Patient-aware contrastive objective preserves per-patient SR structure in RR-interval embeddings, reaching 0.989 AUROC on patient-independent PAF detection with lower variance than SupCon or BCE baselines.
Supervised contrastive learning
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Smaller self-supervised ViTs localize objects better via attention than larger ViTs, enabling A² to decouple localization from feature extraction for competitive performance on distribution-shifted benchmarks.
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
EAVAE disentangles style from content via contrastive pretraining and an explainable discriminator in a VAE setup, claiming SOTA authorship attribution on multiple datasets and strong few-shot AI text detection.
V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.
Contrastive pre-training on unsupervised data at scale creates text and code embeddings that set new state-of-the-art results on classification and semantic search benchmarks.
CrossView Suite supplies a 1.6M-sample dataset, scene-disjoint benchmark, and explicit-alignment framework to advance MLLMs from single-view perception to cross-view spatial intelligence.
JAM aligns frozen vision and language models via joint autoencoders and multimodal Spread Loss, reliably inducing cross-modal alignment across layer depths, objectives, and model scales.
Backdoor attacks succeed against ML fault detection and localization in CPS even when only 10% of the training data is poisoned.
LLMSniffer improves detection of LLM-generated code on GPTSniffer and Whodunit benchmarks by fine-tuning GraphCodeBERT via two-stage supervised contrastive learning plus preprocessing and MLP classification.
citing papers explorer
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Revisiting Feature Prediction for Learning Visual Representations from Video
V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.