saimunikoti

saimunikoti's Stars

• lucidrains/DALLE2-pytorch

  Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch

  Language:Python11.1k1.1k

• coaxsoft/pytorch_bert

  Tutorial for how to build BERT from scratch

  Language:Python8321

• YuliaRubanova/latent_ode

  Code for "Latent ODEs for Irregularly-Sampled Time Series" paper

  Language:Python516126

• FanzhenLiu/Awesome-Deep-Community-Detection

  Deep and conventional community detection related papers, implementations, datasets, and tools.

  49593

• anmol098/anmolsingh.me

  This is my personal website here I keep updating changes which cannot be summed up in my Resume

  Language:JavaScript35717

• aprbw/traffic_prediction

  Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

  Language:TeX27946

• philtabor/Deep-Q-Learning-Paper-To-Code

  Language:Python349146

• learnables/learn2learn

  A PyTorch Library for Meta-learning Research

  Language:Python2.7k353

• DynamicaLab/code-dynalearn

  Language:Python239

• rail-berkeley/rlkit

  Collection of reinforcement learning algorithms

  Language:Python2.5k554

• qihongl/dnd-lstm

  A Python(PyTorch) implementation of memory augmented neural network based on Ritter et al. (2018). Been There, Done That: Meta-Learning with Episodic Recall. ICML.

  Language:Jupyter Notebook338

• BKHMSI/Meta-RL-TwoStep-Task

  PyTorch implementation of Episodic Meta Reinforcement Learning on variants of the "Two-Step" task. Reproduces the results found in three papers. Check the ReadMe for more details!

  Language:Python324

• mahakal001/reinforcement-learning

  A collection of various projects related to Reinforcement Learning

  Language:Jupyter Notebook189

• dmlc/dgl

  Python package built to ease deep learning on graph, on top of existing DL frameworks.

  Language:Python13.5k3k

• Haipeng-Chen/RL4IM-Contingency

  This project aims at using RL to address the contingency-aware influence maximization problem

  Language:Python93

• Aditya239233/GNNExplainer

  Code for running experiments and benchmarking on GNNExplainer: Generating Explanations for Graph Neural Networks

  Language:Python111

• ppope/explain_graphs

  Code for "Explainability methods for graph convolutional neural networks" - PE Pope*, S Kolouri*, M Rostami, CE Martin, H Hoffmann (CVPR 2019)

  Language:Jupyter Notebook342

• idea-iitd/GCOMB

  Language:C++323

• IBM/UQ360

  Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

  Language:Python25661

• albermax/interpretable_ai_book__sw_chapter

  The code snippets for the SW chapter of the "Interpretable AI" book.

  Language:Jupyter Notebook176

• nisyad/network-of-networks-resilience

  Network-of-networks modeling approach for critical infrastructure resilience.

  Language:Jupyter Notebook155

• geopanag/pandemic_tgnn

  Language:Python4314

• bbengfort/hadoop-fundamentals

  Code for the Hadoop Fundamentals for Data Scientists course.

  Language:Python165150

• drkhan107/CoroNet

  CoroNet is a Covid-19 detection and diagnosis tool. It scans chest x-rays and clasifies it into normal, pneumonia or covid classes

  Language:Jupyter Notebook1813

• agchung/Figure1-COVID-chestxray-dataset

  Figure 1 COVID-19 Chest X-ray Dataset Initiative

  3325

• ieee8023/covid-chestxray-dataset

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

  Language:Jupyter Notebook3k1.3k

• IN-CORE/pyincore

  pyIncore is a component of IN-CORE. It is a python package consisting of two primary components: 1) a set of service classes to interact with the IN-CORE web services, and 2) IN-CORE analyses . The pyIncore allows users to apply various hazards to infrastructure in selected areas, propagating the effect of physical infrastructure damage and loss of functionality to social and economic impacts.

  Language:Python257

• Hanjun-Dai/graph_comb_opt

  Implementation of "Learning Combinatorial Optimization Algorithms over Graphs"

  Language:C++495135

• anderzzz/london_bike_forecast

  Graph convolutional neural network for forecasting traffic in the London bike-share system, where the graph convolutions pass spatial information between stations, and one-dimensional convolutions pass information from past traffic.

  Language:Python65

• SheffieldML/GPy

  Gaussian processes framework in python

  Language:Python2k562