contrastive-learning

This paper introduces REVEAL++, a differentiable phenotypic grouping method for vision-language contrastive learning, applied to retinal fundus images and clinical risk narratives for Alzheimer's disease risk prediction, outperforming discrete grouping baselines.

This paper introduces CADE, a framework for time-series question answering that maps each timestep directly into the LLM embedding space and uses a one-directional supervised contrastive loss to align time-series representations with frozen text anchors, outperforming existing baselines on the Time-MQA benchmark.

This paper proposes ImpSH, a triplet-based framework for implicit hate speech classification that aligns posts with implied statements and uses context-bounded semi-hard negative mining to improve cross-dataset generalization.

Proposes TMR-GGNN, a time-aware multi-relational graph neural network for credit card fraud detection that handles imbalanced data and evolving fraud patterns via contrastive learning and focal loss.

This paper proposes a post-training refinement approach using interventional contrastive learning to disentangle speech foundation model representations into separate content and speaker subspaces. The method shows improved out-of-domain speaker verification performance and evidence of successful separation.

MoCo-AIS is a unified contrastive learning framework for computing similarity of vessel trajectories, evaluated on large-scale AIS datasets.

Selective Synergistic Learning (SSync) improves video object-centric learning by selectively distilling reliable cues via pseudo-labeling and transitive merging, avoiding error propagation from indiscriminate dense alignment.

SkillCAT is a training-free framework for LLM agent skill self-evolution that addresses limitations of single-trace bias, unverified merging, and full corpus loading via three stages: Contrastive Causal Extraction, Assessment-Augmented Evolution, and Topology-Aware Task Execution, achieving up to 40.40% improvement on benchmarks.

This paper proposes a probabilistic contrastive pretraining framework for molecular graph transformers to improve multi-task ADME property prediction in drug discovery, achieving significant gains on three benchmarks.

This paper introduces GLACIER, a multimodal student-teacher foundation model that integrates molecular graphs, SMILES strings, and physicochemical descriptors to predict molecular properties efficiently. It leverages Finsler geometry-aware fusion and knowledge distillation from larger teacher models (MiniMol, MolFormer) to achieve high performance with a lightweight architecture.

OSMGraphCLIP is a model that learns global location embeddings from OpenStreetMap data using a graph-based encoder and contrastive alignment with a spherical-harmonics location encoder. It achieves strong performance across diverse geospatial tasks, often matching or exceeding satellite-based methods.

Proposes a POI-aware contrastive training framework using LLM-generated near-misses to improve ASR robustness at code-switching regions, achieving consistent error reductions on two benchmarks.

Proposes MSAIC-Net, a multi-scale attention-enhanced convolutional network for detecting myocardial substrate abnormalities from ECG signals, using imbalance-aware contrastive learning and lead-wise permutation importance for interpretability.

This paper develops a measure-theoretic framework analyzing when contrastive learning recovers meaningful latent geometry, introducing a 'diversity condition' on positive-pair sampling and a support-corrected InfoNCE variant, with experiments validating that sampling diversity and architectural inductive bias interact critically in contrastive representation learning.

This paper introduces KODA (Kernel Optimization for Discrepancy Analysis), a kernel-based framework for comparing and aligning vision-language model representations by identifying sample subsets that are clustered differently across models like CLIP, SigLIP, and BLIP. The method uses contrastive embedding clustering and randomized low-dimensional approximations to scale to large datasets while providing interpretable structural differences between representations.

TPA-AD is a two-stage pseudo anomaly-guided method for bearing time-series anomaly detection that generates pseudo-anomalous windows near normal boundaries using reconstruction models and contrastive learning, then scores anomalies with KNN—without requiring real anomaly samples during training. It is evaluated on bearing fault and degradation datasets, including high-speed train axle-box bearing data.

This paper introduces CoughSense, a system that classifies cough recordings into five respiratory disease categories using a fine-tuned Whisper encoder with active-frame pooling, achieving 82.3% balanced accuracy and deployed as a real-time mobile app.

This paper proposes CL-DMDF, a dynamic multimodal data fusion model that uses contrastive learning and a dual-dimensional attention mechanism to handle missing modalities and improve discriminative learning.

This paper introduces StenCE, a pretraining framework that uses cross-modal contrastive learning between ECG and X-ray angiography representations to detect severe coronary stenosis from ECGs, achieving high performance and enabling early diagnosis even in asymptomatic patients.

BRepCLIP introduces contrastive multimodal pretraining on boundary representation (BRep) primitives for CAD understanding, aligning BRep geometry with language and image embeddings to achieve state-of-the-art retrieval and zero-shot classification.