Michael B. Chang

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All published works

Action	Title	Year	Authors
+ PDF Chat	Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context	2024	Gemini Team Machel Reid Nikolay Savinov Denis Teplyashin Dmitry Lepikhin Timothy Lillicrap Jean-baptiste Alayrac Radu Soricut Angeliki Lazaridou
+	Gemini: A Family of Highly Capable Multimodal Models	2023	Gemini Team Rohan Anil Sebastian Borgeaud Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew M. Dai Anja Hauth Katie Millican
+	Automatically Composing Representation Transformations as a Means for Generalization	2018	Michael B. Chang Abhishek Gupta Sergey Levine Thomas L. Griffiths
+	A Compositional Object-Based Approach to Learning Physical Dynamics	2016	Michael B. Chang Tomer Ullman Antonio Torralba Joshua B. Tenenbaum
+	A Compositional Object-Based Approach to Learning Physical Dynamics	2016	Michael B. Chang Tomer Ullman Antonio Torralba Joshua B. Tenenbaum

Common Coauthors

Coauthor	Papers Together
Laura Knight	2
Alek Dimitriev	2
Jack W. Rae	2
Richard Tanburn	2
Maxim Krikun	2
Érica Rodrigues Moreira	2
Demis Hassabis	2
James Molloy	2
Arthur Guez	2
Josip Djolonga	2
Anudhyan Boral	2
Jeremy Wiesner	2
Nimesh Ghelani	2
Jacob Austin	2
Adam Paszke	2
Parker Schuh	2
Paul Komarek	2
Kiran Vodrahalli	2
Mukund Sundararajan	2
Olivier Dousse	2
Alex Tomala	2
Yonghui Wu	2
Komal Jalan	2
Disha Shrivastava	2
Balaji Lakshminarayanan	2
Sanjay Ghemawat	2
Shuo-Yiin Chang	2
Jessica Landon	2
Nithya Attaluri	2
Axel Stjerngren	2
Michael Sharman	2
Sergey Brin	2
Thibault Sottiaux	2
Raphaël Lopez Kaufman	2
Abhanshu Sharma	2
Phil Crone	2
Ken Franko	2
Andrew Brock	2
Mantas Pajarskas	2
David F. Steiner	2
Oriol Vinyals	2
Remi Crocker	2
Tianqi Liu	2
Vincent J. Hellendoorn	2
Devendra Singh Sachan	2
Ethan Dyer	2
Nina Martin	2
Ricardo Aguilar	2
Vittorio Selo	2
Henryk Michalewski	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Understanding Visual Concepts with Continuation Learning	2016	WILLIAM F. WHITNEY Michael Chang Tejas D. Kulkarni Joshua B. Tenenbaum	2
+	Attend, Infer, Repeat: Fast Scene Understanding with Generative Models	2016	S. M. Ali Eslami Nicolas Heess Théophane Weber Yuval Tassa David Szepesvari Koray Kavukcuoglu Geoffrey E. Hinton	2
+	Neural Programmer-Interpreters	2015	Scott Reed Nando de Freitas	2
+	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets	2016	Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel	2
+	Striving for Simplicity: The All Convolutional Net	2014	Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller	1
+	rnn : Recurrent Library for Torch	2015	Nicholas Léonard Sagar Waghmare Yang Wang Jin-Hwa Kim	1
+	Neural GPUs Learn Algorithms	2015	Łukasz Kaiser Ilya Sutskever	1
+	On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models	2015	Juergen Schmidhuber	1
+	Learning to Compose Neural Networks for Question Answering	2016	Jacob Andreas Marcus Rohrbach Trevor Darrell Dan Klein	1
+	Gated Graph Sequence Neural Networks	2016	Yujia Li Daniel Tarlow Marc Brockschmidt Richard S. Zemel	1
+	Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images	2015	Roozbeh Mottaghi Hessam Bagherinezhad Mohammad Rastegari Ali Farhadi	1
+	Learning Visual Predictive Models of Physics for Playing Billiards	2015	Katerina Fragkiadaki Pulkit Agrawal Sergey Levine Jitendra Malik	1
+	To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction	2016	Wenbin Li Seyedmajid Azimi Aleš Leonardis Mario Fritz	1
+	Adaptive Computation Time for Recurrent Neural Networks	2016	Alex Graves	1
+	Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation	2016	Tejas D. Kulkarni Karthik Narasimhan Ardavan Saeedi Joshua B. Tenenbaum	1
+	Learning to Poke by Poking: Experiential Learning of Intuitive Physics	2016	Pulkit Agrawal Ashvin Nair Pieter Abbeel Jitendra Malik Sergey Levine	1
+	TerpreT: A Probabilistic Programming Language for Program Induction	2016	Alexander L. Gaunt Marc Brockschmidt Rishabh Singh Nate Kushman Pushmeet Kohli Jonathan M. Taylor Daniel Tarlow	1
+	The Option-Critic Architecture	2016	Pierre‐Luc Bacon Jean Harb Doina Precup	1
+	Using Fast Weights to Attend to the Recent Past	2016	Jimmy Ba Geoffrey E. Hinton Volodymyr Mnih Joel Z. Leibo Catalin Ionescu	1
