Public code of the ML course
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Updated
Jun 7, 2017 - Python
Public code of the ML course
Minimal Reproducibility Study of (https://arxiv.org/abs/1911.05248). Experiments with Compression of Deep Neural Networks
Project examing sparse deep learning architectures for ligand classification.
Official implementation of the paper "HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization"
[ICML 2022] "Data-Efficient Double-Win Lottery Tickets from Robust Pre-training" by Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang
3D Loss Landscapes of SoftNet (Sparse Subnetwork)
analysing Model Pruning and Unit Pruning on a large dense MNIST network
[ICCV2023 Official PyTorch code] for Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
Code for CPAL-2024 paper "Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates"
A pure implementation for sparse denoising autoencoder with adaptive evolutionary training using Scipy. The sparse implementation makes the algorithm scalable to high dimensional data and trainable on CPUs.
Code to reproduce the experiments of the ICLR24-paper: "Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging"
[NeurIPS 2022] "Sparse Winning Tickets are Data-Efficient Image Recognizers" by Mukund Varma T, Xuxi Chen, Zhenyu Zhang, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang
A simple C++14 and CUDA-based header-only library with tools for sparse-machine learning.
Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
Molecular-property prediction with sparsity
[AAMAS 2023] Code for the paper "Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning"
Fema: flexible evolutionary multi-faceted analysis for dynamic behavioral pattern discovery
Accompanying source code repository to the paper "Audio Dequantization Using (Co)Sparse (Non)Convex Methods".
A supervised autoencoder with structured sparsity for efficient and informed clinical prognosis.
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