Probabilistic brains: knowns and unknowns
TLDR
The challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference are discussed.Abstract:
There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference.read more
Citations
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Robots that can adapt like animals
Antoine Cully,Jeff Clune,Danesh Tarapore,Danesh Tarapore,Danesh Tarapore,Jean-Baptiste Mouret +5 more
TL;DR: An intelligent trial-and-error algorithm is introduced that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans, and may shed light on the principles that animals use to adaptation to injury.
Journal ArticleDOI
Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing
TL;DR: This work states that biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision, are entering an exciting new era.
Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems
TL;DR: This chapter contains sections titled connectionist Representation and Tensor Product Binding: Definition and Examples, and tensor Product Representation: Properties.
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Confidence and certainty: distinct probabilistic quantities for different goals.
TL;DR: It is proposed that confidence should be defined as the probability that a decision or a proposition is correct given the evidence, a critical quantity in complex sequential decisions and the term certainty should be reserved to refer to the encoding of all other probability distributions over sensory and cognitive variables.
An essay towards solving a problem in the doctrine of chances. [Facsimil]
TL;DR: The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.
References
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Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
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Aspects of the Theory of Syntax
Ann S. Ferebee,Noam Chomsky +1 more
TL;DR: Methodological preliminaries of generative grammars as theories of linguistic competence; theory of performance; organization of a generative grammar; justification of grammar; descriptive and explanatory theories; evaluation procedures; linguistic theory and language learning.
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Aspects of the Theory of Syntax
TL;DR: Generative grammars as theories of linguistic competence as discussed by the authors have been used as a theory of performance for language learning. But they have not yet been applied to the problem of language modeling.
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Information Theory, Inference and Learning Algorithms
TL;DR: A fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
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Information theory, inference, and learning algorithms
TL;DR: In this paper, the mathematics underpinning the most dynamic areas of modern science and engineering are discussed and discussed in a fun and exciting textbook on the mathematics underlying the most important areas of science and technology.
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