Contrastive learning

Timeline

1. Oct 8, 2026 · Research paper · 1 source
   AuraLuxMuse: Adaptive Fusion Modeling for Aesthetic Stage Lighting Design with Music and Expert Guidance
   We present AuraLuxMuse, a novel system for automated aesthetic stage lighting design that integrates expert knowledge, representation learning, and preference-adaptive modeling.
2. Oct 8, 2026 · Research paper · 1 source
   S$^3$Geo: Structure-Semantic Synergistic Learning for Cross-View Geo-Localization
   To address these challenges, we propose S$^3$Geo, a structure-semantic synergistic learning framework for cross-view matching.
3. Oct 8, 2026 · Research paper · 1 source
   Rethinking Contrastive Loss in CLIP Post-training: A Complementary Framework with Frozen Text Encoder
   Building on this finding, we propose ComCLIP, a lightweight single-epoch post-training recipe that freezes CLIP's text encoder---so the refined vision encoder is a drop-in replacement with unchanged architecture and inference cost---and trains the vision encoder with a properly-tempered contrastive loss, an MSE anchoring loss against the original CLIP, and a relational distillation loss from DINOv2.
4. Oct 8, 2026 · Research paper · 1 source
   MetaEncoder: Exploring the Limit of Bi-Encoders for Multimodal System One Decision Making with Natural Language Interface
   We introduce MetaEncoder, which fine-tunes a pre-trained Muse-Glimmer 30B decoder into an instruction-following decision-making encoder.
5. Oct 8, 2026 · Research paper · 1 source
   Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy
   Preclinical models poorly predict human drug efficacy, particularly in neurological disorders.
6. Oct 8, 2026 · Research paper · 1 source
   Social Pain Disrupts Emotion-Action Brain-State Dynamics in Adolescents with Non-Suicidal Self-Injury
   Non-suicidal self-injury (NSSI) is prevalent among adolescents with depression, but the rapid brain-state dynamics linking social distress to maladaptive behavior remain unclear.
7. Oct 7, 2026 · Research paper · 1 source
   Shared Gaussianization: What Gaussian Regularizers Certify About Contrastive Learning, and What They Miss
   What can a distribution-matching regularizer such as SIGReg in LeJEPA certify about contrastive learning?
8. Oct 7, 2026 · Research paper · 1 source
   MorphCL: Morphological Contrastive Learning for Inertial-based Human Activity Recognition
   Here we introduce Morphological Contrastive Learning (MorphCL), a self-supervised pretraining framework that uses structure-aware grouping to inject explicit modeling of global structure into inertial-based SSL approaches.
9. Oct 7, 2026 · Research paper · 1 source
   An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
   Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results.
10. Oct 7, 2026 · Research paper · 1 source
    Node-level Graph Neural Architecture Search Framework
    To overcome this limitation, in this work, we propose a Node-Level Graph Neural Architecture Search (N-GNAS) algorithm.
11. Oct 6, 2026 · Research paper · 1 source
    PVSync: A Unified Lip-Sync Expert for Timing and Articulation
    We introduce PVSync, a unified model for audio-visual offset estimation and phoneme-level articulation scoring.
12. Oct 6, 2026 · Research paper · 1 source
    Contrastive Learning for Aspect Representation towards Explainable Recommendation
    In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that integrates aspect features learned from textual reviews with rating information to improve the accuracy and explainability of recommendations.
13. Oct 6, 2026 · Research paper · 1 source
    MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning
    We introduce a new ECG foundation model --- MS-ECG-FM --- that is trained through contrastive alignment to multiple distinct clinical note types, including ECG, echocardiography, radiology, and discharge reports.

Often appears with