE Stage: self-supervised pretraining on ImageNet-21K using a Masked Autoencoder (MAE); and (2) Distillation Stage: distilling knowledge from multiple state-of-the-art foundation models on a 27M-image dataset. With only 6.7B active parameters, EHV-5B-MoE achieves an exceptional 89.0% linear accuracy on ImageNet-1K, outperforming much larger models like EVA-CLIP-18B and DINOv3-7B. These results demonstrate that EHV effectively learns high-quality, generalizable, and linearly separable features across image classification, video analysis, and dense prediction tasks.
Few-shot learning aims to recognize novel categories from limited labeled samples, where prototypes estimated from 1--5 supports per class are often unreliable. Semantic-based approaches alleviate this by introducing class-level priors, but they often ignore instance-level cues and rarely optimize queries under the inductive protocol. We propose PMCE, a Probabilistic framework that leverages Multi-granularity semantics with Caption-guided Enhancement for few-shot classification. On base classes, we build a knowledge bank with class-wise visual statistics and class-name embeddings. At test time, the semantic embedding of a novel class retrieves a few similar base classes whose visual priors are aggregated into a class-specific prior and combined with the support-based prototype via MAP estimation. Simultaneously, we train a lightweight enhancer on base classes that fuses frozen BLIP captions with visual features, and apply it to both supports and queries without using query-set statistics or novel labels. A simple caption-consistency regularizer further improves robustness to noisy captions. Experiments on four standard benchmarks with ResNet-12 and Swin-T backbones show that PMCE outperforms state-of-the-art few-shot baselines, achieving up to 7.71% gains over the strongest competitor on MiniImageNet in the challenging 1-shot setting. Our code is available at https://github.com/channa419/PMCE.
Contrastive learning has become a dominant paradigm for learning time series representations from large-scale unlabeled data. However, current methods are often adapted from computer vision and rely on random time-domain augmentations (e.g., jittering and cropping). Such augmentations can unpredictably disrupt the natural frequency structure of signals, leading to representations failing to capture crucial patterns in the data. To address this, we propose a framework of Frequency-Aware Augmentation and Alignment for Time Series Contrastive Learning (FACL), which comprises two key innovations. First, FACL employs a novel frequency-structured augmentation mechanism based on wavelet transforms. The mechanism constructs controlled and interpretable contrastive views by the structured attenuation and recombination of specific wavelet components. Second, FACL introduces a multi-level contrastive objective that incorporates a subspace alignment strategy. This objective explicitly aligns representations within their corresponding frequency subspaces. Experiments across six forecasting and four classification benchmarks show that FACL achieves superior performance compared to recent baselines. Ablation studies and model analysis highlight the contribution of each component in FACL. Furthermore, low-sample semi-supervised learning experiments confirm the robustness and generalization of FACL.
We present Risk Atlas Nexus, an open source system for governing AI risks. The system unifies several risk classification frameworks through a common ontology. Given an AI application use case (called an intent), the system estimates risks and associated mitigations that are linked to identified risks. The tool is designed to be incorporated in AI governance workflows where recommendations can be translated to business controls to cover risks arising from AI use in firms.
We present IntelliProof, an interactive system for analyzing argumentative essays through LLMs. IntelliProof structures an essay as an argumentation graph, where claims are represented as nodes, supporting evidence is attached as node properties, and edges encode supporting or attacking relations. Unlike existing automated essay scoring systems, IntelliProof emphasizes the user experience: each relation is initially classified and scored by an LLM, then visualized for enhanced understanding. The system provides justifications for classifications and produces quantitative measures for essay coherence. It enables rapid exploration of argumentative quality while retaining human oversight. In addition, IntelliProof provides a set of tools for a better understanding of an argumentative essay and its corresponding graph in natural language, bridging the gap between the structural semantics of argumentative essays and the user's understanding of a given text.
We present ARGUS, an end-to-end Argument Mining (AM) tool that exploits Large Language Models (LLMs) to automatically perform all core AM tasks, i.e., Argument Component Segmentation, Classification, Relation Identification, and Relation Classification. Furthermore, ARGUS builds the corresponding argumentation framework (AF) and seamlessly integrates symbolic solvers to compute extensions and perform formal reasoning. ARGUS is designed to ensure broad flexibility and usability, supporting any open-source or commercial LLMs and symbolic solvers, providing a ready-to-use platform for exploring neuro-symbolic approaches to argumentation in both research and practical applications.
