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Tim Rocktäschel's Projects

awesome-torch icon awesome-torch

A curated list of awesome Torch tutorials, projects and communities

babi-tasks icon babi-tasks

Task generation for testing text understanding and reasoning

baselines icon baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

breeze icon breeze

Breeze is a numerical processing library for Scala.

chemspot icon chemspot

ChemSpot is a named entity recognition tool for identifying mentions of chemicals in natural language texts, including trivial names, drugs, abbreviations, molecular formulas and IUPAC entities. Since the different classes of relevant entities have rather different naming characteristics, ChemSpot uses a hybrid approach combining a Conditional Random Field with a dictionary. ChemSpot is released under the Common Public License 1.0.

conv_arithmetic icon conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

demos icon demos

Demos and tutorials around Torch7.

dnc icon dnc

A TensorFlow implementation of the Differentiable Neural Computer.

dnc-tensorflow icon dnc-tensorflow

A TensorFlow implementation of DeepMind's Differential Neural Computers (DNC)

dp icon dp

A deep learning library for streamlining research and development using the Torch7 distribution.

einops icon einops

Deep learning operations reinvented (for pytorch, tensorflow, chainer, gluon and others)

elf icon elf

An End-To-End, Lightweight and Flexible Platform for Game Research

enas-pytorch icon enas-pytorch

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

examples icon examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

factorie icon factorie

FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.

ganhacks icon ganhacks

starter from "How to Train a GAN?" at NIPS2016

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