Multi-view-AE
Multi-view-AE: A Python package for multi-view autoencoder models - Published in JOSS (2023)
uform
Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️
cambrian
Cambrian-1 is a family of multimodal LLMs with a vision-centric design.
tyc-dataset
Official and maintained implementation of the dataset paper "The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures" [ICCVW 2023].
https://github.com/accenture/ampligraph
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
player-compatibality-and-win-pred-in-dota2-using-graph-neural-networks
A GNN based approach to model player compatibility in Multiplayer Online Battle Arena (MOBA) games like Dota2.
https://github.com/alleninstitute/coupledae-patchseq
Multimodal data alignment and cell type analysis with coupled autoencoders.
https://github.com/araffin/robotics-rl-srl
S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics
https://github.com/ferencberes/online-node2vec
Node Embeddings in Dynamic Graphs
https://github.com/araffin/srl-zoo
State Representation Learning (SRL) zoo with PyTorch - Part of S-RL Toolbox
https://github.com/cthoyt/embeddingdb
A database for storing and comparing entity embeddings
https://github.com/astrazeneca/subtab
The official implementation of the paper, "SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning"
speech-utility-bioacoustics
On the utility of speech and audio foundation models for marmoset call analysis
https://github.com/aida-ugent/csne
Source code for CSNE: Conditional Signed Network Embeddings (CIKM2020)
tsde
TSDE is a novel SSL framework for TSRL, the first of its kind, effectively harnessing a diffusion process, conditioned on an innovative dual-orthogonal Transformer encoder architecture with a crossover mechanism, and employing a unique IIF mask strategy (KDD 2024, main research track).
mergevq
[CVPR] MergeVQ: A Unified Framework for Visual Generation and Representation with Token Merging and Quantization
ssl-caller-detection
Source code for the paper 'Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?' by E. Sarkar and M. Magimai Doss (2023).
https://github.com/aida-ugent/nrl4lp
Instructions for replicating the experiments in the paper "Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?" (DSAA2020)
https://github.com/aiot-mlsys-lab/cate
[ICML 2021 Oral] "CATE: Computation-aware Neural Architecture Encoding with Transformers" by Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang
https://github.com/aiot-mlsys-lab/arch2vec
[NeurIPS 2020] "Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?" by Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang
https://github.com/biomedsciai/gene-benchmark
Benchmark gene representations from different model families
https://github.com/cvjena/semantic-embeddings
Hierarchy-based Image Embeddings for Semantic Image Retrieval
syncx
Official code repository for NeurIPS 2024 paper "Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery"
maskedfacerepresentation
Masked face recognition focuses on identifying people using their facial features while they are wearing masks. We introduce benchmarks on face verification based on masked face images for the development of COVID-safe protocols in airports.
https://github.com/alfa-group/code-representations-ml-brain
[NeurIPS 2022] "Convergent Representations of Computer Programs in Human and Artificial Neural Networks" by Shashank Srikant*, Benjamin Lipkin*, Anna A. Ivanova, Evelina Fedorenko, Una-May O'Reilly.
https://github.com/berenslab/medical-t-simcne
This repository contains the codes to train a t-SimCNE model. This model has been shown to produce good representations on natural and medical images.
https://github.com/biodt/bfm-model
Multi-modal Foundation Model for Biodiversity dynamics forecasting
https://github.com/cardiokit/vecg
Representational Learning of Single-Lead Electrocardiogram Signals using Beta-TCVAE