Learning physically-instantiated game play through visual observation. - Andrei Barbu; Siddharth Narayanaswamy; Jeffrey Mark Siskind (ICRA, 2010)

A visual language model for estimating object pose and structure in a generative visual domain. - Siddharth Narayanaswamy; Andrei Barbu; Jeffrey Mark Siskind (ICRA, 2011)

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; et al. (UAI, 2012)

Simultaneous Object Detection, Tracking, and Event Recognition - Andrei Barbu; Aaron Michaux; Siddharth Narayanaswamy; Jeffrey Mark Siskind (CoRR, 2012)

Seeing Unseeability to See the Unseeable - Siddharth Narayanaswamy; Andrei Barbu; Jeffrey Mark Siskind (CoRR, 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 002; et al. (CoRR, 2012)

Recognize Human Activities from Partially Observed Videos. - Yu Cao 003; Daniel Paul Barrett; Andrei Barbu; Siddharth Narayanaswamy; Haonan Yu; Aaron Michaux; Yuewei Lin; Sven J. Dickinson; Jeffrey Mark Siskind; Song Wang 002 (CVPR, 2013)

Saying What You're Looking For: Linguistics Meets Video Search. - Andrei Barbu; Siddharth Narayanaswamy; Jeffrey Mark Siskind (CoRR, 2013)

Seeing What You're Told: Sentence-Guided Activity Recognition in Video. - N. Siddharth 001; Andrei Barbu; Jeffrey Mark Siskind (CVPR, 2014)

Seeing is Worse than Believing: Reading People's Minds Better than Computer-Vision Methods Recognize Actions. - Andrei Barbu; Daniel Paul Barrett; Wei Chen 134; Siddharth Narayanaswamy; Caiming Xiong; Jason J. Corso; Christiane D. Fellbaum; Catherine Hanson; Stephen José Hanson; Sébastien Hélie; Evguenia Malaia; Barak A. Pearlmutter; et al. (ECCV (5), 2014)

Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs. - Andreas Stuhlmüller; Robert X. D. Hawkins; N. Siddharth 001; Noah D. Goodman (CoRR, 2015)

A Compositional Framework for Grounding Language Inference, Generation, and Acquisition in Video. - Haonan Yu; N. Siddharth 001; Andrei Barbu; Jeffrey Mark Siskind (J. Artif. Intell. Res., 2015)

Playing Doom with SLAM-Augmented Deep Reinforcement Learning. - Shehroze Bhatti; Alban Desmaison; Ondrej Miksik; Nantas Nardelli; N. Siddharth 001; Philip H. S. Torr (CoRR, 2016)

Inducing Interpretable Representations with Variational Autoencoders. - N. Siddharth 001; Brooks Paige; Alban Desmaison; Jan-Willem van de Meent; Frank D. Wood; Noah D. Goodman; Pushmeet Kohli; Philip H. S. Torr (CoRR, 2016)

Saying What You're Looking For: Linguistics Meets Video Search. - Daniel Paul Barrett; Andrei Barbu; N. Siddharth 001; Jeffrey Mark Siskind (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

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 (NIPS, 2017)

Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. - Stefan Webb; Adam Golinski; Robert Zinkov; N. Siddharth 001; Yee Whye Teh; Frank D. Wood (CoRR, 2017)

FlipDial: A Generative Model for Two-Way Visual Dialogue. - Daniela Massiceti; N. Siddharth 001; Puneet Kumar Dokania; Philip H. S. Torr (CVPR, 2018)

A Semi-supervised Deep Generative Model for Human Body Analysis. - Rodrigo Andrade de Bem; Arnab Ghosh; Thalaiyasingam Ajanthan; Ondrej Miksik; N. Siddharth 001; Philip H. S. Torr (ECCV Workshops (2), 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 (NeurIPS, 2018)

Hierarchical Disentangled Representations. - Babak Esmaeili 001; Hao Wu 020; Sarthak Jain; N. Siddharth 001; Brooks Paige; Jan-Willem van de Meent (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 001; Philip H. S. Torr (CoRR, 2018)

Revisiting Reweighted Wake-Sleep. - Tuan Anh Le 001; Adam R. Kosiorek; N. Siddharth 001; Yee Whye Teh; Frank Wood (CoRR, 2018)

Disentangling Disentanglement. - Emile Mathieu; Tom Rainforth; Siddharth Narayanaswamy; Yee Whye Teh (CoRR, 2018)

Visual Dialogue without Vision or Dialogue. - Daniela Massiceti; Puneet K. Dokania; N. Siddharth 001; Philip H. S. Torr (CoRR, 2018)

Structured Disentangled Representations. - Babak Esmaeili 001; Hao Wu 020; Sarthak Jain; Alican Bozkurt; N. Siddharth 001; Brooks Paige; Dana H. Brooks; Jennifer G. Dy; Jan-Willem van de Meent (AISTATS, 2019)

Disentangling Disentanglement in Variational Autoencoders. - Emile Mathieu; Tom Rainforth; N. Siddharth 001; Yee Whye Teh (ICML, 2019)

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. - Yuge Shi; Siddharth Narayanaswamy; Brooks Paige; Philip H. S. Torr (NeurIPS, 2019)

