Building machines that learn and think like people

Deep Visual-Semantic Alignments for Generating Image Descriptions

(2017) Andrej Karpathy et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Hybrid computing using a neural network with dynamic external memory

(2016) Alex Graves et al.   NATURE

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Unsupervised learning of invariant representations

(2016) Fabio Anselmi et al.   THEORETICAL COMPUTER SCIENCE

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Probabilistic machine learning and artificial intelligence

(2015) Zoubin Ghahramani   NATURE

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Divisions of the physical world: Concepts of objects and substances.

(2015) Lance J. Rips et al.   PSYCHOLOGICAL BULLETIN

Human-level concept learning through probabilistic program induction

(2015) B. M. Lake et al.   SCIENCE

Observing the unexpected enhances infants' learning and exploration

(2015) A. E. Stahl et al.   SCIENCE

Model-Based Reasoning in Humans Becomes Automatic with Training

(2015) Marcos Economides et al.   PLoS Computational Biology

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing

(2015) Nikolaus Kriegeskorte   Annual Review of Vision Science

A rational account of pedagogical reasoning: Teaching by, and learning from, examples

(2014) Patrick Shafto et al.   COGNITIVE PSYCHOLOGY

Performance-optimized hierarchical models predict neural responses in higher visual cortex

(2014) D. L. K. Yamins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Probabilistic models, learning algorithms, and response variability: sampling in cognitive development

(2014) Elizabeth Bonawitz et al.   TRENDS IN COGNITIVE SCIENCES

Learning with Hierarchical-Deep Models

(2013) R. Salakhutdinov et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

From machine learning to machine reasoning

(2013) Léon Bottou   MACHINE LEARNING

Goals and Habits in the Brain

(2013) Ray J. Dolan et al.   NEURON

Simulation as an engine of physical scene understanding

(2013) P. W. Battaglia et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Reconciling intuitive physics and Newtonian mechanics for colliding objects.

(2013) Adam N. Sanborn et al.   PSYCHOLOGICAL REVIEW

Cognitive control over learning: Creating, clustering, and generalizing task-set structure.

(2013) Anne G. E. Collins et al.   PSYCHOLOGICAL REVIEW

The Neuroscience of Learning: Beyond the Hebbian Synapse

(2012) C.R. Gallistel et al.   Annual Review of Psychology

A probabilistic account of exemplar and category generation

(2012) Alan Jern et al.   COGNITIVE PSYCHOLOGY

Canonical Microcircuits for Predictive Coding

(2012) Andre M. Bastos et al.   NEURON

From simple innate biases to complex visual concepts

(2012) S. Ullman et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A Large-Scale Model of the Functioning Brain

(2012) C. Eliasmith et al.   SCIENCE

The origins of inquiry: inductive inference and exploration in early childhood

(2012) Laura Schulz   TRENDS IN COGNITIVE SCIENCES

Monte-Carlo tree search and rapid action value estimation in computer Go

(2011) Sylvain Gelly et al.   ARTIFICIAL INTELLIGENCE

Multistability and Perceptual Inference

(2011) Samuel J. Gershman et al.   NEURAL COMPUTATION

Bayesian sampling in visual perception

(2011) R. Moreno-Bote et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference

(2011) E. Teglas et al.   SCIENCE

How to Grow a Mind: Statistics, Structure, and Abstraction

(2011) J. B. Tenenbaum et al.   SCIENCE

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

(2011) Dejan Pecevski et al.   PLoS Computational Biology

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

(2011) Lars Buesing et al.   PLoS Computational Biology

Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes

(2011) Mehdi Keramati et al.   PLoS Computational Biology

Infants consider both the sample and the sampling process in inductive generalization

(2010) H. Gweon et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Letting structure emerge: connectionist and dynamical systems approaches to cognition

(2010) James L. McClelland et al.   TRENDS IN COGNITIVE SCIENCES

Probabilistic models of cognition: exploring representations and inductive biases

(2010) Thomas L. Griffiths et al.   TRENDS IN COGNITIVE SCIENCES

Reinforcement learning in the brain

(2009) Yael Niv   JOURNAL OF MATHEMATICAL PSYCHOLOGY