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Dive into the surprising conceptual parallels between Quantum Field Theory and Neural Field Models. Learn how physics' most fundamental theory can inspire new approaches in AI, complete with practical Python examples demonstrating field dynamics
Understanding the Shift to Neural Machine Translation Evaluation Neural Machine Translation (NMT) has fundamentally changed how we assess translation
AutoInt uses a multi-head self-attentive neural network with residual connections to automatically learn high-order feature interactions for efficient click-through rate
Discover the Surprising Hidden Dangers of Generative Teaching Networks in AI - Brace Yourself
What would we do without the helpful voices in our GPS systems guiding us to our destinations? How would we look up esoteric facts without the silky voices of
A Matlab Neural Network model, also known as an artificial neural network (ANN), is a computational model inspired by the structure and functioning of the human
I don’t remember how I came across it, but this is one of the most exciting papers I’ve read recently. The authors train a neural network that tries to identify the next in a sequence of MNIST samples, presented in digit order. The interesting part is that when they include a proxy for energy usage in the loss function (i.e. train it to be more energy-efficient), the resulting network seems to exhibit the characteristics of predictive coding: some units seem to be responsible for predictions, others for
CSP Test --> Main Navigation ICML My Stuff Login Select Year: (2021) 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2002 1996 IMLS Archives Workshop INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models Chin-Wei Huang ⋅ David Krueger ⋅ Rianne Van den Berg ⋅ George Papamakarios ⋅ Ricky T. Q. Chen ⋅ Danilo J. Rezende Project Page Abstract Normalizing flows are explicit likelihood models (ELM
NeurIPS Proceedings Search SIMPLIFYING NEURAL NETS BY DISCOVERING FLAT MINIMA Sepp Hochreiter, Jürgen Schmidhuber Advances in Neural Information Processing Systems 7 (NIPS 1994) Abstract We present a new algorithm for finding low complexity networks with high generalization capability. The algorithm searches for large connected regions of so-called ''fiat'' minima of the error func(cid:173) tion. In the weight-space environment of a "flat" minimum, the error remains approximately constant. Using an MDL