BiST-Mamba: A Dual-branch Spatio-Temporal Mamba Network for Encrypted Traffic Classification (Student Abstract)
PDF ↗Encrypted traffic classification has become increasingly important in network security. To address the difficulty of existing architectures in collaboratively modeling spatio-temporal features, we propose BiST-Mamba, a novel dual-branch spatio-temporal Mamba network that enables simultaneous representation of spatio-temporal features. To the best of our knowledge, this is the first work to introduce VMamba into encrypted traffic classification. Preliminary experiments on a small-scale dataset show that our accuracy and F1 scores reach 94.13% and 93.41%, respectively. The method achieves promising classification performance, demonstrating the potential of the model for effective spatio-temporal modeling.
Spatiotemporal Transformers with Multiple Instance Learning for Label-Efficient Behavioral Analysis in Autism (Student Abstract)
PDF ↗The identification of unique traits and behavior is essential to providing personalized intervention in individuals with Autism Spectrum Disorder. However, the limited personalized quantitative data with experts' annotations in autism research pose a fundamental challenge to train AI models for unique behavioral patten discovery. Multiple Instance Learning (MIL) has demonstrated promising results in medical domains, where annotations are only needed at the group level (i.e., a whole sequence) instead of individual data instances. It provides a cost-effective way to train statistical models with limited labeled data. Additionally, the rise of pretrained models have shown great success in improving the performance in few-shot learning scenarios. In this proof-of-concept study, we propose a novel framework that integrates a transformer encoder pre-trained on large-scale spatiotemporal data with MIL, for unique behavioral pattern detection from autistic individuals. Our results demonstrated the discrimination of individual-level autistic behavioral differences and the accurate classification of behaviors across distinct groups: typically developing (TD) and autistic (ASD). Beyond aggregate performance metrics, we highlight visual insights from temporal instance scores, revealing interpretable differences between individuals in their respective groups. These results show promising progress towards tools that can be used for personalized intervention for autistic individuals, and more interpretable AI diagnostics.
Radiotherapy (RT) is a cornerstone of cancer treatment. Following RT, patient-reported outcomes (PROs) collected via standardized questionnaires are crucial for monitoring patients' quality of life and side effects. However, traditional statistical and machine learning methods, which rely on structured numerical data, often fail to capture semantic meaning within patients' health status. To address this, we developed a novel framework using zero- and few-shot large language models (LLMs) to identify patients experiencing mild to severe depression. Furthermore, classification performance is enhanced through parameter-efficient fine-tuning. Experiments on a prostate cancer PRO dataset for depression have demonstrated that our fine-tuned LLMs consistently outperformed other baseline methods across key evaluation metrics.
iCD: An Implicit Clustering Distillation Method for Structural Information Mining (Student Abstract)
PDF ↗Logit Knowledge Distillation has gained substantial research interest in recent years due to its simplicity and lack of requirement for intermediate feature alignment; however, it suffers from limited interpretability in its decision-making process. To address this, we propose implicit Clustering Distillation (iCD): a simple and effective method that mines and transfers interpretable structural knowledge from logits, without requiring ground-truth labels or feature-space alignment. iCD leverages Gram matrices over decoupled local logit representations to enable student models to learn latent semantic structural patterns. Extensive experiments on benchmark datasets demonstrate the effectiveness of iCD across diverse teacher-student architectures, with particularly strong performance in fine-grained classification tasks---achieving a peak improvement of +5.08% over the baseline.
Adversarial training is an effective technique for enhancing the robustness of deep neural networks (DNNs). Prior research shows that misclassified examples influence final adversarial robustness much more than correctly classified examples. Ignoring this difference during training can hurt model performance. In crowdsourcing, varying annotator expertise causes noisy, inconsistent labels. As a result, it is hard to distinguish misclassified and correctly classified examples using only provided annotations. Thus, how to use the reliability and discrepancy between these example types to improve robustness within adversarial learning remains a critical but underexplored issue. In this work, we first explore how misclassified and correctly classified examples affect learning from crowds (LFC) in adversarial environments. Then, we formulate the problem of misclassification-aware robust learning from multiple human labelers as a bilevel min-max problem. After that, we introduce MALC, a new approach to make classifiers more robust to adversarial examples via iterative adversarial example generation and parameter estimation. We conduct an extensive evaluation of the proposed MALC, showing that MALC can outperform the state-of-the-art LFC methods in both white-box and black-box settings.
