[ACL 2024] A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

An open-source library designed for the evaluation of Spiking Neural Networks (SNNs).

相田くひを的《完全自杀手册》 ，这个版本在YuriMiller翻译的基础上添加了一部分注释，并修正了Issues中指出错误的版本，大概以后也会继续维护，会看Issues和Discussions指出文章的错误，一直到告别世界为止

简体中文版本的《完全自杀手册》，自己完成了一部分翻译和校对、注释工作，不久之后就要与这个世界告别，这一本书算是最后的礼物。

Code for paper Weight Expansion: A New Perspective on Dropout and Generalization

Implementation for the paper: Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings

Implementation for the paper: STUN: Self-Teaching Uncertainty Estimation for Place Recognition

Implementation for the paper: AutoPlace: Robust Place Recognition with Single-chip Automotive Radar

https://arxiv.org/pdf/2304.01246.pdf

⏰ AI conference deadline countdowns

Neuromorphic paper list, automatically updating everyday at 8:00 am GMT.

A simple way to deploy local llama-2 models with Docker

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaof…

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

深度学习经典、新论文逐段精读

XAI - An eXplainability toolbox for machine learning

Conversion from CNNs to SNNs using Tensorflow-Keras

The official code for the paper "Delving Deep into Label Smoothing", IEEE TIP 2021

Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.

Glances an Eye on your system. A top/htop alternative for GNU/Linux, BSD, Mac OS and Windows operating systems.

Evaluation robustness of neural network interpretation