Low-rank compression

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Most implemented papers archive 2025-07-28

14 shown of 14 papers with code (34 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
  23 Jul 2021 2 repositories listed Syntology ran 0 of 12 samples · 12 unverified
  We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultaneously achieving the desired overall…
• Domain-adaptive deep network compression
  4 Sep 2017 2 repositories listed
  We show that domain transfer leads to large shifts in network activations and that it is desirable to take this into account when compressing.
• ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration
  30 May 2025 1 repository listed
  Recent methods have explored reducing the hidden dimensions of the KV cache, but many introduce additional computation through projection layers or suffer from significant performance degradation under high compression…
• LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation
  27 Feb 2025 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  Modern automatic speech recognition (ASR) models, such as OpenAI's Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient deployment due to high computational…
• CALoR: Towards Comprehensive Model Inversion Defense
  8 Oct 2024 1 repository listed
  Model Inversion Attacks (MIAs) aim at recovering privacy-sensitive training data from the knowledge encoded in the released machine learning models.
• Unified Low-rank Compression Framework for Click-through Rate Prediction
  28 May 2024 1 repository listed
  To address these challenges, we propose a unified low-rank decomposition framework for compressing CTR prediction models.
• Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
  15 Jan 2024 1 repository listed
  This paper presents a novel gradient compression method for federated learning (FL) in wireless systems.
• Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
  28 Aug 2023 1 repository listed Syntology ran 15 of 18 samples · 3 unverified
  Deep Neural Networks (DNNs) have been a large driver for AI breakthroughs in recent years.
• TT-NF: Tensor Train Neural Fields
  30 Sep 2022 1 repository listed
  Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations.
• Compact Model Training by Low-Rank Projection with Energy Transfer
  12 Apr 2022 1 repository listed
  We theoretically show that energy transfer eases the trend of gradient vanishing caused by projection.
• Model compression as constrained optimization, with application to neural nets. Part V: combining compressions
  9 Jul 2021 1 repository listed
  However, VGG nets can be better compressed by combining low-rank with a few floating point weights.
• Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer
  1 Jun 2020 1 repository listed
  Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix.
• A flexible, extensible software framework for model compression based on the LC algorithm
  15 May 2020 1 repository listed
  We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using different compression schemes with…
• Decomposable-Net: Scalable Low-Rank Compression for Neural Networks
  29 Oct 2019 1 repository listed
  Compressing DNNs is important for the real-world applications operating on resource-constrained devices.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.