Out-of-distribution (OOD) detection and uncertainty estimation (UE) are critical components for building safe machine learning systems. In this work, we propose a novel framework that combines network inversion with classifier training to simultaneously address both OOD detection and uncertainty estimation. We extend a standard n-class classifier by adding an (n+1)-th "garbage" class to capture outliers, initially populated with random Gaussian noise. After each training epoch, we use network inversion to reconstruct inputs for all classes; incoherent reconstructions are assigned to the garbage class for retraining. This iterative cycle of training, inversion, and exclusion continues until inverted samples resemble in-distribution data and uncertainty drops, indicating learned decision boundaries and cleaner class manifolds. At inference, the model detects OOD inputs by classifying them as garbage, with confidence scores estimating uncertainty. Unlike prior methods, this approach requires no external OOD data or post-hoc calibration, providing a simple, unified solution for robust classification, OOD detection, and uncertainty estimation.
Existing federated prompt learning methods for vision-language models like CLIP rely solely on text-based prompts and final-layer visual features, missing crucial multiscale visual details and client-specific style variations. This limits generalization across non-IID distributions and novel classes. We introduce FedCSAP (Federated Cross-Modal Style-Aware Prompt Generation), which harnesses multiscale features from CLIP's vision encoder alongside domain-aware style statistics from client data. By fusing these visual representations with textual context, FedCSAP generates adaptive, context-aware prompts that enhance robustness across seen and unseen classes. Our privacy-preserving approach operates through local training and global aggregation, effectively handling heterogeneous client distributions. Experiments on multiple image classification datasets demonstrate that FedCSAP significantly outperforms existing federated prompt learning methods in both accuracy and generalization.
Transformer models have achieved remarkable success across diverse deep learning fields, including natural language processing (NLP) and computer vision (CV). One drawback of these models is that the computational cost of the softmax attention, the core component of the transformer, exhibits quadratic complexity in both time and memory. As data scales up various attempts have been reported to overcome this bottleneck. The objective of this study is to propose a novel attention mechanism, "Cumulant Attention", that systematically balances efficiency and accuracy. This proposal introduces a statistical-mechanics perspective and a reliable approximation based on cumulant expansion into the attention layer. The low-order variant reduces computational complexity to linear order, similar to the linear attention, while keeping nonlinearity of the softmax attention. We evaluate several variants on CV tasks, including image classification with ViT on ImageNet-100 and video classification with ViViT on UCF-101. Experimental results demonstrate that the cumulant attention outperforms the linear attention and achieves accuracy comparable to the softmax attention. These findings validate the effectiveness of our approach and highlight future directions, including scaling to larger models, extending to other modalities, and optimizing implementations for GPU hardware.
Generative AI-Driven Data Transformation for Enhanced Machine Learning Performance (Student Abstract)
PDF ↗Machine Learning (ML) models have significant potential across research and industry to enable data-driven insights and decision-making. Their performance relies on input data quality, but real-world datasets often contain imperfections, making data preprocessing essential yet time-consuming. Our research proposes a proof-of-concept model using Generative Artificial Intelligence (GenAI) to analyze and transform data for supervised ML classification. The results from the GenAI models will be compared with traditionally preprocessed data to evaluate effectiveness. Preliminary results indicate that incorporating GenAI models into the preprocessing pipeline show potential in improving ML's classification performance.
We introduce a single–backbone foundation model for brain MRI that supports dynamic modality integration: it operates with arbitrary, possibly unseen, combinations of MRI sequences at pretrain and transfer. The encoder is conditioned by text-derived modality embeddings via conditional layer normalization, while a variance–covariance penalty discourages feature collapse. Unlike expert-based designs that grow with each new sequence, our approach scales without adding modality-specific branches. Pretrained self-supervised on ∼60,000 heterogeneous MRIs, the model learns modality-aware yet modality-agnostic features. We outline evaluation on segmentation and classification under missing/unseen modalities and cross-center shifts, and present early feasibility on multiple sclerosis lesion segmentation under limited data. This work moves toward robust, protocol-agnostic MRI foundation models suited to real clinical variability.
Automated mosquito species identification is critical for combating vector-borne diseases. We introduce Q-MoFusion, a novel hybrid quantum-classical framework that fuses deep features from pre-trained Audio Spectrogram Transformer (AST) and Whisper models using a Variational Quantum Circuit (VQC). Our approach significantly outperforms individual backbones and prior state-of-the-art benchmarks, demonstrating superior accuracy and robustness, particularly on imbalanced classes. Q-MoFusion demonstrates the potential of hybrid quantum computing to enhance bioacoustic surveillance for addressing critical public health challenges.