Fetching the paper…
Reading the bibliography…
We provide a study of how induced model sparsity can help achieve compositional generalization and better sample efficiency in grounded language learning problems.
A Markovian Decision Process
Richard Bellman. 1957 · 1957
Earlier work this paper cites.
Aspects of the Theory of Syntax
N. Chomsky. 1965 · 1965
Earlier work this paper cites.
Learning to achieve goals
Leslie Pack Kaelbling. 1993 · 1993
Earlier work this paper cites.
David Yu-Tung Hui, Maxime Chevalier-Boisvert, Dzmitry Bahdanau, and Yoshua Bengio. 2020 · 2007
Earlier work this paper cites.
Think before you act: A simple baseline for compositional generalization
Christina Heinze-Deml and Diane Bouchacourt. 2020 · 2009
Earlier work this paper cites.
Learning to interpret natural language navigation instructions from observations
David L. Chen and Raymond J. Mooney. 2011 · 2011
Earlier work this paper cites.
Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
Earlier work this paper cites.
Charles Beattie, Joel Z. Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, Julian Schrittwieser, Keith Anderson, Sarah York, Max Cant, Adam Cain, Adrian Bolton, Stephen Gaffney, Helen King, Demis Hassabis, Shane Legg, and Stig Petersen. 2016 · 2016
Earlier work this paper cites.
Natural language communication with robots
Yonatan Bisk, Deniz Yuret, and Daniel Marcu. 2016 · 2016
Earlier work this paper cites.
Grounded language learning in a simulated 3d world
Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, and Phil Blunsom. 2017 · 2017
Earlier work this paper cites.
Zero-shot task generalization with multi-task deep reinforcement learning
Junhyuk Oh, Satinder P. Singh, Honglak Lee, and Pushmeet Kohli. 2017 · 2017
Earlier work this paper cites.
Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, and Anton van den Hengel. 2018 · 2018
Earlier work this paper cites.
Gated-attention architectures for task-oriented language grounding
Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, and Ruslan Salakhutdinov. 2018 · 2018
Earlier work this paper cites.
Minimalistic Gridworld Environment for OpenAI Gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal. 2018 · 2018
Earlier work this paper cites.
Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, and David Silver. 2018 · 2018
Cited alongside, same era.
Grounding language for transfer in deep reinforcement learning
Karthik Narasimhan, Regina Barzilay, and Tommi S. Jaakkola. 2018 · 2018
Cited alongside, same era.
FiLM: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron C. Courville. 2018 · 2018
Cited alongside, same era.
Towards sample efficient reinforcement learning
Yang Yu. 2018 · 2018
Cited alongside, same era.
Learning to understand goal specifications by modelling reward
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Seyed Arian Hosseini, Pushmeet Kohli, and Edward Grefenstette. 2019 · 2019
Cited alongside, same era.
BabyAI: A platform to study the sample efficiency of grounded language learning
ALFRED: A benchmark for interpreting grounded instructions for everyday tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox. 2020 · 2020
Later among the works it cites.
Social and governance implications of improved data efficiency
Aaron D. Tucker, Markus Anderljung, and Allan Dafoe. 2020 · 2020
Later among the works it cites.
Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare. 2021 · 2021
Later among the works it cites.
Neural event semantics for grounded language understanding
Shyamal Buch, Li Fei-Fei, and Noah D. Goodman. 2021 · 2021
Later among the works it cites.
Zero-shot task adaptation using natural language
Prasoon Goyal, Raymond J. Mooney, and Scott Niekum. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, and Yoshua Bengio. 2019 · 2019
Cited alongside, same era.
Value propagation networks
Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H. S. Torr, and Nicolas Usunier. 2019 · 2019
Cited alongside, same era.
When to use parametric models in reinforcement learning?
Hado van Hasselt, Matteo Hessel, and John Aslanides. 2019 · 2019
Cited alongside, same era.
Attention with sparsity regularization for neural machine translation and summarization
Jiajun Zhang, Yang Zhao, Haoran Li, and Chengqing Zong. 2019 · 2019
Cited alongside, same era.
Systematic generalization on gSCAN with language conditioned embedding
Tong Gao, Qi Huang, and Raymond J. Mooney. 2020 · 2020
Cited alongside, same era.
Top-KAST: Top-K always sparse training
Siddhant M. Jayakumar, Razvan Pascanu, Jack W. Rae, Simon Osindero, and Erich Elsen. 2020 · 2020
Cited alongside, same era.
Model based reinforcement learning for atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, and Henryk Michalewski. 2020 · 2020
Cited alongside, same era.
Grounding language to entities and dynamics for generalization in reinforcement learning
Austin W. Hanjie, Victor Zhong, and Karthik Narasimhan. 2021 · 2021
Later among the works it cites.
BC-Z: zero-shot task generalization with robotic imitation learning
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn. 2021 · 2021
Later among the works it cites.
A survey of generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel. 2021 · 2021
Later among the works it cites.
Compositional networks enable systematic generalization for grounded language understanding
Yen-Ling Kuo, Boris Katz, and Andrei Barbu. 2021 · 2021
Later among the works it cites.
Systematic generalization on gSCAN: What is nearly solved and what is next?
Linlu Qiu, Hexiang Hu, Bowen Zhang, Peter Shaw, and Fei Sha. 2021 · 2021
Later among the works it cites.
ALFWorld: Aligning text and embodied environments for interactive learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew J. Hausknecht. 2021 · 2021
Later among the works it cites.
Multi-task reinforcement learning with context-based representations
Shagun Sodhani, Amy Zhang, and Joelle Pineau. 2021 · 2021
Later among the works it cites.
Leveraging sparse linear layers for debuggable deep networks
Eric Wong, Shibani Santurkar, and Aleksander Madry. 2021 · 2021
Later among the works it cites.
A consciousness-inspired planning agent for model-based reinforcement learning
Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, and Yoshua Bengio. 2021 · 2021
Later among the works it cites.
Improving systematic generalization through modularity and augmentation
Laura Ruis and Brenden M. Lake. 2022 · 2022
Closest in time.
Generalisation in lifelong reinforcement learning through logical composition
Geraud Nangue Tasse, Steven James, and Benjamin Rosman. 2022 · 2022
Closest in time.