Compute-efficient reinforcement learning with binary neural networks and evolution strategies.
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Updated
Apr 21, 2023 - Python
Compute-efficient reinforcement learning with binary neural networks and evolution strategies.
Library for Structured Matrices (approximation methods and structured layers for neural networks)
Exploring Variational Deep Q Networks. A study undertaken for the University of Cambridge's R244 Computer Science Masters Course. Inspired by https://arxiv.org/abs/1711.11225/.
[BMVC 2022] Wide Feature Projection with Fast and Memory-Economic Attention for Efficient Image Super-Resolution
Channel-Prioritized Convolutional Neural Networks for Sparsity and Multi-fidelity
[MicroNet Challenge (NeurIPS 2019 )] "Adjustable Quantization: Jointly Learn the Bit-width and Weight in DNN Training" by Yonggan Fu, Ruiyang Zhao, Yue Wang, Chaojian Li, Haoran You, Zhangyang Wang, Yingyan Lin
Official PyTorch training code of Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity (ICCV2023-RCV)
NeurIPS 2019 MicroNet Challenge
Finding Storage- and Compute-Efficient Convolutional Neural Networks
Extremely light-weight MixNet with Top-1 75.7% and 2.5M params
MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation (TCSVT 2022)
Cheng-En Wu, Yi-Ming Chan and Chu-Song Chen "On Merging MobileNets for Efficient Multitask Inference", International Symposium on High-Performance Computer Architecture(HPCA) on Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications(EMC2), 2019
[ICLR24] AutoVP: An Automated Visual Prompting Framework and Benchmark
[ICML 2023] Linkless Link Prediction via Relational Distillation
Cheng-Hao Tu, Jia-Hong Lee, Yi-Ming Chan and Chu-Song Chen, "Pruning Depthwise Separable Convolutions for MobileNet Compression," International Joint Conference on Neural Networks, IJCNN 2020, July 2020.
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
Yi-Min Chou, Yi-Ming Chan, Jia-Hong Lee, Chih-Yi Chiu, Chu-Song Chen, "Unifying and Merging Well-trained Deep Neural Networks for Inference Stage," International Joint Conference on Artificial Intelligence (IJCAI), 2018
[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“
[FPGA'21] CoDeNet is an efficient object detection model on PyTorch, with SOTA performance on VOC and COCO based on CenterNet and Co-Designed deformable convolution.
Code for WF-IoT paper 'TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers'
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