+ PDF Chat	Inverse Compositional Spatial Transformer Networks	2017	Chen-Hsuan Lin Simon Lucey	1
+	PathNet: Evolution Channels Gradient Descent in Super Neural Networks	2017	Chrisantha Fernando Dylan Banarse Charles Blundell Yori Zwólš David Ha Andrei A. Rusu Alexander Pritzel Daan Wierstra	1
+	Differentiable Functional Program Interpreters	2016	John Feser Marc Brockschmidt Alexander L. Gaunt Daniel Tarlow	1
+	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	2017	Chelsea Finn Pieter Abbeel Sergey Levine	1
+	Making Neural Programming Architectures Generalize via Recursion	2017	Jonathon Cai Richard Shin Dawn Song	1
+	Metacontrol for Adaptive Imagination-Based Optimization	2017	Jessica B. Hamrick Andrew J. Ballard Razvan Pascanu Oriol Vinyals Nicolas Heess Peter Battaglia	1
+	A simple neural network module for relational reasoning	2017	Adam Santoro David Raposo David G. T. Barrett Mateusz Malinowski Razvan Pascanu Peter Battaglia Timothy Lillicrap	1
+	Gradient Episodic Memory for Continual Learning	2017	David López-Paz Marc’Aurelio Ranzato	1
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	1
+	Independently Controllable Factors	2017	Valentin Thomas Jules Pondard Emmanuel Bengio Marc Sarfati Philippe Beaudoin Marie‐Jean Meurs Joëlle Pineau Doina Precup Yoshua Bengio	1
+	Neural Task Programming: Learning to Generalize Across Hierarchical Tasks	2017	Danfei Xu Suraj Nair Yuke Zhu Julian Gao Animesh Garg Li Fei-Fei Silvio Savarese	1
+	Meta Learning Shared Hierarchies	2017	Kevin Frans Jonathan Ho Xi Chen Pieter Abbeel John Schulman	1
+	Learning to select computations	2017	Falk Lieder Frederick Callaway Sayan Gul Paul M. Krueger Thomas L. Griffiths	1
+	Learning Independent Causal Mechanisms	2017	Giambattista Parascandolo Niki Kilbertus Mateo Rojas-Carulla Bernhard Schölkopf	1
+	Recasting Gradient-Based Meta-Learning as Hierarchical Bayes	2018	Erin Grant Chelsea Finn Sergey Levine Trevor Darrell Thomas L. Griffiths	1
+	Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions	2018	Sjoerd van Steenkiste Michael Chang Klaus Greff Jürgen Schmidhuber	1
+	Towards Synthesizing Complex Programs from Input-Output Examples	2017	Xinyun Chen Chang Liu Dawn Song	1
+	A Simple Neural Attentive Meta-Learner	2017	Nikhil Mishra Mostafa Rohaninejad Xi Chen Pieter Abbeel	1
+	Memorize or generalize? Searching for a compositional RNN in a haystack	2018	Adam Liska Germán Kruszewski Marco Baroni	1
+	Meta-Reinforcement Learning of Structured Exploration Strategies	2018	Abhishek Gupta Russell Mendonca YuXuan Liu Pieter Abbeel Sergey Levine	1
+	Synthesizing Programs for Images using Reinforced Adversarial Learning	2018	Yaroslav Ganin Tejas Kulkarni I. Babuschkin S. M. Ali Eslami Oriol Vinyals	1
+	Universal Planning Networks	2018	Aravind Srinivas Allan Jabri Pieter Abbeel Sergey Levine Chelsea Finn	1
+	Graph networks as learnable physics engines for inference and control	2018	Álvaro Sánchez‐González Nicolas Heess Jost Tobias Springenberg Josh Merel Martin Riedmiller Raia Hadsell Peter Battaglia	1
+	Relational inductive biases, deep learning, and graph networks	2018	Peter Battaglia Jessica B. Hamrick Victor Bapst Álvaro Sánchez‐González Vinícius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner	1
+	Unsupervised Meta-Learning for Reinforcement Learning	2018	Abhishek Gupta Benjamin Eysenbach Chelsea Finn Sergey Levine	1
+	Modular meta-learning	2018	Ferran Alet Tomás Lozano‐Pérez Leslie Pack Kaelbling	1
+	Visual Reinforcement Learning with Imagined Goals	2018	Ashvin Nair Vitchyr H. Pong Murtaza Dalal Shikhar Bahl Steven Lin Sergey Levine	1
+	Modular Networks: Learning to Decompose Neural Computation	2018	Louis Kirsch Julius Kunze David Barber	1
+	Systematic Generalization: What Is Required and Can It Be Learned?	2018	Dzmitry Bahdanau Shikhar Murty Michael Noukhovitch Thien Huu Nguyen Harm de Vries Aaron Courville	1
+	Towards a Definition of Disentangled Representations	2018	Irina Higgins David Amos David Pfau Sébastien Racanière Löıc Matthey Danilo Jimenez Rezende Alexander Lerchner	1
+	Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis	2018	Rudy Bunel Matthew Hausknecht Jacob Devlin Rishabh Singh Pushmeet Kohli	1