chrisyxue

RMN-RPN-for-FSL

The source codes for paper "Relative Position and Map Networks in Few-shot Learning for Image Classification"

RCN_for_Interpretable_few_shot

The source codes for Region Comparison Network for Interpretable Few-shot Image Classification

DA4AL

Codes for 'Exploring Minimally Sufficient Representation in Active Learning through Label-Irrelevant Patch Augmentation'

AdversarialQuerying

A PyTorch implementation of the method found in "Adversarially Robust Few-Shot Learning: A Meta-Learning Approach"

CloserLookFewShot

source code to ICLR'19, 'A Closer Look at Few-shot Classification'

GAFairC

The source codes for paper 'Group AdaBoost with Fairness Constraint' accepted by SDM 23

lime

Lime: Explaining the predictions of any machine learning classifier

MMPF

Implementation of Minimax Pareto Fairness framework

academic-kickstart

BIND

Open-source code for "Interpreting Unfairness in Graph Neural Networks via Training Node Attribution".

CA-AT

The codes for Conflict-Aware Adversarial Training

CNN-from-Scratch

A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset

CSE482_NBA

The source codes for CSE482 Project in MSU

CSE847

Codes for CSE847

daily-paper-computer-vision

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

easy-rl

强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/

FEAT

The code repository for "Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions"

group_DRO

Distributionally robust neural networks for group shifts

interpretability-by-parts

[CVPR 2020 Oral] Interpretable and Accurate Fine-grained Recognition via Region Grouping

Jailbreak_Proj

Project of Jailbreak

LLMforChildren

LLM for Children

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

meta-dataset

A dataset of datasets for learning to learn from few examples

neural-backed-decision-trees

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

NLP-HW3

HW3 for NLP Class

ProtoPNet

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 Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University) (* denotes equal contribution).

PRWN_for_FEAT

The source codes about using PRW Loss on FEAT for Semi-Supervised Learning

Sketch-Based-Deep-Learning

A resource repository for sketch based deep learning papers

Zhiyu_Xue

zyxue.github.com

薛至喻的个人主页