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ba_matlab

Bundle Adjustment for Matlab

clrs

📚 Solutions to Introduction to Algorithms Third Edition

cords

Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coresets and data selection.

craig

Data-efficient Training of Machine Learning Models

es654-assignment_4

from-0-to-research-scientist-resources-guide

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

glister

gnn-substructure-counting

The official implementation of ''Can Graph Neural Networks Count Substructures?'' NeurIPS 2020

gnnpapers

Must-read papers on graph neural networks (GNN)

gpstuff

GPstuff - Gaussian process models for Bayesian analysis

learning-kernels

Learning kernels with random features (https://papers.nips.cc/paper/6180-learning-kernels-with-random-features)

module-0

Module 0 - Fundamentals

module-1

Module 1 - Autodifferentiation

module-2

Module 2 - Tensors

module-3

Module 3 - Efficiency

nestedgnn

ntk-sketch-rf

Python implementation of Scaling Neural Tangent Kernels via Sketching and Random Features

preconditioned_gps

Preconditioning Kernel Matrices

ptis

This repository contains the project proposal,code and final report for the implementation of the paper "Parallax Tolerant Image Stitching"(link :"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6909813").

reformer-pytorch

Reformer, the efficient Transformer, in Pytorch

seal

SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction). "M. Zhang, Y. Chen, Link Prediction Based on Graph Neural Networks, NeurIPS 2018 spotlight".

trax

Trax — Deep Learning with Clear Code and Speed

write_a_c_compiler

Test suite to help you write your own C compiler