Pinned Repositories
cs228-notes
Course notes for CS228: Probabilistic Graphical Models.
llmtools
Finetuning Large Language Models on One Consumer GPU in Under 4 Bits
audio-super-res
Audio super resolution using neural networks
cornell-cs5785-2020-applied-ml
Teaching materials for the applied machine learning course at Cornell Tech (online edition)
cornell-cs5785-2023-applied-ml
Lecture materials for Cornell CS5785 Applied Machine Learning (Fall 2023)
gwaskb
Machine-curated database of genetic disease and genome-wide association studies
minillm
MiniLLM is a minimal system for running modern LLMs on consumer-grade GPUs
teaching-material
Teaching materials for the machine learning and deep learning classes at Stanford and Cornell
tensor-factorization
Tensor Factorization via Matrix Factorization
tf-wgan
Wasserstein DCGAN in Tensorflow/Keras
kuleshov's Repositories
kuleshov/audio-super-res
Audio super resolution using neural networks
kuleshov/teaching-material
Teaching materials for the machine learning and deep learning classes at Stanford and Cornell
kuleshov/cornell-cs5785-2020-applied-ml
Teaching materials for the applied machine learning course at Cornell Tech (online edition)
kuleshov/minillm
MiniLLM is a minimal system for running modern LLMs on consumer-grade GPUs
kuleshov/cornell-cs5785-2023-applied-ml
Lecture materials for Cornell CS5785 Applied Machine Learning (Fall 2023)
kuleshov/tf-wgan
Wasserstein DCGAN in Tensorflow/Keras
kuleshov/gwaskb
Machine-curated database of genetic disease and genome-wide association studies
kuleshov/tensor-factorization
Tensor Factorization via Matrix Factorization
kuleshov/cs228-notes
Lecture notes on probabilistic graphical modeling, based on Stanford CS228 (work in progress!)
kuleshov/online-learning
A few basic online learning algorithms
kuleshov/deep-learning-models
Implementations of popular deep learning models in Theano+Lasagne
kuleshov/architect
Scaffolding genomes using synthetic long read clouds
kuleshov/deep-hybrid-models
Deep hybrid models: bridging discriminative and generative approaches https://cs.stanford.edu/~ermon/papers/uai2017_cr.pdf
kuleshov/neural-variational-inference
Neural variational inference and learning in undirected graphical models http://www.stanford.edu/~kuleshov/papers/nips2017.pdf
kuleshov/generalized-rayleigh-quotient
Fast algorithms for sparse principal component analysis
kuleshov/ProbHap
Probabilistic single-individual haplotyping
kuleshov/convolutional-draw
Tensorflow implementation of Convolutional DRAW by Gregor et al. (2016)
kuleshov/prism
Statistical phasing software for long read data
kuleshov/dotfiles
kuleshov/multivariate-deep-learning
kuleshov/pixelcnn-pp-experiments
kuleshov/scripts
Various short scripts I wrote for my work
kuleshov/glow-experiments
kuleshov/Glow-PyTorch
Simple, extendable, easy to understand Glow implementation in PyTorch
kuleshov/improved-gan
code for the paper "Improved Techniques for Training GANs"
kuleshov/mm-experiment
kuleshov/NeuralRandomFieldLearning
http://web.stanford.edu/~kuleshov/papers/iclr2016.pdf
kuleshov/snorkel
A lightweight platform for developing information extraction systems using data programming
kuleshov/srez
Image super-resolution through deep learning
kuleshov/tempens
Temporal ensembling for semi-supervised learning