Stars
Code for studying the super weight in LLM
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
[NeurIPS 2024] BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models
A simple, performant and scalable Jax LLM!
The IterativeDisplay class for MATLAB is designed to assist in displaying iterative process updates in a structured and customizable manner.
A playbook for systematically maximizing the performance of deep learning models.
TruthfulQA: Measuring How Models Imitate Human Falsehoods
Simple clipboard manager to be integrated with rofi - Static binary available
Code and data for "Lost in the Middle: How Language Models Use Long Contexts"
Automatically exported from code.google.com/p/latex-bibitemstyler
Graph-Mamba: Towards Long-Range Graph Sequence Modelling with Selective State Spaces
Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
A Latex style and template for paper preprints (based on NIPS style)
良性过拟合现象是深度学习方法揭示的关键奥秘之一:深度神经网络即使完全拟合噪声训练数据,似乎也能很好地预测。
[NeurIPS'18, Spotlight oral] "Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds", by Xiaohan Chen*, Jialin Liu*, Zhangyang Wang and Wotao Yin.
[ICLR 2023] "On Representing Mixed-Integer Linear Programs by Graph Neural Networks" by Ziang Chen, Jialin Liu, Xinshang Wang, Jianfeng Lu, Wotao Yin.
Optimization-based deep learning models can give explainability with output guarantees and certificates of trustworthiness.
LotteryFL: Empower Edge Intelligence with Personalized and Communication-Efficient Federated Learning (2021 IEEE/ACM Symposium on Edge Computing)
Fit interpretable models. Explain blackbox machine learning.