Fetching the paper…
Reading the bibliography…
We present a framework for the unsupervised learning of neurosymbolic encoders, which are encoders obtained by composing neural networks with symbolic programs from a domain-specific language.
Objective criteria for the evaluation of clustering methods
William M Rand · 1971
Earlier work this paper cites.
Least squares quantization in pcm
Stuart Lloyd · 1982
Earlier work this paper cites.
A survey of very large-scale neighborhood search techniques
Ravindra K Ahuja, Özlem Ergun, James B Orlin, and Abraham P Punnen · 2002
Earlier work this paper cites.
Unsupervised feature extraction for time series clustering using orthogonal wavelet transform
Hui Zhang, Tu Bao Ho, Yang Zhang, and M-S Lin · 2006
Earlier work this paper cites.
Introduction to information retrieval , volume 39
Hinrich Schütze, Christopher D Manning, and Prabhakar Raghavan · 2008
Earlier work this paper cites.
Automating string processing in spreadsheets using input-output examples
Sumit Gulwani · 2011
Earlier work this paper cites.
Social behavior recognition in continuous video
Xavier P Burgos-Artizzu, Piotr Dollár, Dayu Lin, David J Anderson, and Pietro Perona · 2012
Earlier work this paper cites.
Toward a science of computational ethology
David J Anderson and Pietro Perona · 2014
Earlier work this paper cites.
Mapping the stereotyped behaviour of freely moving fruit flies
Gordon J Berman, Daniel M Choi, William Bialek, and Joshua W Shaevitz · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
Diederik P Kingma, Danilo J Rezende, Shakir Mohamed, and Max Welling · 2014
Earlier work this paper cites.
Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor · 2014
Earlier work this paper cites.
Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio · 2015
Earlier work this paper cites.
Mapping sub-second structure in mouse behavior
Alexander B Wiltschko, Matthew J Johnson, Giuliano Iurilli, Ralph E Peterson, Jesse M Katon, Stan L Pashkovski, Victoria E Abraira, Ryan P Adams, and Sandeep Robert Datta · 2015
Earlier work this paper cites.
Adaptive neural compilation
Rudy R Bunel, Alban Desmaison, Pawan K Mudigonda, Pushmeet Kohli, and Philip Torr · 2016
Earlier work this paper cites.
Learning recurrent representations for hierarchical behavior modeling
Eyrun Eyjolfsdottir, Kristin Branson, Yisong Yue, and Pietro Perona · 2016
Earlier work this paper cites.
Terpret: A probabilistic programming language for program induction
Alexander L Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, and Daniel Tarlow · 2016
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
Earlier work this paper cites.
Composing graphical models with neural networks for structured representations and fast inference
Matthew J Johnson, David Duvenaud, Alexander B Wiltschko, Sandeep R Datta, and Ryan P Adams · 2016
Earlier work this paper cites.
Discrete variational autoencoders
Jason Tyler Rolfe · 2016
Earlier work this paper cites.
Understanding disentangling in β \beta -vae
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2017
Cited alongside, same era.
Adversarial information factorization
Antonia Creswell, Yumnah Mohamied, Biswa Sengupta, and Anil A Bharath · 2017
Cited alongside, same era.
Factorized variational autoencoders for modeling audience reactions to movies
Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, and Greg Mori · 2017
Cited alongside, same era.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing · 2017
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
Cited alongside, same era.
Learning representations for time series clustering
Qianli Ma, Jiawei Zheng, Sen Li, and Gary W Cottrell · 2019
Later among the works it cites.
Imitation-projected programmatic reinforcement learning
Abhinav Verma, Hoang M Le, Yisong Yue, and Swarat Chaudhuri · 2019
Later among the works it cites.
Guided variational autoencoder for disentanglement learning
Zheng Ding, Yifan Xu, Weijian Xu, Gaurav Parmar, Yang Yang, Max Welling, and Zhuowen Tu · 2020
Later among the works it cites.
Learning task-general representations with generative neuro-symbolic modeling
Reuben Feinman and Brenden M Lake · 2020
Later among the works it cites.
Vectornet: Encoding hd maps and agent dynamics from vectorized representation
Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, and Cordelia Schmid · 2020
Later among the works it cites.
B-soid: An open source unsupervised algorithm for discovery of spontaneous behaviors
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
Program synthesis using abstraction refinement
Xinyu Wang, Isil Dillig, and Rishabh Singh · 2017
Cited alongside, same era.
Learning hierarchical features from generative models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
Cited alongside, same era.
Verifiable reinforcement learning via policy extraction
Osbert Bastani, Yewen Pu, and Armando Solar-Lezama · 2018
Cited alongside, same era.
Towards synthesizing complex programs from input-output examples
Xinyun Chen, Chang Liu, and Dawn Song · 2018
Cited alongside, same era.
Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings
John Co-Reyes, YuXuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Learning disentangled joint continuous and discrete representations
Emilien Dupont · 2018
Cited alongside, same era.
Alexander I Hsu and Eric A Yttri · 2020
Later among the works it cites.
Synthesizing programmatic policies that inductively generalize
Jeevana Priya Inala, Osbert Bastani, Zenna Tavares, and Armando Solar-Lezama · 2020
Later among the works it cites.
Learning lane graph representations for motion forecasting
Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, and Raquel Urtasun · 2020
Later among the works it cites.
Identifying behavioral structure from deep variational embeddings of animal motion
Kevin Luxem, Falko Fuhrmann, Johannes Kürsch, Stefan Remy, and Pavol Bauer · 2020
Later among the works it cites.
Disentangled self-supervision in sequential recommenders
Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, and Wenwu Zhu · 2020
Later among the works it cites.
The mouse action recognition system (mars): a software pipeline for automated analysis of social behaviors in mice
Cristina Segalin, Jalani Williams, Tomomi Karigo, May Hui, Moriel Zelikowsky, Jennifer J Sun, Pietro Perona, David J Anderson, and Ann Kennedy · 2020
Later among the works it cites.
Learning differentiable programs with admissible neural heuristics
Ameesh Shah, Eric Zhan, Jennifer J Sun, Abhinav Verma, Yisong Yue, and Swarat Chaudhuri · 2020
Later among the works it cites.
Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
Later among the works it cites.
Learning calibratable policies using programmatic style-consistency
Eric Zhan, Albert Tseng, Yisong Yue, Adith Swaminathan, and Matthew Hausknecht · 2020
Later among the works it cites.
A survey on neural network interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis, and Ke Tang · 2020
Later among the works it cites.
Neurosymbolic programming
Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue, et al · 2021
Closest in time.
Differentiable synthesis of program architectures
Guofeng Cui and He Zhu · 2021
Closest in time.
Mining naturalistic human behaviors in long-term video and neural recordings
Satpreet H Singh, Steven M Peterson, Rajesh PN Rao, and Bingni W Brunton · 2021
Closest in time.
Learning to synthesize programs as interpretable and generalizable policies
Dweep Trivedi, Jesse Zhang, Shao-Hua Sun, and Joseph J Lim · 2021
Closest in time.
Neurosymbolic programming for science
Jennifer J Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando Solar-Lezama, Swarat Chaudhuri, Yisong Yue, and Omar Costilla-Reyes · 2022
Closest in time.