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

Vincenzo Lomonaco

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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

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Diego Marron, Jesse Read, Albert Bifet, Nacho Navarro

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

November 8, 2015 by hgpu

Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network

Peilu Wang, Yao Qian, Frank K. Soong, Lei He, Hai Zhao

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Tags: Computer science, CUDA, Neural networks, NLP, nVidia

October 29, 2015 by hgpu

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

Felix Weninger, Johannes Bergmann, Bjorn Schuller

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 560, Package, Speech recognition

October 8, 2015 by hgpu

Fast Algorithms for Convolutional Neural Networks

Andrew Lavin

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

October 3, 2015 by hgpu

Convolutional Neural Network for Sentence Classification

Yahui Chen

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

September 5, 2015 by hgpu

Deep Convolutional Neural Networks for Smile Recognition

Patrick O. Glauner

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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

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

August 27, 2015 by hgpu

Towards Distortion-Predictable Embedding of Neural Networks

Axel Angel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Thesis

August 7, 2015 by hgpu

Real-Time Pedestrian Detection With Deep Networks Cascades

Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke, Abhijit Ogale, Dave Ferguson

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K20

August 6, 2015 by hgpu

Semantic Pose using Deep Networks Trained on Synthetic RGB-D

Jeremie Papon, Markus Schoeler

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Tags: CNN, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Rendering

August 5, 2015 by hgpu

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

Masato Mimura, Shinsuke Sakai, Tatsuya Kawahara

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Speech recognition

July 29, 2015 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

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

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

Fast Algorithms for Convolutional Neural Networks

Convolutional Neural Network for Sentence Classification

Deep Convolutional Neural Networks for Smile Recognition

Accelerated Deep Learning using Intel Xeon Phi

Towards Distortion-Predictable Embedding of Neural Networks

Real-Time Pedestrian Detection With Deep Networks Cascades

Semantic Pose using Deep Networks Trained on Synthetic RGB-D

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

Recent source codes

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

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