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2s-agcn

Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

actor

Official Pytorch implementation of the paper "Action-Conditioned 3D Human Motion Synthesis with Transformer VAE", ICCV 2021

adversarialtexture

Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

ai4all

All About AI

all-lab

All-lab

automated-r-codes

This code will help to Working with Multiple Projects Simultaneously

awesome-federated-machine-learning

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

awesome-go

A curated list of awesome Go frameworks, libraries and software

awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. By everyone, for everyone!

bayesian_trafficprediction

best-heart-drug-selection

Created for toolchain: https://cloud.ibm.com/devops/toolchains/c706570e-7143-4aec-9b9c-72b6d06ac805?env_id=ibm%3Ayp%3Ajp-tok

blind-justice

:lock: Blind Justice :lock: Code for the paper "Blind Justice: Fairness with Encrypted Sensitive Attributes", ICML 2018

caret

caret (Classification And Regression Training) R package that contains misc functions for training and plotting classification and regression models

ccurprograming

R Programing course's Lab

cgcnn

clean-fid

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

cnn_randrnn

When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition

colorgram.py

A Python module for extracting colors from images. Get a palette of any picture!

comogan

CoMoGAN: continuous model-guided image-to-image translation. CVPR 2021 oral.

cooc_texture

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

crbm-drum-patterns

Python code to do Conditional Restricted Boltzmann machine training applied to drum pattern generation.

create-grid-index

Creates a polygon feature class which acts as a grid overlain on another feature class. This is useful for setting up Data Driven Pages. The script demonstrates use of geometry objects and Describe objects in ArcPy.

cryptonets

CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

cs273a-introduction-to-machine-learning

Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

darch

Create deep architectures in the R programming language

datasharing

myData

decent

Unpaired Image Translation, Neurips2022

deep

some temporal RBM based models we're playing with. Deep Learning ;-)

deep-belief-nets-for-topic-modeling

This repository is a proof of concept toolbox for using Deep Belief Nets for Topic Modeling in Python.