N. Siddharth

According to our database, N. Siddharth authored at least 55 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Are LLMs good pragmatic speakers?

Mingyue Jian, Siddharth Narayanaswamy

CoRR, 2024

Banyan: Improved Representation Learning with Explicit Structure.

Mattia Opper, N. Siddharth

CoRR, 2024

Self-StrAE at SemEval-2024 Task 1: Making Self-Structuring AutoEncoders Learn More With Less.

Mattia Opper, Siddharth Narayanaswamy

Proceedings of the 18th International Workshop on Semantic Evaluation, 2024

Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding.

Chuanhao Sun, Zhihang Yuan, Kai Xu, Luo Mai, N. Siddharth, Shuo Chen, Mahesh K. Marina

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Autoencoding Conditional Neural Processes for Representation Learning.

Victor Prokhorov, Ivan Titov, N. Siddharth

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Bayesian Program Learning by Decompiling Amortized Knowledge.

Alessandro B. Palmarini, Christopher G. Lucas, N. Siddharth

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Multi-Label Classification for Implicit Discourse Relation Recognition.

Wanqiu Long, Siddharth Narayanaswamy, Bonnie Webber

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

On the effect of curriculum learning with developmental data for grammar acquisition.

Mattia Opper, J. Morrison, N. Siddharth

CoRR, 2023

DreamDecompiler: Improved Bayesian Program Learning by Decompiling Amortised Knowledge.

Alessandro B. Palmarini, Christopher G. Lucas, N. Siddharth

CoRR, 2023

StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure.

Mattia Opper, Victor Prokhorov, Siddharth Narayanaswamy

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

Drawing out of Distribution with Neuro-Symbolic Generative Models.

Yichao Liang, Josh Tenenbaum, Tuan Anh Le, N. Siddharth

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adversarial Masking for Self-Supervised Learning.

Yuge Shi, N. Siddharth, Philip H. S. Torr, Adam R. Kosiorek

Proceedings of the International Conference on Machine Learning, 2022

Gradient Matching for Domain Generalization.

Yuge Shi, Jeffrey Seely, Philip H. S. Torr, Siddharth Narayanaswamy, Awni Y. Hannun, Nicolas Usunier, Gabriel Synnaeve

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Multimodal VAEs through Mutual Supervision.

Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, Siddharth Narayanaswamy

Proceedings of the Tenth International Conference on Learning Representations, 2022

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface.

Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, Siddharth Narayanaswamy, Samuel Gershman, Joshua B. Tenenbaum

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents.

Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth

CoRR, 2021

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models.

Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth

Proceedings of the 9th International Conference on Learning Representations, 2021

Capturing Label Characteristics in VAEs.

Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, Siddharth Narayanaswamy, Tom Rainforth

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

DGPose: Deep Generative Models for Human Body Analysis.

Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip H. S. Torr

Int. J. Comput. Vis., 2020

Rethinking Semi-Supervised Learning in VAEs.

Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth

CoRR, 2020

Simulation-Based Inference for Global Health Decisions.

Christian Schröder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Robert Zinkov, Puneet K. Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip H. S. Torr, Atilim Günes Baydin

CoRR, 2020

A Revised Generative Evaluation of Visual Dialogue.

Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr

CoRR, 2020

Multitask Soft Option Learning.

Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019

Lessons from reinforcement learning for biological representations of space.

Alexander Muryy, N. Siddharth, Nantas Nardelli, Andrew Glennerster, Philip H. S. Torr

CoRR, 2019

Multitask Soft Option Learning.

Maximilian Igl, Andrew Gambardella, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson

CoRR, 2019

A Conditional Deep Generative Model of People in Natural Images.

Rodrigo Andrade de Bem, Arnab Ghosh, Adnane Boukhayma, Thalaiyasingam Ajanthan, N. Siddharth, Philip H. S. Torr

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2019

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow.

Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models.

Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Disentangling Disentanglement in Variational Autoencoders.

Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh

Proceedings of the 36th International Conference on Machine Learning, 2019

Structured Disentangled Representations.

Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Visual Dialogue without Vision or Dialogue.

Daniela Massiceti, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr

CoRR, 2018

Disentangling Disentanglement.

Emile Mathieu, Tom Rainforth, Siddharth Narayanaswamy, Yee Whye Teh

CoRR, 2018

Revisiting Reweighted Wake-Sleep.

Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

CoRR, 2018

DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis.

Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, N. Siddharth, Philip H. S. Torr

CoRR, 2018

Hierarchical Disentangled Representations.

Babak Esmaeili, Hao Wu, Sarthak Jain, N. Siddharth, Brooks Paige, Jan-Willem van de Meent

CoRR, 2018

Faithful Inversion of Generative Models for Effective Amortized Inference.

