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neural-network-compression

topic3 events
papersTODAY 04:00 UTC

Paper reviews tensorization for neural network compression and interpretability

A revised arXiv paper examines tensorization, a method that reshapes a network's dense weight matrices into higher-order tensors and approximates them with low-rank tensor network decompositions. The authors argue the approach remains underused despite promising results as a model compression technique, and they highlight its potential for making networks easier to interpret. The submission appears as a replacement cross-list across arXiv's AI and machine learning categories.

research/model-compression/interpretability/arxiv
papersTODAY 04:00 UTC

One-shot pruning found to act as implicit regularizer for speech recognition models

A study argues that one-shot magnitude pruning does more than compress neural networks, acting as an implicit regularizer for automatic speech recognition. Testing with Whisper-small, the authors combine gradient- and Fisher-based sensitivity measures to guide which weights to remove. The work reframes pruning as a training technique rather than only a efficiency tool.

pruning/speech-recognition/whisper/research