no code implementations • 10 Nov 2021 • Sheetal Rajpal, Ankit Rajpal, Manoj Agarwal, Naveen Kumar
CONCLUSIONS: The proposed technique enables us to find a concise and clinically relevant gene signature set.
no code implementations • 6 Nov 2021 • Sheetal Rajpal, Virendra Kumar, Manoj Agarwal, Naveen Kumar
By deploying the combined network of stages 1 and 2, we have been able to attain a mean 10-fold test accuracy of 0. 907 on the TCGA breast cancer dataset.
no code implementations • 7 Nov 2020 • Sameer Kumar, James Bradbury, Cliff Young, Yu Emma Wang, Anselm Levskaya, Blake Hechtman, Dehao Chen, HyoukJoong Lee, Mehmet Deveci, Naveen Kumar, Pankaj Kanwar, Shibo Wang, Skye Wanderman-Milne, Steve Lacy, Tao Wang, Tayo Oguntebi, Yazhou Zu, Yuanzhong Xu, Andy Swing
Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run.
no code implementations • 16 Jul 2020 • Sheetal Rajpal, Manoj Agarwal, Ankit Rajpal, Navin Lakhyani, Arpita Saggar, Naveen Kumar
Further, LIME has been integrated with the proposed COV-ELM model to generate annotated CXR images.
no code implementations • 30 Nov 2019 • Anugraha Sinha, Naveen Kumar, Murukesh Mohanan, MD Muhaimin Rahman, Yves Quemener, Amina Mim, Suzana Ilić
In this work, we present a quantized deep neural network deployed on a low-power edge device, inferring learned motor-movements of a suspended robot in a defined space.
no code implementations • 23 Oct 2019 • Prashanth Gurunath Shivakumar, Naveen Kumar, Panayiotis Georgiou, Shrikanth Narayanan
We introduce and analyze different recurrent neural network architectures for incremental and online processing of the ASR transcripts and compare it to the existing offline systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +8
2 code implementations • 2 Oct 2019 • Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim Hazelwood, Andrew Hock, Xinyuan Huang, Atsushi Ike, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Tsuguchika Tabaru, Carole-Jean Wu, Lingjie Xu, Masafumi Yamazaki, Cliff Young, Matei Zaharia
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.
no code implementations • 21 Sep 2019 • Sameer Kumar, Victor Bitorff, Dehao Chen, Chiachen Chou, Blake Hechtman, HyoukJoong Lee, Naveen Kumar, Peter Mattson, Shibo Wang, Tao Wang, Yuanzhong Xu, Zongwei Zhou
The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0. 6 training benchmark demonstrates the scalability of a suite of industry relevant ML models.
1 code implementation • 3 Apr 2019 • Krishna Somandepalli, Naveen Kumar, Ruchir Travadi, Shrikanth Narayanan
We propose Deep Multiset Canonical Correlation Analysis (dMCCA) as an extension to representation learning using CCA when the underlying signal is observed across multiple (more than two) modalities.
no code implementations • EMNLP 2018 • Beto Boullosa, Richard Eckart de Castilho, Naveen Kumar, Jan-Christoph Klie, Iryna Gurevych
Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains.
1 code implementation • ICML 2017 • Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean
Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices.
no code implementations • 16 Apr 2017 • Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, C. Richard Ho, Doug Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Matt Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter, Walter Wang, Eric Wilcox, Doe Hyun Yoon
Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand.