Popular GitHub Repositories for Pushing

Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.

[ICML 2024] BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Auxiliary code for pulling, loading reinforcement learning models based on DI-engine from the Huggingface Hub, or pushing them onto Huggingface Hub with auto-created model card.

A PyTorch Lightning Callback for pushing models to the Hugging Face Hub ???

Pushing Text To Speech models into production using torchserve, kubernetes and react web app :smile:

Pushing the Limits of Zero-shot End-to-End Speech Translation

Official code repository for the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain"

A lazy developer's git tool for generating BRUTALLY RUDE commit messages with AI and automatically pushing changes. Uses Ollama to create technically accurate but hilariously offensive commit messages that follow conventional commit format.

Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

Code for Generalization Guarantees for (Multi-Modal) Imitation Learning

This project is dedicated to advancing the field of animatronic robots by enabling them to generate lifelike facial expressions, pushing the boundaries of what's possible in human-robot interaction.

OpioidRL is a cutting-edge reinforcement learning (RL) library that simulates drug addiction behaviors within RL agents. Inspired by the addictive properties of drugs like methamphetamine and crack cocaine, OpioidRL offers a unique environment where agents experience reward dependency, high-risk decision-making, and compulsive behaviors — pushing

Tool for pushing audio samples through sliced FFT into polyphonic MIDI

?AI Developer passionate about pushing the boundaries of what's possible with Large Language Models and automation. Dedicated to creating intelligent solutions that bridge the gap between human creativity and machine efficiency.

VGG 19 is a Very Deep Convolutional Networks for Large-Scale Image Recognition In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.We use the network with pretrained parameters to classify images on ImageNet.