Neural Network Compression

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13 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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Context-Aware Markov VAE for CSI Compression in Wireless Systems
Jun 15, 2026Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan +2Variational Autoencoders Neural Network Compression
Ericsson Research, Stockholm, Sweden · Department of Computer and Information Science, Linköping University, Linköping, Sweden · Department of Electrical Engineering, Linköping University, Linköping, Sweden
Mixtures of Subspaces for Bandwidth Efficient Context Parallel Training
Jun 15, 2026Sameera Ramasinghe, Ajanthan Thalaiyasingam, Hadi Mohaghegh Dolatabadi +5Communication-Efficient Distributed Training Long-Context Language Modeling
Pluralis Research
Embedded Arena: Iterative Optimization via Hardware Feedback
Jun 15, 2026Zhihan Zhang, Alexander Le Metzger, Jiuyang Lyu +10Large Language Model-Guided Optimization Tiny ML
University of Washington · University of California San Diego · Northeastern University
AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformers
Jun 14, 2026Rachmad Vidya Wicaksana Putra, Saad Iftikhar, Muhammad ShafiqueSpiking Transformers Neural Network Quantization
eBRAIN Lab, Division of Engineering, New York University (NYU) Abu Dhabi, United Arab Emirates (UAE)
Improved Knowledge Distillation for Land-Use Image Classification
Jun 12, 2026Arundhuti Sur, Abhiroop Chatterjee, Susmita Ghosh +1Cross-Architecture Knowledge Distillation Knowledge Distillation
Jadavpur University, India · Rochester Institute of Technology, USA
VQ4SNN: Vector Quantization for Memory-Efficient FPGA Spiking Neural Networks
Jun 12, 2026Dimitrios Sekertzis, Giorgos DimitrakopoulosMemory-Efficient Inference Vector Quantization
Integrated Circuits Lab, Electrical and Computer Engineering, Democritus University of Thrace (DUTH), Greece
Squeeze-Release: Iterative Pruning with Exact Structural Minimization
Jun 12, 2026Roman Denkin, Ida Akerholm, Prashant Singh +1Structured Pruning Neural Network Compression
Department of Information Technology, Uppsala University, Uppsala, Sweden · Science for Life Laboratory, Uppsala University, Uppsala, Sweden
Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices
Jun 10, 2026Farough Shayeste Roodi, Parham Zilouchian Moghaddam, Mahdi Mohammadi-nasab +3Electroencephalography EEG Seizure Detection
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran · Intelligent Future Technologies (IFT), Mälardalen University, Väster˚as, Sweden · Department of Computer Systems, Tallinn University of Technology (TalTech), Tallinn, Estonia
Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering
Jun 10, 2026Haoning Xu, Zhaoqing Li, Huimeng Wang +4Speech Foundation Models Clustering
The Chinese University of Hong Kong, Hong Kong SAR, China · National Research Council Canada, Canada
How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap
Jun 7, 2026Jasmeet Singh Bindra, Siddharth PanwarElectroencephalography Neural Network Compression
Indian Knowledge Systems and Mental Health Applications (IKSMHA) Center, Indian Institute of Technology Mandi, Himachal Pradesh, India · School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Himachal Pradesh, India
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
Jun 5, 2026Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi +1Mixed-Precision Quantization LLM Quantization
UiT The Arctic University of Norway · University of Oslo, Norway
Learned Subspace Compression for Communication-Efficient Pipeline Parallelism
Jun 3, 2026Paul Janson, Edouard Oyallon, Eugene BelilovskyLLM Compression Communication-Efficient Distributed Training
Concordia University · Mila Quebec AI Institute · CNRS, Sorbonne University
Data Efficient Complex Feature Fusion Network For Hyperspectral Image Classification
Jun 3, 2026Maitreya Shelare, Atharva Satam, Poonam Sonar +1Efficient Neural Network Inference Complex-Valued Neural Networks
Department of Electronics and Telecommunication, Rajiv Gandhi Institute of Technology, University of Mumbai, India
HalfNet: Randomized Neural Networks with Learned Subspace Geometry
Jun 3, 2026Ethem AlpaydinNeural Representation Geometry Random Feature Methods
Independent Researcher Istanbul, Turkey
PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers
Jun 2, 2026Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio +1Memory-Efficient Inference Spiking Neural Networks
eBRAIN Lab, Division of Engineering, New York University (NYU) Abu Dhabi, United Arab Emirates (UAE)
Neural Network Compression by Approximate Differential Equivalence
May 31, 2026Ravi Dhiman, Andrea Passarella, Mirco Tribastone +1Neural Network Compression
IMT School for Advanced Studies Lucca 55100, Italy · IIT CNR, Pisa 56124, Italy
STARFISH: faST Accuracy Recovery in pruned networks From Internal State Healing
May 31, 2026Shir Maon, Odelia Melamed, Adi ShamirNeural Network Compression Neural Network Pruning
Weizmann Institute of Science
Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits
May 29, 2026Salem Ameen, Sunil VaderaMulti-Armed Bandits Neural Network Compression
School of Science, Engineering and Environment, University of Salford, Salford, United Kingdom
Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Parameter Compression of Deep Neural Networks
May 28, 2026Andrzej Cichocki, Michal WietczakTensor Networks Neural Network Compression
Systems Research Institute of Polish Academy of Science and Warsaw University of Technology, Poland
Semantic Optimal Transport for Sparse Autoencoder Feature Matching and Circuit Compression
May 27, 2026Tue M. Cao, Nguyen Do, My T. ThaiSparse Autoencoders Mechanistic Interpretability
Department of Computer and Information Science and Engineering, University of Florida
Recursive Vision Transformer with Dynamic Depth and Width Adjustment for Resource-Efficient Image Semantic Communication
May 27, 2026Zhilong Zhang, Xinhui Zhang, Gongyu Jin +3Dynamic Neural Networks Semantic Communication
Beijing Laboratory of Advanced Information Network, and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
MTL-FNO: A Lightweight Multi-Task Fourier Neural Operator for Sparse Field Reconstruction
May 26, 2026Siyu Ye, Shihang Li, Zhiqiang Gong +4Fourier Neural Operator Multi-Task Learning
Defense Innovation Institute, Academy of Military Science, Beijing, 100071, China · Intelligent Game and Decision Laboratory, Beijing, 100071, China
Relative Repairability: A Calibration-Based Diagnostic for High-Sparsity Post-Pruning Allocation
May 25, 2026Qishi Zhan, Liang He, Minxuan Hu +1Neural Network Compression Neural Network Pruning
Marquette University · Tongji University · Cornell University +1
A general tensor-structured compression scheme for efficient large language models
May 25, 2026Ying Lu, Peng-Fei Zhou, Qi-Xuan Fang +3LLM Inference Efficiency LLM Compression
School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China. · Kavli Institute for Theoretical Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China. · Center for Quantum Physics and Intelligent Sciences, Department of Physics, Capital Normal University, Beijing, 100048, China. +1
Motion-Compensated Weight Compression
May 23, 2026Ismail LamaakalPredictive Coding Weight-Only Quantization
Multidisciplinary Faculty of Nador · Mohammed Premier University · Oujda 60000, Morocco
Slimmable ConvNeXt: Width-Adaptive Inference for Efficient Multi-Device Deployment
May 21, 2026Janek Haberer, Jon Eike Wilhelm, Olaf LandsiedelEfficient Neural Network Inference Adaptive Inference
Kiel University, Germany · Hamburg University of Technology (TUHH), Germany · UNU-INWEH, Germany
Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation
May 21, 2026Fabian Morelli, Stephan EcksteinInverse Optimal Transport Ensemble Learning
Department of Computer Science, University of Tübingen, Germany · Department of Mathematics, University of Tübingen, Germany.
Adaptive Signal Resuscitation: Channel-wise Post-Pruning Repair for Sparse Vision Networks
May 20, 2026Qishi Zhan, Ziheng Chen, Minxuan HuNeural Network Compression Neural Network Pruning
Department of Mathematical and Statistical Sciences, Marquette University, USA · The University of Texas at Austin, USA · Cornell Ann S. Bowers College of Computing and Information Science, Cornell University, USA
Fast Tensorization of Neural Networks via Slice-wise Feature Distillation
May 19, 2026Safa Hamreras, Sukhbinder Singh, Román OrúsTensor Networks Tensor Decomposition
Donostia International Physics Center, Paseo Manuel de Lardizabal 4, E-20018 San Sebastián, Spain · Multiverse Computing, Spadina Ave., Toronto, ON M5T 2C2, Canada · Multiverse Computing, Paseo de Miramón 170, E-20014 San Sebastián, Spain +1
Replacement Learning: Training Neural Networks with Fewer Parameters
May 19, 2026Yuming Zhang, Peizhe Wang, Tianyang Han +5Deep Learning Optimization Neural Network Compression
The University of Hong Kong, Hong Kong, China · Southeast University, Nanjing, China

