Displaying 1 to 20 from 37 results

AmpliGraph - Python library for Representation Learning on Knowledge Graphs https://docs

  •    Python

Open source library based on TensorFlow that predicts links between concepts in a knowledge graph. AmpliGraph is a suite of neural machine learning models for relational Learning, a branch of machine learning that deals with supervised learning on knowledge graphs.

simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

  •    Jupyter

News! We have released a TF2 implementation of SimCLR (along with converted checkpoints in TF2), they are in tf2/ folder. News! Colabs for Intriguing Properties of Contrastive Losses are added, see here.

CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.

  •    Jupyter

We would like to thank all participants for their submissions and we hope that this challenge provided insights to practitioners and researchers about the challenges in semantic code search and motivated new research. We would like to encourage everyone to continue using the dataset and the human evaluations, which we now provide publicly. Please, see below for details, specifically the Evaluation section. No new submissions to the challenge will be accepted.

awesome-network-embedding - A curated list of network embedding techniques.

  •    

Also called network representation learning, graph embedding, knowledge embedding, etc. The task is to learn the representations of the vertices from a given network.




self-label - Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)

  •    Python

🆕✅🎉 updated code: 23rd April 2020: bug fixes + CIFAR code + evaluation for resnet & alexnet. Checkout our blogpost for a quick non-technical overview and an interactive visualization of our clusters.

PaddleHelix - Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

  •    Python

2021.06.17 PaddleHelix team won the 2nd place in the OGB-LCS KDD Cup 2021 PCQM4M-LSC track, predicting DFT-calculated HOMO-LUMO energy gap of molecules. Please refer to the solution for more details. 2021.05.20 PaddleHelix v1.0 released. 1) Update from static framework to dynamic framework; 2) Add new applications: molecular generation and drug-drug synergy.

ConMask - ConMask model described in paper Open-world Knowledge Graph Completion.

  •    Python

Code for AAAI'18 paper: Open-world Knowledge Graph Completion. Warning: Current implementation needs a machine with four GPUs, this could be reduced to 1 GPU but needs code modification.

bimu - Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders

  •    Python

The individual similarity scores, presented as averages in the paper, are reported in appendix. See python3.4 examples/run_bimu.py --help for the full list of options, and set the Theano flags as THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32.


hmm-reps - Hidden Markov models for word representations

  •    Python

Learn discrete and continuous word representations with Hidden Markov models, including variants defined over unlabeled and labeled parse trees. Despite the mentioned, the running time is relatively slow and is especially sensitive to the number of states. A speed-up would be possible through the use of sparse matrices, but at several places the replacement is not trivial.

sigver_wiwd - Learned representation for Offline Handwritten Signature Verification

  •    Jupyter

This repository contains the code and instructions to use the trained CNN models described in [1] to extract features for Offline Handwritten Signatures. It also includes the models described in [2] that can generate a fixed-sized feature vector for signatures of different sizes. We tested the code in Ubuntu 16.04. This code can be used with or without GPUs - to use a GPU with Theano, follow the instructions in this link. Note that Theano takes time to compile the model, so it is much faster to instantiate the model once and run forward propagation for many images (instead of calling many times a script that instantiates the model and run forward propagation for a single image).

cuNVSM - Neural Vector Space Models

  •    Cuda

⚠️ You need a CUDA-compatible GPU (compute capability 5.2+) to use this software. cuNVSM is a C++/CUDA implementation of state-of-the-art NVSM and LSE representation learning algorithms.

SERT - Semantic Entity Retrieval Toolkit

  •    Python

The Semantic Entity Retrieval Toolkit (SERT) is a collection of neural entity retrieval algorithms. SERT requires Python 3.5 and assorted modules. The trec_eval utility is required for evaluation and the end-to-end scripts. If you wish to train your models on GPGPUs, you will need a GPU compatible with Theano.

sub-character-cws - Sub-Character Representation Learning

  •    Python

Codes and corpora for paper "Dual Long Short-Term Memory Networks for Sub-Character Representation Learning" (accepted at ITNG 2018). We proposed to learn character and sub-character level representations jointly for capturing deeper level of semantic meanings. When applied to Chinese Word Segmentation as a case example, our solution achieved state-of-the-art results on both Simplified and Traditional Chinese, without extra Traditional to Simplified Chinese conversion.

ICE - ICE: Item Concept Embedding

  •    C++

The ICE toolkit is designed to embed the concepts of items into an embedding representation such that the resulted embeddings can be compared in terms of overall conceptual similarity regardless of item types (ICE: Item Concept Embedding via Textual Information, SIGIR 2017). For example, a song can be used to retrieve conceptually similar songs (homogeneous) as well as conceptually similar concepts (heterogeneous). In specific, ICE incorporates items and their representative concepts (words extracted from the item's textual information) using a heterogeneous network and then learns the embeddings for both items and concepts in terms of the shared concept words. Since items are defined in terms of concepts, adding expanded concepts into the network allows the learned embeddings to be used to retrieve conceptually more diverse and yet relevant results.

proNet-core - A general-purpose network embedding framework: pair-wise representations optimization Network

  •    C++

In the near future, we will redesign the framework making some solid APIs for fast development on different network embedding techniques. This shell script will help obtain the representations of the Youtube links in Youtube-links dataset.

program-induction - A library for program induction and learning representations.

  •    Rust

A library for program induction and learning representations. Implements Bayesian program learning and genetic programming. See the docs for more information.

Variational-Ladder-Autoencoder - Implementation of VLAE

  •    Python

This is the implementation of the Variational Ladder Autoencoder. Training on this architecture with standard VAE disentangles high and low level features without using any other prior information or inductive bias. This has been successful on MNIST, SVHN, and CelebA. LSUN is a little difficult for VAE with pixel-wise reconstruction loss. However with another recently work we can generate sharp results on LSUN as well. This architecture serve as the baseline architecture for that model.

robotics-rl-srl - S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics

  •    Python

This repository was made to evaluate State Representation Learning methods using Reinforcement Learning. It integrates (automatic logging, plotting, saving, loading of trained agent) various RL algorithms (PPO, A2C, ARS, ACKTR, DDPG, DQN, ACER, CMA-ES, SAC, TRPO) along with different SRL methods (see SRL Repo) in an efficient way (1 Million steps in 1 Hour with 8-core cpu and 1 Titan X GPU). We also release customizable Gym environments for working with simulation (Kuka arm, Mobile Robot in PyBullet, running at 250 FPS on a 8-core machine) and real robots (Baxter Robot, Robobo with ROS).

srl-zoo - State Representation Learning (SRL) zoo with PyTorch - Part of S-RL Toolbox

  •    Python

A collection of State Representation Learning (SRL) methods for Reinforcement Learning, written using PyTorch. Please read the documentation for more details, we provide anaconda env files and docker images.






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