Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
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Updated
May 8, 2024 - Python
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]
Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"
[CVPR 2024] DeepCache: Accelerating Diffusion Models for Free
[ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
"LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS", Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, Zhangyang Wang
List of papers related to neural network quantization in recent AI conferences and journals.
[CVPR 2021] Exploring Sparsity in Image Super-Resolution for Efficient Inference
(CVPR 2021, Oral) Dynamic Slimmable Network
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
Deep Face Model Compression
[ECCV2022] Efficient Long-Range Attention Network for Image Super-resolution
[ECCV 2022] Official implementation of the paper "DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation"
Explorations into some recent techniques surrounding speculative decoding
On-device LLM Inference Powered by X-Bit Quantization
Soft Threshold Weight Reparameterization for Learnable Sparsity
[NeurIPS'23] Speculative Decoding with Big Little Decoder
[ICLR 2022] Code for Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (GLNN)
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