Neural and Evolutionary Computing
Hamiltonian Monte Carlo Particle Swarm Optimizer
June, 2022
Abstract
Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem
GEMA: An open-source Python library for self-organizing-maps
March, 2022
Abstract
Deep Convolutional Autoencoder for Assessment of Anomalies in Multi-stream Sensor Data
February, 2022
Abstract
A fully convolutional autoencoder is developed for the detection of anomalies in multi-sensor vehicle drive-cycle data from the powertrain domain. Preliminary results collected on real-world powertrain data show that the reconstruction error of faulty drive cycles deviates significantly relative to the reconstruction of healthy drive cycles using the trained autoencoder. The results demonstrate applicability for identifying faulty drive-cycles, and for improving the accuracy of system prognosis and predictive maintenance in connected vehicles.
In-Datacenter Performance Analysis of a Tensor Processing Unit
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.
One-Shot Imitation Learning
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
March 2017
Abstract:
Learning to Generate Reviews and Discovering Sentiment
Alec Radford, Rafal Jozefowicz, Ilya Sutskever
April, 2017
Abstract:
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever
March 2017
Abstract:
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies
Shihao Ji, S. V. N. Vishwanathan, Nadathur Satish, Michael J. Anderson, Pradeep Dubey
Mar 2016
Abstract: