chandu-97's Stars
floodsung/Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
pytorch/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
datasciencescoop/Data-Science--Cheat-Sheet
Cheat Sheets
torch/torch7
http://torch.ch
jcjohnson/pytorch-examples
Simple examples to introduce PyTorch
hill-a/stable-baselines
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
ikostrikov/pytorch-a2c-ppo-acktr-gail
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
cornellius-gp/gpytorch
A highly efficient implementation of Gaussian Processes in PyTorch
szagoruyko/pytorchviz
A small package to create visualizations of PyTorch execution graphs
cbfinn/maml
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
enggen/DeepMind-Advanced-Deep-Learning-and-Reinforcement-Learning
Advanced Deep Learning and Reinforcement Learning course taught at UCL in partnership with Deepmind
cvondrick/videogan
Generating Videos with Scene Dynamics. NIPS 2016.
glouppe/info8006-introduction-to-ai
Lectures for INFO8006 Introduction to Artificial Intelligence, ULiège
ethanjperez/film
FiLM: Visual Reasoning with a General Conditioning Layer
apeterswu/RL4NMT
Reinforcement Learning for Neural Machine Translation
cvalenzuela/hpc
A quick reference to access NYU High Performance Computing
activatedgeek/torchrl
Highly Modular and Scalable Reinforcement Learning
foolyc/Meta-SGD
Meta-SGD experiment on Omniglot classification compared with MAML
dylandjian/retro-contest-sonic
World Models applied to the Open AI Sonic Retro Contest
stc/HackPact
A one month experimentation with sound, algorithms & unconventional sequencers
PetterS/patch-inpainting
Image inpainting using coherency senstitive hashing
ignaciorlando/fundus-vessel-segmentation-tbme
In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
cbaziotis/keras-utilities
Utilities for Keras - Deep Learning library
aborghi/retro_contest_agent
cbaziotis/datastories-semeval2017-task6
Deep-learning model presented in "DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison".
srikarym/OCR_Telugu_code
distillpub/post--feature-wise-transformations
Feature-Wise Transformations
sanjanprakash/Cluster-Analysis-over-Financial-Characteristics
Grouping North American corporations into year-wise clusters based on their financial variables. A time-series correlation analysis was also performed within each cluster using ARIMA models to forecast stock prices.
nyu-pl-sp19/recitations
Notes from the Recitations
manishmadugula/Speech_processing-Phonological-processes-
Intern at NITK