Stefan Webb, Adam Golinski, Robert Zinkov, Siddharth Narayanaswamy, Tom Rainforth, Yee Whye Teh, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Semi-supervised Deep Generative Model for Human Body Analysis.

Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, N. Siddharth, Philip H. S. Torr

Proceedings of the Computer Vision - ECCV 2018 Workshops, 2018

FlipDial: A Generative Model for Two-Way Visual Dialogue.

Daniela Massiceti, N. Siddharth, Puneet Kumar Dokania, Philip H. S. Torr

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference.

Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, Frank D. Wood

CoRR, 2017

Learning Disentangled Representations with Semi-Supervised Deep Generative Models.

Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Saying What You're Looking For: Linguistics Meets Video Search.

Daniel Paul Barrett, Andrei Barbu, N. Siddharth, Jeffrey Mark Siskind

IEEE Trans. Pattern Anal. Mach. Intell., 2016

Inducing Interpretable Representations with Variational Autoencoders.

N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr

CoRR, 2016

Playing Doom with SLAM-Augmented Deep Reinforcement Learning.

Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N. Siddharth, Philip H. S. Torr

CoRR, 2016

2015

A Compositional Framework for Grounding Language Inference, Generation, and Acquisition in Video.

Haonan Yu, N. Siddharth, Andrei Barbu, Jeffrey Mark Siskind

J. Artif. Intell. Res., 2015

Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs.

Andreas Stuhlmüller, Robert X. D. Hawkins, N. Siddharth, Noah D. Goodman

CoRR, 2015

2014

Seeing is Worse than Believing: Reading People's Minds Better than Computer-Vision Methods Recognize Actions.

Andrei Barbu, Daniel Paul Barrett, Wei Chen, Siddharth Narayanaswamy, Caiming Xiong, Jason J. Corso, Christiane D. Fellbaum, Catherine Hanson, Stephen José Hanson, Sébastien Hélie, Evguenia Malaia, Barak A. Pearlmutter, Jeffrey Mark Siskind, Thomas Michael Talavage, Ronnie B. Wilbur

Proceedings of the Computer Vision - ECCV 2014, 2014

Seeing What You're Told: Sentence-Guided Activity Recognition in Video.

N. Siddharth, Andrei Barbu, Jeffrey Mark Siskind

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013

Saying What You're Looking For: Linguistics Meets Video Search.

Andrei Barbu, Siddharth Narayanaswamy, Jeffrey Mark Siskind

CoRR, 2013

Recognize Human Activities from Partially Observed Videos.

Yu Cao, Daniel Paul Barrett, Andrei Barbu, Siddharth Narayanaswamy, Haonan Yu, Aaron Michaux, Yuewei Lin, Sven J. Dickinson, Jeffrey Mark Siskind, Song Wang

Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012

Large-Scale Automatic Labeling of Video Events with Verbs Based on Event-Participant Interaction

Andrei Barbu, Alexander Bridge, Dan Coroian, Sven J. Dickinson, Sam Mussman, Siddharth Narayanaswamy, Dhaval Salvi, Lara Schmidt, Jiangnan Shangguan, Jeffrey Mark Siskind, Jarrell W. Waggoner, Song Wang, Jinlian Wei, Yifan Yin, Zhiqi Zhang

CoRR, 2012

Seeing Unseeability to See the Unseeable

Siddharth Narayanaswamy, Andrei Barbu, Jeffrey Mark Siskind

CoRR, 2012

Simultaneous Object Detection, Tracking, and Event Recognition

Andrei Barbu, Aaron Michaux, Siddharth Narayanaswamy, Jeffrey Mark Siskind

CoRR, 2012

Video In Sentences Out.

Andrei Barbu, Alexander Bridge, Zachary Burchill, Dan Coroian, Sven J. Dickinson, Sanja Fidler, Aaron Michaux, Sam Mussman, Siddharth Narayanaswamy, Dhaval Salvi, Lara Schmidt, Jiangnan Shangguan, Jeffrey Mark Siskind, Jarrell W. Waggoner, Song Wang, Jinlian Wei, Yifan Yin, Zhiqi Zhang

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2011

A visual language model for estimating object pose and structure in a generative visual domain.

Siddharth Narayanaswamy, Andrei Barbu, Jeffrey Mark Siskind

Proceedings of the IEEE International Conference on Robotics and Automation, 2011

2010

Learning physically-instantiated game play through visual observation.

Andrei Barbu, Siddharth Narayanaswamy, Jeffrey Mark Siskind

Proceedings of the IEEE International Conference on Robotics and Automation, 2010