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Ian Goodfellow Quotes
born 1987 United States
Ian J. Goodfellow (born 1987) is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning.
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12 quotes
“While neuroscience is an important source of inspiration, it need not be taken as a rigid guide.”
“Cognitive science is an interdisciplinary approach to understanding the mind, combining multiple different levels of analysis.”
“A probability distribution over discrete variables may be described using a probability mass function (PMF).”
“The central idea in connectionism is that a large number of simple computational units can achieve intelligent behavior when networked together.”
“We assume familiarity with programming, a basic understanding of computational performance issues, complexity theory, introductory level calculus and some of the terminology of graph theory.”
“A representation learning algorithm can discover a good set of features for a simple task in minutes, or for a complex task in hours to months.”
“Even today’s networks, which we consider quite large from a computational systems point of view, are smaller than the nervous system of even relatively primitive vertebrate animals like frogs.”
“∑x∈xP(x) = 1. We refer to this property as being normalized. Without this property, we could obtain probabilities greater than one by computing the probability of one of many events occurring.”
“one of the names that deep learning has gone by is artificial neural networks (ANNs).”
“Working successfully with datasets smaller than this is an important research area, focusing in particular on how we can take advantage of large quantities of unlabeled examples, with unsupervised or semi-supervised learning.”
“The former kind of probability, related directly to the rates at which events occur, is known as frequentist probability, while the latter, related to qualitative levels of certainty, is known as Bayesian probability.”
“we do not yet know enough about biological learning for neuroscience to offer much guidance for the learning algorithms we use to train these architectures.”
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