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Context-Aware Markov VAE for CSI Compression in Wireless Systems

Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan, Zheng Chen+1

Ericsson Research, Stockholm, Sweden · Department of Computer and Information Science, Linköping University, Linköping, Sweden · Department of Electrical Engineering, Linköping University, Linköping, Sweden

Variational Autoencoders Neural Network Compression

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This paper considers neural channel state information (CSI) compression for time-varying massive multiple-input multiple-output (MIMO) channels in frequency division duplex (FDD) systems with limited feedback resources. The main challenge lies in obtaining a compact and efficient representation of the CSI given that it exhibits strong temporal correlation across successive snapshots. Existing memoryless compression models do not exploit this property, while simple temporal extensions often incorporate multiple observations without explicitly modeling the latent dynamics. We propose a context-aware compression framework based on a k-memory Markov variational autoencoder (k-MMVAE), which uses a finite temporal window to capture the evolution of CSI in the latent space. The model introduces Markov-structured latent dynamics with finite memory, enabling efficient use of temporal dependencies for compression. Simulation results show that the proposed approach improves target CSI reconstruction performance compared to memoryless and weakly sequential baselines, particularly at low and moderate compression rates. These results suggest that explicit latent temporal modeling can provide an effective mechanism for CSI compression under limited feedback constraints.

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