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3dconv-lstm-fmri-denoising

aerial-imagery

Data Science Research Project: Map poverty using satellite images.

awesome-cell-cell-communication

Collection of computational tools for cell-cell communication inference for single-cell and spatially resolved omics

awesome-deeplearning-resources

Deep Learning and deep reinforcement learning research papers and some codes

awesome-graph-explainability-papers

Papers about explainability of GNNs

awesome-graph-llm

A collection of AWESOME things about Graph-Related LLMs.

awesome-graph-transformer

Papers about graph transformers.

ccc-benchmark

Systematic benchmarking of computational methods for cell- cell communication inference from single-cell or spatial transcriptomics

continual-learning-baselines

Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.

deepspeedexamples

Example models using DeepSpeed

fcrn-depthprediction

Deeper Depth Prediction with Fully Convolutional Residual Networks (FCRN)

gnnpapers

Must-read papers on graph neural networks (GNN)

gpt-engineer

Specify what you want it to build, the AI asks for clarification, and then builds it.

jarvis

JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf

new-grad-positions

Collection of New Grad Full Time positions in SWE, Quant, and PM.

ogb-lsc-pcqm4mv2

The OGB-LSC is the Large Scale Competition by Open Graph Benchmark to help accelerate research into machine learning on graph structured data

prompt-engineering-guide

🐙 Guides, papers, lecture, notebooks and resources for prompt engineering

pyscisci

Science of Science

satimage

Code and models for the manuscript "Predicting Poverty and Developmental Statistics from Satellite Images using Multi-task Deep Learning"

scbert

scfoundation

snn_benchmark

transformerst-deconv

Integrating Histology Imaging for Reference-Free Cell-Type Deconvolution in Spatial Transcriptomics

unsupervised-capsule-network

Capsule network with variations. Originally proposed by Tieleman & Hinton : http://www.cs.toronto.edu/~tijmen/tijmen_thesis.pdf