Low-Rank Matrix Approximation for Neural Network Compression
Fuente:
arXiv
Saved in:
| Main Authors: | Cherukuri, Kalyan, Lala, Aarav |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
by: Lala, Aarav, et al.
Published: (2025)
by: Lala, Aarav, et al.
Published: (2025)
Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning
by: Cherukuri, Kalyan, et al.
Published: (2025)
by: Cherukuri, Kalyan, et al.
Published: (2025)
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
by: Cherukuri, Kalyan, et al.
Published: (2025)
by: Cherukuri, Kalyan, et al.
Published: (2025)
New Hardness Results for Low-Rank Matrix Completion
by: Chawin, Dror, et al.
Published: (2025)
by: Chawin, Dror, et al.
Published: (2025)
Lossless Model Compression via Joint Low-Rank Factorization Optimization
by: Zhang, Boyang, et al.
Published: (2024)
by: Zhang, Boyang, et al.
Published: (2024)
When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?
by: Li, Chenyang, et al.
Published: (2025)
by: Li, Chenyang, et al.
Published: (2025)
What is a Sketch-and-Precondition Derivation for Low-Rank Approximation? Inverse Power Error or Inverse Power Estimation?
by: Xu, Ruihan, et al.
Published: (2025)
by: Xu, Ruihan, et al.
Published: (2025)
Omnipredictors for Regression and the Approximate Rank of Convex Functions
by: Gopalan, Parikshit, et al.
Published: (2024)
by: Gopalan, Parikshit, et al.
Published: (2024)
Reachability In Simple Neural Networks
by: Sälzer, Marco, et al.
Published: (2022)
by: Sälzer, Marco, et al.
Published: (2022)
Proximity to Losslessly Compressible Parameters
by: Farrugia-Roberts, Matthew
Published: (2023)
by: Farrugia-Roberts, Matthew
Published: (2023)
Spiky Rank and Its Applications to Rigidity and Circuits
by: Hambardzumyan, Lianna, et al.
Published: (2026)
by: Hambardzumyan, Lianna, et al.
Published: (2026)
Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective
by: Cao, Yang, et al.
Published: (2025)
by: Cao, Yang, et al.
Published: (2025)
The Descriptive Complexity of Graph Neural Networks
by: Grohe, Martin
Published: (2023)
by: Grohe, Martin
Published: (2023)
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension
by: Chandrasekaran, Gautam, et al.
Published: (2024)
by: Chandrasekaran, Gautam, et al.
Published: (2024)
Sandwiching Polynomials for Geometric Concepts with Low Intrinsic Dimension
by: Klivans, Adam R., et al.
Published: (2026)
by: Klivans, Adam R., et al.
Published: (2026)
The Communication Complexity of Approximating Matrix Rank
by: Sherstov, Alexander A., et al.
Published: (2024)
by: Sherstov, Alexander A., et al.
Published: (2024)
Ranking Vectors Clustering: Theory and Applications
by: Fattahi, Ali, et al.
Published: (2025)
by: Fattahi, Ali, et al.
Published: (2025)
Complexity of Injectivity and Verification of ReLU Neural Networks
by: Froese, Vincent, et al.
Published: (2024)
by: Froese, Vincent, et al.
Published: (2024)
On the Hardness of Learning One Hidden Layer Neural Networks
by: Li, Shuchen, et al.
Published: (2024)
by: Li, Shuchen, et al.
Published: (2024)
Verifying Quantized Graph Neural Networks is PSPACE-complete
by: Sälzer, Marco, et al.
Published: (2025)
by: Sälzer, Marco, et al.
Published: (2025)
The Complexity of Verifying Feedforward Neural Networks in Quantised Settings
by: Alsmann, Eric, et al.
Published: (2026)
by: Alsmann, Eric, et al.
Published: (2026)
Computational Complexity Evaluation of Neural Network Applications in Signal Processing
by: Freire, Pedro, et al.
Published: (2022)
by: Freire, Pedro, et al.
Published: (2022)
Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
by: Bertschinger, Daniel, et al.
Published: (2022)
by: Bertschinger, Daniel, et al.
Published: (2022)
On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective
by: Li, Xiaoyu, et al.
Published: (2025)
by: Li, Xiaoyu, et al.
Published: (2025)
Low-degree learning and the metric entropy of polynomials
by: Eskenazis, Alexandros, et al.
Published: (2022)
by: Eskenazis, Alexandros, et al.
Published: (2022)
Mathematical Formalism for Memory Compression in Selective State Space Models
by: Bhat, Siddhanth
Published: (2024)
by: Bhat, Siddhanth
Published: (2024)
Unlocking the Theory Behind Scaling 1-Bit Neural Networks
by: Daliri, Majid, et al.
Published: (2024)
by: Daliri, Majid, et al.
Published: (2024)
The Expressive Power of Low Precision Softmax Transformers with (Summarized) Chain-of-Thought
by: Brösamle, Moritz, et al.
Published: (2026)
by: Brösamle, Moritz, et al.
Published: (2026)
Low Rank Matrix Rigidity: Tight Lower Bounds and Hardness Amplification
by: Alman, Josh, et al.
Published: (2025)
by: Alman, Josh, et al.
Published: (2025)
The Optimal Approximation Factor in Density Estimation
by: Bousquet, Olivier, et al.
Published: (2019)
by: Bousquet, Olivier, et al.
Published: (2019)
Exact and Approximate Algorithms for Polytree Learning
by: Harviainen, Juha, et al.
Published: (2026)
by: Harviainen, Juha, et al.
Published: (2026)
How Much Cache Does Reasoning Need? Depth-Cache Tradeoffs in KV-Compressed Transformers
by: Wang, Xiao
Published: (2026)
by: Wang, Xiao
Published: (2026)
Reducing the Complexity of Matrix Multiplication to $O(N^2log_2N)$ by an Asymptotically Optimal Quantum Algorithm
by: Yao, Jiaqi, et al.
Published: (2026)
by: Yao, Jiaqi, et al.
Published: (2026)
Reinforced Generation of Combinatorial Structures: Hardness of Approximation
by: Nagda, Ansh, et al.
Published: (2025)
by: Nagda, Ansh, et al.
Published: (2025)
On the Hardness of Approximation of the Fair k-Center Problem
by: Thejaswi, Suhas
Published: (2026)
by: Thejaswi, Suhas
Published: (2026)
The Reachability Problem for Neural-Network Control Systems
by: Schilling, Christian, et al.
Published: (2024)
by: Schilling, Christian, et al.
Published: (2024)
Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom
by: Grewal, Sabee, et al.
Published: (2022)
by: Grewal, Sabee, et al.
Published: (2022)
Parameterized Hardness of Zonotope Containment and Neural Network Verification
by: Froese, Vincent, et al.
Published: (2025)
by: Froese, Vincent, et al.
Published: (2025)
Provably Explaining Neural Additive Models
by: Bassan, Shahaf, et al.
Published: (2026)
by: Bassan, Shahaf, et al.
Published: (2026)
Similar Items
-
Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
by: Lala, Aarav, et al.
Published: (2025) -
Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning
by: Cherukuri, Kalyan, et al.
Published: (2025) -
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
by: Cherukuri, Kalyan, et al.
Published: (2025) -
New Hardness Results for Low-Rank Matrix Completion
by: Chawin, Dror, et al.
Published: (2025) -
Lossless Model Compression via Joint Low-Rank Factorization Optimization
by: Zhang, Boyang, et al.
Published: (2024)