arXiv Machine Learning
Jul 14
Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data
arXiv:2607. 11883v1 Announce Type: new Abstract: Compression is fundamental to intelligence.
On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisation
The paper investigates the limits of the maximal coding rate reduction (MCR²) framework for out‑of‑distribution (OOD) generalisation. It shows that MCR² can lead to complete prediction failure under distribution shift, even when a perfectly stable feature is available, and that adding invariance principles from IRM or REx does not resolve this issue. The authors conclude that additional assumptions or learning principles are needed to guarantee stable OOD predictions with MCR².
Related stories
Hugging Face Trending Papers
Jul 13
Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data
Compression is fundamental to intelligence. A model that can represent its training data as a short code has discovered regularities that enable generalization.
arXiv Machine Learning
Jun 10
Post-Training Augmentation Invariance
arXiv:2505. 11702v3 Announce Type: replace Abstract: This work develops a framework for post-training augmentation invariance, in which our goal is to add invariance properties to a pretrained network without altering its behavior on the original, non-augmented input distribution.
arXiv Computer Vision
Sep 23
StableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training
StableVQ introduces practical guidelines to improve training stability for vector‑quantized tokenizers used in image generation models. It addresses instability caused by the entanglement of encoder–decoder and codebook training by proposing three techniques: Dynamic STE for the encoder, Region VQ Loss for the codebook, and a Decoupled Schedule for independent learning rates. Experiments on ImageNet show consistent gains in stability, codebook utilization, and reconstruction quality across various settings.
Hugging Face Trending Papers
Sep 23
Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces
Methods operating on Vision Transformer (ViT) feature spaces typically rely on Euclidean distance or cosine similarity. This assumes that every direction is equally meaningful, but there is no reason...
arXiv Machine Learning
Jul 8
Boosting with List-Decodable Codes
arXiv:2607. 05791v1 Announce Type: cross Abstract: Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (Schapire 1989).
arXiv Machine Learning
Jun 4
Rethinking Incompleteness: Formalizing Protocol Divergence and Train-Once Learning for Robust IMVC
arXiv:2606. 04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations.
arXiv Machine Learning
Sep 24
Task-Induced Riemannian Metrics for Vision Transformer Feature Spaces
arXiv:2609.27988v1 Announce Type: cross Abstract: Methods operating on Vision Transformer (ViT) feature spaces typically rely on Euclidean distance or cosine similarity. This assumes that every direc...
arXiv Machine Learning
Aug 11
An Information-Theoretic Framework for Feature Construction in Out-of-Distribution Detection
arXiv:2506. 14194v2 Announce Type: replace Abstract: We present a theory for the construction of out-of-distribution (OOD) detection features for neural networks.
arXiv Machine Learning
Jun 16
When Confidence Lacks Concepts: Interpretable OOD Detection via Representation Perturbations
arXiv:2606. 16196v1 Announce Type: new Abstract: Deep neural networks have achieved remarkable performance across medical imaging tasks, yet their tendency to overgeneralize under distributional shifts poses a major obstacle to safe clinical deployment.
arXiv AI
Jun 16
ALCL: An Adaptive Log-Correntropy Loss for Robust Learning under Non-Gaussian Noise
arXiv:2606. 16050v1 Announce Type: cross Abstract: Robust deep learning under heavy-tailed and impulsive noise remains challenging because conventional losses such as mean squared error (MSE) exhibit unbounded sensitivity to outliers.
arXiv Machine Learning
Jun 5
Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning
arXiv:2605. 27991v2 Announce Type: replace-cross Abstract: Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early-stopping rules.