"neural network compression" Papers

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Binary Quadratic Quantization: Beyond First-Order Quantization for Real-Valued Matrix Compression

Kyo Kuroki, Yasuyuki Okoshi, Thiem Van Chu et al.

NEURIPS 2025arXiv:2510.18650

COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation

Uliana Parkina, Maxim Rakhuba

NEURIPS 2025arXiv:2507.07580

Mask in the Mirror: Implicit Sparsification

Tom Jacobs, Rebekka Burkholz

ICLR 2025arXiv:2408.09966

SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism

Reda Marzouk, Shahaf Bassan, Guy Katz

NEURIPS 2025arXiv:2510.21599

DSD$^2$: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?

Victor Quetu, Enzo Tartaglione

AAAI 2024paperarXiv:2303.01213

Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD

Yijun Wan, Melih Barsbey, Abdellatif Zaidi et al.

ICML 2024arXiv:2306.08125

Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning

Micah Goldblum, Marc Finzi, Keefer Rowan et al.

ICML 2024spotlight