NeurIPS Proceedings Search Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics Niru Maheswaranathan, Alex Williams, Matthew Golub, Surya Ganguli, David Sussillo Advances in Neural Information Processing Systems 32 (NeurIPS 2019) Abstract Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes. Given a trained recurrent network, we would like to reverse engineer it--to obtain a
Networks of coupled dynamical systems have been used to model biological oscillators1,2,3,4, Josephson junction arrays5,6, excitable media7, neural networks8,9,10, spatial games11, genetic control networks12 and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned throug
Learn how the Perceptron Neural Network processes inputs, makes decisions, and powers AI. Explore its structure, working, and real-world applications
NeurIPS 2020 What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Meta Review The reviews feel that the issues are interesting and the contributions are sufficient for acceptance. However, there are serious suggestions for improvements in the experiments. It seems the paper is suggestive, but not definitive, on the long tail hypothesis
Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks. For machine translation, despite the evolution from long short-term memory networks to Transformer networks, plus the introduction and development of attention mechanism, encoder-decoder is still the de facto neural network architecture for state-of-the-art models. While the motivation for decoding information from some hidden space is straightforward, the strict separation of the encoding and decoding steps into an enco
Chris Hulbert, Splinter Software, is a contracting iOS developer based in Australia.
Characterizing the computational power of neural network architectures in terms of formal language theory remains a crucial line of research, as it describes lower and
(Phys.org)—A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published
The French government is planning to hold a confidence vote on Prime Minister François Bayrou’s fiscal plan. If a coalition of opposition parties votes against the government, as is widely expected, Bayrou will have to submit his resignation to French President Emmanuel Macron. This could cause a fall to the middle band for the STOXX […]
IBM’s Dmitry Krotov is a theorist on the hunt for artificial neural networks that can crunch data as efficiently as the brain