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arXiv:2611.07753v1 Announce Type: new Abstract: Neural collapse (NC) describes the elegant geometric structure that sample features and classifier vectors exhibit when a classification model's loss r...
arXiv:2311. 02960v5 Announce Type: replace Abstract: Over the past decade, deep learning has proven to be a highly effective tool for learning meaningful features from raw data.
The paper introduces LIDFS, an unsupervised feature selection technique that uses Local Intrinsic Dimensionality (LID) to rank features based on their local discriminability. By treating each feature as a point and estimating its LID via maximum likelihood from distances to its k nearest neighboring features, LIDFS assigns higher importance to features with lower LID and refines the ranking to remove locally redundant features. Experiments on 23 high‑dimensional datasets show that LIDFS outperforms both classic and state‑of‑the‑art methods in supervised and clustering evaluations while running faster than its closest competitor.
The paper presents a tight generalization bound for deterministic neural networks that relies on the quality of their last hidden layer representations rather than traditional parameter-space metrics. By using a parameter‑free k‑nearest‑neighbor classifier to assess representation quality, the bound decomposes into three measurable components—local class geometry, decoder‑network agreement, and training stability—providing both a non‑vacuous certificate and a diagnostic tool for understanding generalization. Experiments show the bound achieves an 8.0‑percentage‑point gap to test error on CIFAR‑10 with ResNet‑18, yields the first non‑vacuous certificates on CIFAR‑100, and certifies 1.4 % on CIFAR‑10 and 23.1 % on 1,000‑class ImageNet using frozen DINOv2 representations.
arXiv:2605.11608v2 Announce Type: replace-cross Abstract: A single base LLM now comes with dozens of post-training variants, quantized, LoRA-adapted, or distilled, and each has to be checked before r...
arXiv:2610.27443v1 Announce Type: new Abstract: Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets p...