no code implementations • 14 Oct 2019 • Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei. Lin, Yangqing Jia
One critical issue for efficiently operating practical AI clouds, is to characterize the computing and data transfer demands of these workloads, and more importantly, the training performance given the underlying software framework and hardware configurations.
1 code implementation • CVPR 2019 • Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha
We formulate platform-aware NN architecture search in an optimization framework and propose a novel algorithm to search for optimal architectures aided by efficient accuracy and resource (latency and/or energy) predictors.
5 code implementations • CVPR 2019 • Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer
Due to this, previous neural architecture search (NAS) methods are computationally expensive.
Ranked #789 on Image Classification on ImageNet
no code implementations • 24 Nov 2018 • Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy
The application of deep learning techniques resulted in remarkable improvement of machine learning models.
no code implementations • 6 Dec 2017 • Andrew Tulloch, Yangqing Jia
Many applications of mobile deep learning, especially real-time computer vision workloads, are constrained by computation power.
64 code implementations • 8 Jun 2017 • Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He
To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training.
no code implementations • 15 Mar 2016 • Zhicheng Yan, Hao Zhang, Yangqing Jia, Thomas Breuel, Yizhou Yu
State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs).
4 code implementations • 14 Mar 2016 • Martín 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
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
2 code implementations • CVPR 2015 • Xufeng Han, Thomas Leung, Yangqing Jia, Rahul Sukthankar, Alexander C. Berg
We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods.
77 code implementations • CVPR 2015 • Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).
2 code implementations • 20 Jun 2014 • Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
no code implementations • 21 Dec 2013 • Judy Hoffman, Eric Tzeng, Jeff Donahue, Yangqing Jia, Kate Saenko, Trevor Darrell
In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be?
no code implementations • 17 Dec 2013 • Yunchao Gong, Yangqing Jia, Thomas Leung, Alexander Toshev, Sergey Ioffe
Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications.
no code implementations • NeurIPS 2013 • Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Tom Griffiths, Trevor Darrell
Learning a visual concept from a small number of positive examples is a significant challenge for machine learning algorithms.
8 code implementations • 6 Oct 2013 • Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.
no code implementations • 15 Jan 2013 • Oriol Vinyals, Yangqing Jia, Trevor Darrell
Recently, the computer vision and machine learning community has been in favor of feature extraction pipelines that rely on a coding step followed by a linear classifier, due to their overall simplicity, well understood properties of linear classifiers, and their computational efficiency.
no code implementations • NeurIPS 2012 • Oriol Vinyals, Yangqing Jia, Li Deng, Trevor Darrell
The use of random projections is key to our method, as we show in the experiments section, in which we observe a consistent improvement over previous --often more complicated-- methods on several vision and speech benchmarks.
Ranked #221 on Image Classification on CIFAR-10
no code implementations • NeurIPS 2011 • Yangqing Jia, Trevor Darrell
Many applications in computer vision measure the similarity between images or image patches based on some statistics such as oriented gradients.
no code implementations • NeurIPS 2010 • Yangqing Jia, Mathieu Salzmann, Trevor Darrell
Recent approaches to multi-view learning have shown that factorizing the information into parts that are shared across all views and parts that are private to each view could effectively account for the dependencies and independencies between the different input modalities.