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Yixin Liu's Projects

r3m icon r3m

Pre-training Reusable Representations for Robotic Manipulation Using Diverse Human Video Data

radial_rl_v2 icon radial_rl_v2

This repository contains the official code for our NeurIPS 2021 publication "Robust Deep Reinforcement Learning through Adversarial Loss​"

runner icon runner

A light-weight script for launching a large number of experiments.

sayhello icon sayhello

A simple message board application build with Flask.

scipdf_parser icon scipdf_parser

Python PDF parser for scientific publications: content and figures

scut-bachelor-thesis-template icon scut-bachelor-thesis-template

Latex template for the bachelor graduation thesis of South China University of Technology (SCUT) 华南理工大学 本科毕业论文LaTeX模板

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

shekou-watchdog icon shekou-watchdog

一个用于监控蛇口母港至香港国际机场船票的工具,支持微信、邮件实时推送通知。

smoothing icon smoothing

Provable adversarial robustness at ImageNet scale

smoothing-adversarial icon smoothing-adversarial

Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"

stable-unlearnable-example icon stable-unlearnable-example

[AAAI'24] Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise

tensorflow icon tensorflow

An Open Source Machine Learning Framework for Everyone

transformer-explainability icon transformer-explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

ule-grayaug icon ule-grayaug

Going Grayscale: The Road to Understanding and Improving Unlearnable Examples

vit-pytorch icon vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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