Skip to search form
Skip to main content
Skip to account menu
Semantic Scholar
Semantic Scholar's Logo
Search 217,273,704 papers from all fields of science
Search
Sign In
Create Free Account
Graphics processing unit
Known as:
Integrated graphics solution
, 3D graphic accelerator
, Graphical processing unit
Expand
A graphics processing unit (GPU), occasionally called visual processing unit (VPU), is a specialized electronic circuit designed to rapidly…
Expand
Wikipedia
(opens in a new tab)
Create Alert
Alert
Related topics
Related topics
50 relations
2.5D
ARM Cortex-A9
ATi Radeon R400 Series
Accelerated Graphics Port
Expand
Broader (1)
Graphics hardware
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Graph Attention Networks
Petar Velickovic
,
Guillem Cucurull
,
Arantxa Casanova
,
Adriana Romero
,
P. Lio’
,
Yoshua Bengio
International Conference on Learning…
2017
Corpus ID: 3292002
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging…
Expand
Highly Cited
2017
Highly Cited
2017
Automatic differentiation in PyTorch
Adam Paszke
,
Sam Gross
,
+7 authors
Adam Lerer
2017
Corpus ID: 40027675
In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on…
Expand
Highly Cited
2016
Highly Cited
2016
TensorFlow: A system for large-scale machine learning
Martín Abadi
,
P. Barham
,
+19 authors
Xiaoqiang Zhang
USENIX Symposium on Operating Systems Design and…
2016
Corpus ID: 6287870
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow…
Expand
Highly Cited
2016
Highly Cited
2016
Feature Pyramid Networks for Object Detection
Tsung-Yi Lin
,
Piotr Dollár
,
Ross B. Girshick
,
Kaiming He
,
Bharath Hariharan
,
Serge J. Belongie
Computer Vision and Pattern Recognition
2016
Corpus ID: 10716717
Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid…
Expand
Highly Cited
2016
Highly Cited
2016
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi
,
Ashish Agarwal
,
+37 authors
Xiaoqiang Zheng
arXiv.org
2016
Corpus ID: 5707386
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A…
Expand
Highly Cited
2016
Highly Cited
2016
Identity Mappings in Deep Residual Networks
Kaiming He
,
X. Zhang
,
Shaoqing Ren
,
Jian Sun
European Conference on Computer Vision
2016
Corpus ID: 6447277
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence…
Expand
Highly Cited
2015
Highly Cited
2015
U-Net: Convolutional Networks for Biomedical Image Segmentation
O. Ronneberger
,
P. Fischer
,
T. Brox
International Conference on Medical Image…
2015
Corpus ID: 3719281
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper…
Expand
Highly Cited
2015
Highly Cited
2015
GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
M. Abraham
,
T. Murtola
,
+4 authors
E. Lindahl
2015
Corpus ID: 11829284
Highly Cited
2014
Highly Cited
2014
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia
,
Evan Shelhamer
,
+5 authors
Trevor Darrell
ACM Multimedia
2014
Corpus ID: 1799558
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning…
Expand
Highly Cited
2008
Highly Cited
2008
Relational joins on graphics processors
Bingsheng He
,
Ke Yang
,
+4 authors
P. Sander
SIGMOD Conference
2008
Corpus ID: 944705
We present a novel design and implementation of relational join algorithms for new-generation graphics processing units (GPUs…
Expand
By clicking accept or continuing to use the site, you agree to the terms outlined in our
Privacy Policy
(opens in a new tab)
,
Terms of Service
(opens in a new tab)
, and
Dataset License
(opens in a new tab)
ACCEPT & CONTINUE