{"total":18,"rows":[{"id":"2405.14377","title":"CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization","created":"2024-05-23","cited_by_count":15,"area":null},{"id":"2308.09858","title":"Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks","created":"2023-08-18","cited_by_count":12,"area":null},{"id":"2402.11417","title":"LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models","created":"2024-02-18","cited_by_count":12,"area":null},{"id":"2401.10341","title":"ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks","created":"2024-01-18","cited_by_count":11,"area":null},{"id":"2211.16749","title":"HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression","created":"2022-11-30","cited_by_count":9,"area":null},{"id":"2207.01751","title":"TT-PINN: A Tensor-Compressed Neural PDE Solver for Edge Computing","created":"2022-07-04","cited_by_count":7,"area":null},{"id":"2105.06250","title":"3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration","created":"2021-05-11","cited_by_count":3,"area":null},{"id":"2405.03089","title":"Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition","created":"2024-05-06","cited_by_count":3,"area":null},{"id":"2501.06300","title":"Tensorization of neural networks for improved privacy and interpretability","created":"2025-01-10","cited_by_count":3,"area":null},{"id":"2501.06663","title":"Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization","created":"2025-01-11","cited_by_count":3,"area":null},{"id":"2111.01697","title":"Low-Rank+Sparse Tensor Compression for Neural Networks","created":"2021-11-02","cited_by_count":2,"area":null},{"id":"2305.05318","title":"How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?","created":"2023-05-09","cited_by_count":2,"area":null},{"id":"2306.01076","title":"Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding","created":"2023-06-01","cited_by_count":2,"area":null},{"id":"2502.12384","title":"Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2208.10498","title":"Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey","created":"2022-08-22","cited_by_count":1,"area":null},{"id":"2608.30505","title":"Tensor Methods for Language Models: From Token Representation to Training, Adaptation, Inference, Compression, and Interpretability","created":"2026-08-31","cited_by_count":1,"area":null},{"id":"2205.10651","title":"Tensor Shape Search for Optimum Data Compression","created":"2022-05-21","cited_by_count":0,"area":null},{"id":"2511.17971","title":"Comprehensive Design Space Exploration for Tensorized Neural Network Hardware Accelerators","created":"2025-11-22","cited_by_count":0,"area":null}],"next_cursor":null}