Python Neural-differential-equations Resources

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

Portal is the fastest way to load and visualize your deep neural networks on images and videos 🔮

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (SDPoint) This repository contains the cod

PNASNet.pytorch PyTorch implementation of PNASNet-5. Specifically, PyTorch code from this repository is adapted to completely match both my implemetat

Neural Implicit Reconstruction with Vector Neurons This repository contains code for the neural implicit reconstruction experiments in the paper Vecto

IDRLnet IDRLnet is a machine learning library on top of PyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inver

Licensing The majority of neural-scs is licensed under the CC BY-NC 4.0 License, however, portions of the project are available under separate license

LTP: Learned Token Pruning for Transformers Check our paper for more details. Installation We follow the same installation procedure as the original H

One-shot Neural Architecture Search uses a single supernet to approximate the performance each architecture. However, this performance estimation is super inaccurate because of co-adaption among operations in supernet.

UnICORNN (Undamped Independent Controlled Oscillatory RNN) [ICML 2021] This repository contains the implementation to reproduce the numerical experime

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: give

VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

NeuralCompression is a Python repository dedicated to research of neural networks that compress data. The repository includes tools such as JAX-based entropy coders, image compression models, video compression models, and metrics for image and video evaluation.

Neural Arithmetic Logic Units [WIP] This is a PyTorch implementation of Neural Arithmetic Logic Units by Andrew Trask, Felix Hill, Scott Reed, Jack Ra

Update 2019/06/24: A model trained on 10% of the Shepard-Metzler dataset has been added, the following notebook explains the main features of this mod

Transparency-by-Design networks (TbD-nets) This repository contains code for replicating the experiments and visualizations from the paper Transparenc

This is a fork of Fairseq(-py) with implementations of the following models: Pervasive Attention - 2D Convolutional Neural Networks for Sequence-to-Se

deep-dream-in-pytorch Pytorch (https://github.com/pytorch/pytorch) implementation of the deep dream (https://en.wikipedia.org/wiki/DeepDream) computer

pytorch-flows A PyTorch implementations of Masked Autoregressive Flow and some other invertible transformations from Glow: Generative Flow with Invert

relational-rnn-pytorch An implementation of DeepMind's Relational Recurrent Neural Networks (Santoro et al. 2018) in PyTorch. Relational Memory Core (

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

This is an attempt to reproduce results in Perturbative Neural Networks paper. See original repo for details.

WaveGlow A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis Quick Start: Install requirements: pip install

Visualizing the Loss Landscape of Neural Nets This repository contains the PyTorch code for the paper Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer

Graph Wavelet Neural Network ⠀⠀ A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019). Abstract We present graph wavelet neural network

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

SGCN ⠀ A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today's data can be represented as

GAM ⠀⠀ A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practic

A Closer Look at Structured Pruning for Neural Network Compression Code used to reproduce experiments in https://arxiv.org/abs/1810.04622. To prune, w

SimGNN ⠀⠀⠀ A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity s

Character Based CNN This repo contains a PyTorch implementation of a character-level convolutional neural network for text classification. The model a

DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the

APPNP ⠀ A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message pass

MixHop and N-GCN ⠀ A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)

Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte

CapsGNN ⠀⠀ A PyTorch implementation of Capsule Graph Neural Network (ICLR 2019). Abstract The high-quality node embeddings learned from the Graph Neur

BigGAN-PyTorch The author's officially unofficial PyTorch BigGAN implementation. This repo contains code for 4-8 GPU training of BigGANs from Large Sc

Hailo Model Zoo The Hailo Model Zoo provides pre-trained models for high-performance deep learning applications. Using the Hailo Model Zoo you can mea

RandWireNN Unofficial PyTorch Implementation of: Exploring Randomly Wired Neural Networks for Image Recognition. Results Validation result on Imagenet

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks This repository implements a capsule model Inten

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

ClusterGCN ⠀⠀ A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019). A

QuickDraw - AirGesture Introduction Here is my python source code for QuickDraw - an online game developed by google, combined with AirGesture - a sim

VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

LiDAR-MOS: Moving Object Segmentation in 3D LiDAR Data This repo contains the code for our paper: Moving Object Segmentation in 3D LiDAR Data: A Learn

On the Bottleneck of Graph Neural Networks and its Practical Implications This is the official implementation of the paper: On the Bottleneck of Graph

Vision Transformer Architecture Search This repository open source the code for ViTAS: Vision Transformer Architecture Search. ViTAS aims to search fo

MicRank: Learning to Rank Microphones for Distant Speech Recognition Application Scenario Many applications nowadays envision the presence of multiple

Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation This repository is the pytorch implementation of our paper: Hierarchical Cr

