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Places205-VGGNet Models for Scene Recognition

Limin Wang, Sheng Guo, Weilin Huang, Yu Qiao

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

August 10, 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

Face Search at Scale: 80 Million Gallery

Dayong Wang, Charles Otto, Anil K. Jain

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Tags: Computer science, Computer vision, CUDA, Deep learning, Face detection, nVidia

August 3, 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 Convolutional Patch Networks for Scene Understanding

Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler

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Tags: CNN, Computer science, Deep learning, Machine learning, OpenCL, Package

July 24, 2015 by hgpu

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

Baoguang Shi, Xiang Bai, Cong Yao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Tesla K40

July 24, 2015 by hgpu

Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

Keiron O'Shea

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 580

July 24, 2015 by hgpu

DeepFont: Identify Your Font from An Image

Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia

July 17, 2015 by hgpu

Towards Good Practices for Very Deep Two-Stream ConvNets

Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, MPI, nVidia, OpenMPI, Package

July 10, 2015 by hgpu

Experiments on Parallel Training of Deep Neural Network using Model Averaging

Hang Su, Haoyu Chen

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Tags: Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, Tesla K20

July 8, 2015 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Places205-VGGNet Models for Scene Recognition

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

Face Search at Scale: 80 Million Gallery

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

Efficient Convolutional Patch Networks for Scene Understanding

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

DeepFont: Identify Your Font from An Image

Towards Good Practices for Very Deep Two-Stream ConvNets

Experiments on Parallel Training of Deep Neural Network using Model Averaging

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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