high performance computing on graphics processing units: hgpu.org

hgpu.org » Deep learning

Supervised Hashing with Deep Neural Networks

Ziming Zhang, Yuting Chen, Venkatesh Saligrama

View

Tags: Computer science, CUDA, Deep learning, Hashing, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan

November 20, 2015 by hgpu

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Martin Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

View

Tags: Computer science, Computer vision, CUDA, Deep learning, Heterogeneous systems, Information Retrieval, Machine learning, Neural networks, nVidia, Package, Speech recognition

November 11, 2015 by hgpu

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Diego Marron, Jesse Read, Albert Bifet, Nacho Navarro

View

Tags: Computer science, CUDA, Deep learning, Nearest neighbour, Neural networks, nVidia, Tesla K40

November 8, 2015 by hgpu

Deep Learning for Computer Vision: A comparison between Convolutional Neural Networks and Hierarchical Temporal Memories on object recognition tasks

Vincenzo Lomonaco

View

Tags: Artificial intelligence, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Pattern recognition, Tesla C2075, Thesis

November 8, 2015 by hgpu

ZNN – A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines

Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung

View

Tags: CNN, Computer science, Computer vision, Deep learning, FFT, Intel Xeon Phi, Machine learning, Package

October 25, 2015 by hgpu

Fast Algorithms for Convolutional Neural Networks

Andrew Lavin

View

Tags: Algorithms, CNN, Computer science, Deep learning, FFT, Image recognition, Machine learning, Neural networks

October 3, 2015 by hgpu

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

John-Alexander M. Assael

View

Tags: CNN, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Tesla K40, Thesis

September 26, 2015 by hgpu

Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems

Fabian Tschopp

View

Tags: AMD FirePro W9100, Artificial intelligence, ATI, Caffe, CNN, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Thesis

September 15, 2015 by hgpu

Convolutional Neural Network for Sentence Classification

Yahui Chen

View

Tags: CNN, Computer science, CUDA, Deep learning, Machine learning, Neural networks, NLP, nVidia, nVidia GeForce GTX Titan X, Thesis

September 5, 2015 by hgpu

Partitioning Large Scale Deep Belief Networks Using Dropout

Yanping Huang, Sai Zhang

View

Tags: Benchmarking, Databases, Deep learning, Machine learning, nVidia, nVidia GeForce 8800 GS, Python, Statistics

August 31, 2015 by hgpu

Deep Convolutional Neural Networks for Smile Recognition

Patrick O. Glauner

View

Tags: Computer science, CUDA, Deep learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40, Thesis

August 28, 2015 by hgpu

Accelerated Deep Learning using Intel Xeon Phi

Andre Viebke

View

Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Machine learning, Neural networks, OpenMP, Thesis

August 27, 2015 by hgpu

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

Inside VOLT: Designing an Open-Source GPU Compiler (Tool)

SciDef: Automated Definition Extraction from Scientific Literature

SciDef: Automating Definition Extraction from Academic Literature with Large Language Models

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

See all packages

* * *

* * *

HGPU group © 2010-2026 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: