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

[NeurIPS 2024 Spotlight]"LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS", Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, Zhangyang Wang

Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

List of papers related to neural network quantization in recent AI conferences and journals.

[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

[CVPR 2021] Exploring Sparsity in Image Super-Resolution for Efficient Inference

(CVPR 2021, Oral) Dynamic Slimmable Network

Explorations into some recent techniques surrounding speculative decoding

[ECCV2022] Efficient Long-Range Attention Network for Image Super-resolution

On-device LLM Inference Powered by X-Bit Quantization

Deep Face Model Compression

[ECCV 2022] Official implementation of the paper "DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation"

[NeurIPS 2024] AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising

[ICLR 2022] Code for Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (GLNN)

Soft Threshold Weight Reparameterization for Learnable Sparsity