Jiaqing-ASU

easycv

The app to create cover letter based on job description and your own resume with LangChain + LLM

logicnets

m3_learning

Datasets, Tutorials, and Papers for the M3-Learning Research Group

RCworkshops

ASU Research Computing :: materials from hosted workshops

Contour-Stochastic-Gradient-Langevin-Dynamics

An elegant adaptive importance sampling algorithms for simulations of multi-modal distributions (NeurIPS'20)

helm

Holistic Evaluation of Language Models (HELM), a framework to increase the transparency of language models (https://arxiv.org/abs/2211.09110). This framework is also used to evaluate text-to-image models in Holistic Evaluation of Text-to-Image Models (HEIM) (https://arxiv.org/abs/2311.04287).

LoRA

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

stanford_alpaca

Code and documentation to train Stanford's Alpaca models, and generate the data.

WeightWatcher

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

dnn-mode-connectivity

Mode Connectivity and Fast Geometric Ensembles in PyTorch

hidden-networks

learning-subspaces

optimizer

This repository includes code to reproduce the tables in "Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent"

PyHessian

PyHessian is a Pytorch library for second-order based analysis and training of Neural Networks

mnist-c

robustbench

RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]

d3-3d

d3-3d is a powerful JavaScript library designed for 3D visualizations, specifically tailored to work seamlessly with d3.js. This library enables the projection of 3D data onto web browsers, making it an essential tool for developers interested in creating immersive and dynamic visualizations.

loss-landscapes

Approximating neural network loss landscapes in low-dimensional parameter subspaces for PyTorch

loss_landscape_taxonomy

[NeurIPS 2021] code for "Taxonomizing local versus global structure in neural network loss landscapes" https://arxiv.org/abs/2107.11228

tf-resnet-cifar10

TensorFlow implementation of ResNet-110 tested on CIFAR10

gam-changer

Editing machine learning models to reflect human knowledge and values

loss-landscape

Code for visualizing the loss landscape of neural nets

ttk

TTK - Topological Data Analysis and Visualization - Source Code

onnx-mlir

Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure

d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada: