Xiao Wang (wang3702)

wang3702

Geek Repo

Company:University of Washington

Location:Seattle

Home Page:https://xiaowang.org

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Xiao Wang's repositories

EnAET

EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning

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CoSeg

CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation

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pix2seq

Pix2Seq codebase: multi-tasks with generative modeling (autoregressive and diffusion)

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wang3702.github.io

My personal website

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ADpred_publication

scripts and notebook for publication of ADpred

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barlowtwins

PyTorch implementation of Barlow Twins.

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DivideMix

Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning

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DOVE

Docking Model Evaluation by 3D Deep Convo-lutional Neural Networks Xiao Wang, Genki Terashi, Charles W. Christoffer, Mengmeng Zhu, and Daisuke Kihara, In submission (2019)

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Emap2sec

Emap2sec is a computational tool to identify protein secondary structures

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L_DMI

Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"

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mae-1

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

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moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

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MoCo-Pytorch

An unofficial Pytorch implementation of "Improved Baselines with Momentum Contrastive Learning" (MoCoV2) - X. Chen, et al.

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moco-v3

PyTorch implementation of MoCo v3 https//arxiv.org/abs/2104.02057

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NLNL-Negative-Learning-for-Noisy-Labels

NLNL: Negative Learning for Noisy Labels

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PlotNeuralNet

Latex code for making neural networks diagrams

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pytorch-randaugment

Unofficial PyTorch Reimplementation of RandAugment.

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README

README文件语法解读,即Github Flavored Markdown语法介绍

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representation-flow-cvpr19

Code and models for our CVPR'19 paper "Representation Flow for Action Recognition"

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swav

PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882

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torchlars

A LARS implementation in PyTorch

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unsup_temp_embed

Official implementation of the paper: Unsupervised learning of action classes with continuous temporal embedding (CVPR'19)

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wassdistance

Approximating Wasserstein distances with PyTorch

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