Name: Ryan Spring
Type: User
Company: Rice University; @RUSH-LAB ; @Nvidia
Bio: A PhD graduate researching Machine Learning, Locality-Sensitive Hashing, and Deep Learning Compilers.
Twitter: ryanspring13
Location: Santa Clara
Blog: https://www.linkedin.com/in/rdspring1
Ryan Spring's Projects
Autonomous Robots Assignment 4
Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)
Cuda Programming Tutorials
Elements of Programming Interviews
http://www.cs.utexas.edu/~witchel/380L/lab/assign3.html
A toolkit for developing and comparing reinforcement learning algorithms.
https://openai.com/requests-for-research/#inverse-draw
Use LSH Sampling for Mutual Information Estimation
Scalable and Sustainable Deep Learning via Randomized Hashing
LSH Split-Merge MCMC for LDA topic models
One-Shot Learning using Nearest-Neighbor Search (NNS) and Locality-Sensitive Hashing LSH
Assembler for NVIDIA Maxwell architecture
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
Implimentation of the Model Free Episodic Control paper by Deep Mind : http://arxiv.org/abs/1606.04460
A Learnable LSH Framework for Efficient NN Training
2.5D Matrix Multiplication using MPI
Collection of algorithms for approximating Fisher Information Matrix for Natural Gradient (and second order method in general)
A Python-level JIT compiler designed to make unmodified PyTorch programs faster.
Open Source Computer Vision Library
Provides method to access Kinect Color and Depth Stream in OpenCV Mat format.
Stepwise optimizations of DGEMM on CPU, reaching performance faster than Intel MKL eventually, even under multithreading.
Highly optimized DGEMV on CPU with both serial and parallel performance better than MKL and OpenBLAS.
Optimizing SGEMM kernel functions on NVIDIA GPUs to a close-to-cuBLAS performance.
Reinforcement learning environments with musculoskeletal models
Project 2: User Programs
CS439 Project 3: Virtual Memory