Pedro Sandoval (psandovalsegura)

psandovalsegura

Geek Repo

Location:College Park, MD

Home Page:http://www.cs.umd.edu/~psando/

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Pedro Sandoval's repositories

autoregressive-poisoning

Code for the paper "Autoregressive Perturbations for Data Poisoning" (NeurIPS 2022)

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learn-from-unlearnable

Code for the paper "What Can We Learn from Unlearnable Datasets?" (NeurIPS 2023)

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AdversarialQuerying

Code for "Interpretability for Prototypical Networks" (based on the repo for Adv Robust Few-Shot Learning)

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synergy-algorithms

Algorithms for learning team compositions using synergy

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advertorch

A Toolbox for Adversarial Robustness Research

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CenterNet

An easy to understand version of CenterNet

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fairgame

Tool to help us buy hard to find items.

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ffcv-imagenet

Train ImageNet *fast* in 500 lines of code with FFCV

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imagenet-c-p

Corruption and Perturbation Robustness (ICLR 2019)

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mae

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

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nanoGPT

The simplest, fastest repository for training/finetuning medium-sized GPTs.

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neural-anisotropy-directions

Source code for "Neural Anisotropy Directions"

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pix2seq

Pix2Seq - A general framework for turning RGB pixels into semantically meaningful sequences

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Pytorch-Adversarial-Training-CIFAR

This repository provides simple PyTorch implementations for adversarial training methods on CIFAR-10.

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pytorch-semseg

Semantic Segmentation Architectures Implemented in PyTorch

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PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.

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Robust-Semantic-Segmentation

Robust Semantic Segmentation using AdvProp (a fork of DDC-AT)

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robustness

A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.

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semseg

Semantic Segmentation in Pytorch

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ViT-CIFAR

PyTorch implementation for Vision Transformer[Dosovitskiy, A.(ICLR'21)] modified to obtain over 90% accuracy FROM SCRATCH on CIFAR-10 with small number of parameters (= 6.3M, originally ViT-B has 86M).

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VT

Enhancing the Transferability of Adversarial Attacks through Variance Tuning

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