Popular GitHub Repositories for Chi Square

A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

Neo: Hierarchical Confusion Matrix Visualization (CHI 2022)

Official datasets and pytorch implementation repository of SQuARe and KoSBi (ACL 2023)

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

ARChat Chrome Extension for CHI 2023 Papers (Visual Captions, Text Stability)

Angler: Machine Translation Visualization (CHI 2023)

ARDrone autonomous indoor navigation by integrating lsd-slam with imu through least square method

Generating Realistic Synthetic Data

The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.

Implementation of the paper "On the Asymptotic Mean Square Error Optimality of Diffusion Models."

The home of HeadlineSquare and Dr. Headline. HeadlineSquare is a public square for US news headlines, fully powered by Dr. Headline, an autonomous AI agent who applies academic neutrality and rigor to news curation.

AdaptAI: A Personalized Solution to Sense Your Stress, Fix Your Mess, and Boost Productivity

In this work the proposed defense strategy is evaluated against two black-box adversarial attacks, Hop Skip Jump and Square

Chime SMA ChatGPT Multi Lingual IVR for Square Retail

Super Resolution's the images by 3x using CNN

Generate OpenAPI 3 specifications for any Go web project via static analysis, with zero magic comments.

A Smart Traffic Management System for Ho Chi Minh City, Vietnam leveraging batch and real-time data processing, intuitive dashboards, and monitoring tools to optimize traffic flow, enhance safety, and support sustainable urban mobility through advanced analytics and user-friendly applications.

Our project focuses on forecasting photovoltaic (solar) power generation using a hybrid model of Gradient Boosting and LSTM. It predicts solar output with high accuracy, optimizing energy usage, improving grid stability, and enhancing renewable energy integration.