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. - Tuan Anh Le 001; Adam R. Kosiorek; N. Siddharth 001; Yee Whye Teh; Frank Wood (UAI, 2019)

A Conditional Deep Generative Model of People in Natural Images. - Rodrigo Andrade de Bem; Arnab Ghosh; Adnane Boukhayma; Thalaiyasingam Ajanthan; N. Siddharth 001; Philip H. S. Torr (WACV, 2019)

Multitask Soft Option Learning. - Maximilian Igl; Andrew Gambardella; Nantas Nardelli; N. Siddharth 001; Wendelin Böhmer; Shimon Whiteson (CoRR, 2019)

Lessons from reinforcement learning for biological representations of space. - Alexander Muryy; N. Siddharth 001; Nantas Nardelli; Andrew Glennerster; Philip H. S. Torr (CoRR, 2019)

Multitask Soft Option Learning. - Maximilian Igl; Andrew Gambardella; Jinke He; Nantas Nardelli; N. Siddharth 001; Wendelin Boehmer; Shimon Whiteson (UAI, 2020)

A Revised Generative Evaluation of Visual Dialogue. - Daniela Massiceti; Viveka Kulharia; Puneet K. Dokania; N. Siddharth 001; Philip H. S. Torr (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 001; Ana Belen Espinosa-Gonzalez; Ara Darzi; Philip H. S. Torr; Atilim Günes Baydin (CoRR, 2020)

Rethinking Semi-Supervised Learning in VAEs. - Tom Joy; Sebastian M. Schmon; Philip H. S. Torr; N. Siddharth 001; Tom Rainforth (CoRR, 2020)

DGPose: Deep Generative Models for Human Body Analysis. - Rodrigo Andrade de Bem; Arnab Ghosh; Thalaiyasingam Ajanthan; Ondrej Miksik; Adnane Boukhayma; N. Siddharth 001; Philip H. S. Torr (Int. J. Comput. Vis., 2020)

Capturing Label Characteristics in VAEs. - Tom Joy; Sebastian M. Schmon; Philip H. S. Torr; Siddharth Narayanaswamy; Tom Rainforth (ICLR, 2021)

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. - Yuge Shi; Brooks Paige; Philip H. S. Torr; N. Siddharth 001 (ICLR, 2021)

InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. - Ning Miao; Emile Mathieu; N. Siddharth 001; Yee Whye Teh; Tom Rainforth (CoRR, 2021)

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface. - Tuan Anh Le 001; Katherine M. Collins; Luke Hewitt; Kevin Ellis; Siddharth Narayanaswamy; Samuel Gershman; Joshua B. Tenenbaum (ICLR, 2022)

Learning Multimodal VAEs through Mutual Supervision. - Tom Joy; Yuge Shi; Philip H. S. Torr; Tom Rainforth; Sebastian M. Schmon; Siddharth Narayanaswamy (ICLR, 2022)

Gradient Matching for Domain Generalization. - Yuge Shi; Jeffrey Seely; Philip H. S. Torr; Siddharth Narayanaswamy; Awni Y. Hannun; Nicolas Usunier; Gabriel Synnaeve (ICLR, 2022)

Adversarial Masking for Self-Supervised Learning. - Yuge Shi; N. Siddharth 001; Philip H. S. Torr; Adam R. Kosiorek (ICML, 2022)

Drawing out of Distribution with Neuro-Symbolic Generative Models. - Yichao Liang; Josh Tenenbaum 001; Tuan Anh Le 001; N. Siddharth 001 (NeurIPS, 2022)

StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure. - Mattia Opper; Victor Prokhorov; Siddharth Narayanaswamy (EMNLP, 2023)

DreamDecompiler: Improved Bayesian Program Learning by Decompiling Amortised Knowledge. - Alessandro B. Palmarini; Christopher G. Lucas; N. Siddharth 001 (CoRR, 2023)

On the effect of curriculum learning with developmental data for grammar acquisition. - Mattia Opper; J. Morrison; N. Siddharth 001 (CoRR, 2023)

Multi-Label Classification for Implicit Discourse Relation Recognition. - Wanqiu Long; Siddharth Narayanaswamy; Bonnie Webber (ACL (Findings), 2024)

Bayesian Program Learning by Decompiling Amortized Knowledge. - Alessandro B. Palmarini; Christopher G. Lucas; N. Siddharth 001 (ICML, 2024)

Autoencoding Conditional Neural Processes for Representation Learning. - Victor Prokhorov; Ivan Titov 001; N. Siddharth 001 (ICML, 2024)

Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding. - Chuanhao Sun; Zhihang Yuan; Kai Xu 014; Luo Mai; N. Siddharth 001; Shuo Chen; Mahesh K. Marina (ICML, 2024)

Self-StrAE at SemEval-2024 Task 1: Making Self-Structuring AutoEncoders Learn More With Less. - Mattia Opper; Siddharth Narayanaswamy (SemEval@NAACL, 2024)

Banyan: Improved Representation Learning with Explicit Structure. - Mattia Opper; N. Siddharth 001 (CoRR, 2024)

Are LLMs good pragmatic speakers? - Mingyue Jian; Siddharth Narayanaswamy (CoRR, 2024)

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning. - Magdalena Proszewska; Nikolay Malkin; N. Siddharth 001 (CoRR, 2025)