Machine Learning Researcher
ConDo performs domain adaptation (also known as batch correction) under confounding. Domain adaptation methods typically transform domains to match each other, but what if there are true differences between the domains that must be preserved? ConDo conditions on confounding variables while finding an optimal transformation.
PerturbNet learns the gene network that modulates the influence of SNPs on phenotypes, using SNPs as naturally occurring perturbation of a biological system. PerturbNet uses a probabilistic graphical model to directly model both the cascade of perturbation from SNPs to the gene network to the phenotype network and the network at each layer of molecular and clinical phenotypes. PerturbNet learns the entire model by solving a single optimization problem with an extremely fast algorithm that can analyze human genome-wide data within a few hours.
MLPerf™ Inference Benchmark Suite.
[memory-efficient implementation of pyramidal encoder in RNN-T model]
Python library for in-memory classical matrix completion.
[implementation of memory-efficient incremental SV thresholding]
A library that converts TensorFlow models to ONNX.
[statically-determined output padding for ConvTranspose]
Python library for Product Quantization (PQ) and Optimized Product Quantization (OPQ).
[OPQ initialization using multivariate Gaussian assumption]