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8-puzzle

develop a solution for the 8 puzzle problem using path finding and graph traversal algorithms: (BFS) and (A*).

aaai-adversarial-clustering

accessibility-developer-tools

This is a library of accessibility-related testing and utility code.

adatest

Find and fix bugs in natural language machine learning models using adaptive testing.

adversarial-examples

Code for the paper: "Adversarial Examples for Models of Code"

adversarial-robustness-toolbox

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

aries

asr-api-study

async

Async utilities for node and the browser

chai

BDD / TDD assertion framework for node.js and the browser that can be paired with any testing framework.

clever

CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is a robustness metric for deep neural networks

cocofuzzing

code2seq

Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"

code2vec

TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"

code_swapping

codexglue

CodeXGLUE

congruency

[TPAMI2019] The implementation for "Direction Concentration Learning: Enhancing Congruency in Machine Learning"

coreset-al

A greedy implementation of coreset based active learning for image classification (https://arxiv.org/abs/1708.00489)

d3uiteam

dagre

Directed graph layout for JavaScript

decision_boundary

deepconcolic

Concolic Testing for Deep Neural Networks

deepgini

deepgraph

deepwordbug

CodeBase for Paper: "Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers", / Interactive Demo @

densenet

DenseNet implementation in Keras

densenet-cifar10

Train DenseNet on Cifar-10 based on Keras

double-win-quant

[ICML 2021] "Double-Win Quant: Aggressively Winning Robustness of Quantized DeepNeural Networks via Random Precision Training and Inference" by Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, Yingyan Lin

drtest

There is Limited Correlation between Coverage and Robustness for Deep Neural Networks (#387)

error-detection

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks