HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization

14 August 2023

Papers citing "HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization"

8 / 8 papers shown

Title
UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model T. Kaiser Thomas Norrenbrock Bodo Rosenhahn 48 0 0 08 May 2025
QPM: Discrete Optimization for Globally Interpretable Image Classification Thomas Norrenbrock T. Kaiser Sovan Biswas R. Manuvinakurike Bodo Rosenhahn 55 0 0 27 Feb 2025
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Chris Kolb T. Weber Bernd Bischl David Rügamer 111 0 0 04 Feb 2025
Q-SENN: Quantized Self-Explaining Neural Networks Thomas Norrenbrock Marco Rudolph Bodo Rosenhahn FAtt AAML MILM 25 6 0 21 Dec 2023
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training Tianlong Chen Zhenyu (Allen) Zhang Pengju Wang Santosh Balachandra Haoyu Ma Zehao Wang Zhangyang Wang OOD AAML 77 46 0 20 Feb 2022
RelTR: Relation Transformer for Scene Graph Generation Yuren Cong M. Yang Bodo Rosenhahn ViT 94 133 0 27 Jan 2022
Powerpropagation: A sparsity inducing weight reparameterisation Jonathan Richard Schwarz Siddhant M. Jayakumar Razvan Pascanu P. Latham Yee Whye Teh 90 54 0 01 Oct 2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 141 684 0 31 Jan 2021