Python Adaptive-gradient-clipping Resources

Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s

DSP Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021. Authors

Convolutional Networks with Adaptive Inference Graphs (ConvNet-AIG) This repository contains a PyTorch implementation of the paper Convolutional Netwo

Integrated Gradients This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found h

Bonsai is a wrapper for the XGBoost and Catboost model training pipelines that leverages Bayesian optimization for computationally efficient hyperparameter tuning.

Gradient Matching for Domain Generalisation This is the official PyTorch implementation of Gradient Matching for Domain Generalisation. In our paper,

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021) This repository is for BAAF-Net introduce

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. The constraints are defined as upper bounds on sub-objective loss function. MooGBT uses a Augmented Lagrangian(AL) based constrained optimization framework with Gradient Boosted Trees, to optimize for multiple objectives.

GPRGNN This is the source code for our ICLR2021 paper: Adaptive Universal Generalized PageRank Graph Neural Network. Hidden state feature extraction i

A ready-to-use framework of latest models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, and etc.

자율 주행차의 영상 기반 차간거리 유지 개발 Table of Contents 프로젝트 소개 주요 기능 시스템 구조 디렉토리 구조 결과 실행 방법 참조 팀원 프로젝트 소개 YOLO-v5 기반으로 단안 카메라의 영상을 활용해 차간 거리를 일정하게 유지하며 주행하는 Adap

[PDF] | [Slides] The official implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021 Long talk) Installation Inst

Image Translation with ASAPNets Spatially-Adaptive Pixelwise Networks for Fast Image Translation, CVPR 2021 Webpage | Paper | Video Installation insta

RainNet — Official Pytorch Implementation Region-aware Adaptive Instance Normalization for Image Harmonization Jun Ling, Han Xue, Li Song*, Rong Xie,

Batch Soft-DTW(Dynamic Time Warping) in TensorFlow2 including forward and backward computation Custom TensorFlow2 implementations of forward and backw

Toy implementations of some popular ML optimizers using Python/JAX

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

ASGNet The code is for the paper "Adaptive Prototype Learning and Allocation for Few-Shot Segmentation" (accepted to CVPR 2021) [arxiv] Overview data/

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.09

PGBM Probabilistic Gradient Boosting Machines (PGBM) is a probabilistic gradient boosting framework in Python based on PyTorch/Numba, developed by Air

You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient (paper) @misc{zhang2021compress,

SYMPAIS: Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Overview | Installation | Documentation | Examples | Notebo

ATLOP Code for AAAI 2021 paper Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling. If you make use of this co

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging This repository contains an implementation

Geometry-Aware Gradient Algorithms for Neural Architecture Search This repository contains the code required to run the experiments for the DARTS sear

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking. TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa.

One-Shot Voice Conversion with Weight Adaptive Instance Normalization By Shengjie Huang, Yanyan Xu*, Dengfeng Ke*, Mingjie Chen, Thomas Hain. This rep

Gradient-Induced Co-Saliency Detection Zhao Zhang*, Wenda Jin*, Jun Xu, Ming-Ming Cheng ⭐ Project Home » The official repo of the ECCV 2020 paper Grad

UnrealPerson: An Adaptive Pipeline for Costless Person Re-identification In our paper (arxiv), we propose a novel pipeline, UnrealPerson, that decreas

RADN [CVPRW 2021] Code for Region-Adaptive Deformable Network for Image Quality Assessment [Paper on arXiv] Overview Update [2021/5/7] add codes for W

GCA Source code for Graph Contrastive Learning with Adaptive Augmentation (WWW 2021) For example, to run GCA-Degree under WikiCS, execute: python trai

Paddle-RLBooks Welcome to Paddle-RLBooks which is a reinforcement learning code study guide based on pure PaddlePaddle. 欢迎来到Paddle-RLBooks，该仓库主要是针对强化学

Reinforcement Learning (PyTorch) 🤖 + 🍰 = ❤️ This repo will contain PyTorch implementation of various fundamental RL algorithms. It's aimed at making

CorDA Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation Prerequisite Please create and activate the follo

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms This repo contains the source code to reproduce the results in the paper A Close

AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data [WIP] Unofficial Pytorch implementation of AdaSpeech 2. Requirements : All code written i

Introduction This is the PyTorch implementation for Adaptive Graph Representation Learning for Video Person Re-identification. Get started git clone h

BNN - BN = ? Training Binary Neural Networks without Batch Normalization Codes for this paper BNN - BN = ? Training Binary Neural Networks without Bat

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

This is the original implementation of our paper, A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (arXiv:1706.1

A tour through tensorflow with financial data I present several models ranging in complexity from simple regression to LSTM and policy networks. The s

Stock Trading Market OpenAI Gym Environment with Deep Reinforcement Learning using Keras Overview This project provides a general environment for stoc

...Minimizing the mean square error on future experience. - Richard S. Sutton BTGym Scalable event-driven RL-friendly backtesting library. Build on

LifelongReID Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu L

PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds by Mutian Xu*, Runyu Ding*, Hengshuang Zhao, and Xiaojuan Qi. Int

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021, official Pytorch implementatio

gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D

Adaptive Transformers in RL Official implementation of Adaptive Transformers in RL In this work we replicate several results from Stabilizing Transfor

GS-WGAN This repository contains the implementation for GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS

AS-Net Code for one-stage adaptive set-based HOI detector AS-Net. Mingfei Chen*, Yue Liao*, Si Liu, Zhiyuan Chen, Fei Wang, Chen Qian. "Reformulating

RPG (Reward-Randomized Policy Gradient) Zhenggang Tang*, Chao Yu*, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Shaolei Du, Yu Wang, Yi Wu (

Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Gradient Centralization TensorFlow This Python package implements Gradient Centralization in TensorFlow, a simple and effective optimization technique

Summary Pyroomacoustics is a software package aimed at the rapid development and testing of audio array processing algorithms. The content of the pack

Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

PyTorch implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping Paper: https://arxiv.org/abs/2102.06171.pdf Original code: htt

Keras implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Differentiable Morphing Image morphing without reference points by applying warp maps and optimizing over them. Differentiable Morphing is machine lea

SLM Lab Modular Deep Reinforcement Learning framework in PyTorch. Documentation: https://slm-lab.gitbook.io/slm-lab/ BeamRider Breakout KungFuMaster M

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Install

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

auto_ml Automated machine learning for production and analytics Installation pip install auto_ml Getting started from auto_ml import Predictor from au

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl