Dynamic Training of Liquid State Machines

no code implementations • 6 Feb 2023 • Pavithra Koralalage, Ireoluwa Fakeye, Pedro Machado, Jason Smith, Isibor Kennedy Ihianle, Salisu Wada Yahaya, Andreas Oikonomou, Ahmad Lotfi

The experimental results showed that by using spike metrics and a range of weights, the desired output and the actual output of spiking neurons could be effectively optimised, leading to improved performance of SNNs.

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

28 code implementations • 26 Sep 2016 • Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean

To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.

Machine Translation NMT +2

Unsupervised Learning on an Approximate Corpus

no code implementations • NAACL 2012 • Jason Smith, Jason Eisner

Language Modelling Part-Of-Speech Tagging +1

Review of Hypothesis Alignment Algorithms for MT System Combination via Confusion Network Decoding

no code implementations • WS 2012 • Antti-Veikko Rosti, Xiaodong He, Damianos Karakos, Gregor Leusch, Yuan Cao, Markus Freitag, Spyros Matsoukas, Hermann Ney, Jason Smith, Bing Zhang

Machine Translation