ElasticGNN This repository includes the official implementation of ElasticGNN in the paper "Elastic Graph Neural Networks" [ICML 2021]. Xiaorui Liu, W

neural waveshaping synthesis real-time neural audio synthesis in the waveform domain paper • website • colab • audio by Ben Hayes, Charalampos Saitis,

PGExplainer This is a Tensorflow implementation of the paper: Parameterized Explainer for Graph Neural Network https://arxiv.org/abs/2011.04573 NeurIP

GLAT Implementation for the ACL2021 paper "Glancing Transformer for Non-Autoregressive Neural Machine Translation" Requirements Python = 3.7 Pytorch

​ DS2.ai is an integrated AI operation solution that supports all stages from custom AI development to deployment. It is an AI-specialized platform service that collects data, builds a training dataset through data labeling, and enables automatic development of artificial intelligence and easy deployment and operation.

code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

Visual-Reasoning-eXplanation [CVPR 2021 A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts] Project Page | Vid

A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.

NCRF Prerequisites Data Whole slide images Annotations Patch images Model Training Testing Tissue mask Probability map Tumor localization FROC evaluat

EfficientNet A PyTorch implementation of EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. [arxiv] [Official TF Repo] Implemen

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening Introduction This is an implementation of the model used for breast

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pu

Draw like Bob Ross using the power of Neural Networks! (+ Pytorch) Learning Process Visualization Getting started Install dependecies Requires python3

Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with Source: https://www.ka

Torchreid Torchreid is a library for deep-learning person re-identification, written in PyTorch. It features: multi-GPU training support both image- a

Hasktorch Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the co

Master Release Pytorch - Py + Nim A Nim frontend for pytorch, aiming to be mostly auto-generated and internally using ATen. Because Nim compiles to C+

UNet for segmenting salt deposits from seismic images with PyTorch. General We, tugstugi and xuyuan, have participated in the Kaggle competition TGS S

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

ocaml-torch provides some ocaml bindings for the PyTorch tensor library. This brings to OCaml NumPy-like tensor computations with GPU acceleration and tape-based automatic differentiation.

tch-rs Rust bindings for the C++ api of PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorc

All-in-one web-based development environment for machine learning Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issu

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields [project page][paper][cite] Geometry-Consistent Neural Shape Represe

HyperPose is a library for building high-performance custom pose estimation applications.

Densely Connected Time Delay Neural Network PyTorch implementation of Densely Connected Time Delay Neural Network (D-TDNN) in our paper "Densely Conne

NU-Wave — Official PyTorch Implementation NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling Junhyeok Lee, Seungu Han @ MINDsLab Inc

Neural Logic Inductive Learning This is the implementation of the Neural Logic Inductive Learning model (NLIL) proposed in the ICLR 2020 paper: Learn

Depth-supervised NeRF: Fewer Views and Faster Training for Free Project | Paper | YouTube Pytorch implementation of our method for learning neural rad

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets This is the official PyTorch implementation for the paper Rapid Neural A

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

The goal is to classify different birds species based on their songs/calls. Spectrograms have been extracted from the audio samples and used as features for classification.

This is my reading list for my PhD in AI, NLP, Deep Learning and more.

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

Balancing Training for Multilingual Neural Machine Translation Implementation of the paper Balancing Training for Multilingual Neural Machine Translat

REval Table of Contents Introduction Overview Requirements Installation Probing Usage Citation License 🎓 Introduction REval is a simple framework for

Implicit Representations of Meaning in Neural Language Models Preliminaries Create and set up a conda environment as follows: conda create -n state-pr

R-Drop: Regularized Dropout for Neural Networks R-drop is a simple yet very effective regularization method built upon dropout, by minimizing the bidi

FastPitchFormant - PyTorch Implementation PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis. Qu

AnalyticMesh Analytic Marching is an exact meshing solution from neural networks. Compared to standard methods, it completely avoids geometric and top

Recurrent Fast Weight Programmers This is the official repository containing the code we used to produce the experimental results reported in the pape

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

Graph4NLP Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP).

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

Non-autoregressive Deep Learning-Based TTS Template This is a template for the Non-autoregressive TTS model. It contains Data Preprocessing Pipeline D

EdMIPS is an efficient algorithm to search the optimal mixed-precision neural network directly without proxy task on ImageNet given computation budgets. It can be applied to many popular network architectures, including ResNet, GoogLeNet, and Inception-V3.

SNN_Calibration Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021 Feature Comparison of SNN calibration: Features SNN Direct Tr

AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis | Project Page | Paper | PyTorch implementation for the paper "AD-NeRF: Audio

Soft DTW Loss Function for PyTorch in CUDA This is a Pytorch Implementation of Soft-DTW: a Differentiable Loss Function for Time-Series which is batch