N. Siddharth

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 43

Top Topics

Sequential Monte Carlo

Supervised Learning

Top Venues

Publications

Wanqiu Long, N. Siddharth, Bonnie Webber
Multi-Label Classification for Implicit Discourse Relation Recognition. CoRR (2024)
Mattia Opper, N. Siddharth
Banyan: Improved Representation Learning with Explicit Structure. CoRR (2024)
Mattia Opper, N. Siddharth
Self-StrAE at SemEval-2024 Task 1: Making Self-Structuring AutoEncoders Learn More With Less. CoRR (2024)
Mattia Opper, Victor Prokhorov, N. Siddharth
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure. CoRR (2023)
Mattia Opper, J. Morrison, N. Siddharth
On the effect of curriculum learning with developmental data for grammar acquisition. CoRR (2023)
Alessandro B. Palmarini, Christopher G. Lucas, N. Siddharth
DreamDecompiler: Improved Bayesian Program Learning by Decompiling Amortised Knowledge. CoRR (2023)
Victor Prokhorov, Ivan Titov, N. Siddharth
Autoencoding Conditional Neural Processes for Representation Learning. CoRR (2023)
Yichao Liang, Josh Tenenbaum, Tuan Anh Le, N. Siddharth
Drawing out of Distribution with Neuro-Symbolic Generative Models. NeurIPS (2022)
Yuge Shi, N. Siddharth, Philip H. S. Torr, Adam R. Kosiorek
Adversarial Masking for Self-Supervised Learning. ICML (2022)
Yuge Shi, N. Siddharth, Philip H. S. Torr, Adam R. Kosiorek
Adversarial Masking for Self-Supervised Learning. CoRR (2022)
Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le, N. Siddharth
Drawing out of Distribution with Neuro-Symbolic Generative Models. CoRR (2022)
Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, N. Siddharth, Samuel J. Gershman, Joshua B. Tenenbaum
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface. CoRR (2021)
Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. ICLR (2021)
Yuge Shi, Jeffrey Seely, Philip H. S. Torr, N. Siddharth, Awni Hannun, Nicolas Usunier, Gabriel Synnaeve
Gradient Matching for Domain Generalization. CoRR (2021)
Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth
InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. CoRR (2021)
Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, N. Siddharth
Learning Multimodal VAEs through Mutual Supervision. CoRR (2021)
Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Boehmer, Shimon Whiteson
Multitask Soft Option Learning. UAI (2020)
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
Simulation-Based Inference for Global Health Decisions. CoRR (2020)
Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip H. S. Torr
DGPose: Deep Generative Models for Human Body Analysis. Int. J. Comput. Vis. 128 (5) (2020)
Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr
A Revised Generative Evaluation of Visual Dialogue. CoRR (2020)
Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. CoRR (2020)
Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth
Rethinking Semi-Supervised Learning in VAEs. CoRR (2020)
Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer G. Dy, Jan-Willem van de Meent
Structured Disentangled Representations. AISTATS (2019)
Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. CoRR (2019)
Alexander Muryy, N. Siddharth, Nantas Nardelli, Andrew Glennerster, Philip H. S. Torr
Lessons from reinforcement learning for biological representations of space. CoRR (2019)
Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow. UAI (2019)
Maximilian Igl, Andrew Gambardella, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson
Multitask Soft Option Learning. CoRR (2019)
Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh
Disentangling Disentanglement in Variational Autoencoders. ICML (2019)
Rodrigo Andrade de Bem, Arnab Ghosh, Adnane Boukhayma, Thalaiyasingam Ajanthan, N. Siddharth, Philip H. S. Torr
A Conditional Deep Generative Model of People in Natural Images. WACV (2019)
Babak Esmaeili, Hao Wu, Sarthak Jain, N. Siddharth, Brooks Paige, Jan-Willem van de Meent
Hierarchical Disentangled Representations. CoRR (2018)
Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
Revisiting Reweighted Wake-Sleep. CoRR (2018)
Daniela Massiceti, N. Siddharth, Puneet Kumar Dokania, Philip H. S. Torr
FlipDial: A Generative Model for Two-Way Visual Dialogue. CVPR (2018)
Daniela Massiceti, N. Siddharth, Puneet Kumar Dokania, Philip H. S. Torr
FlipDial: A Generative Model for Two-Way Visual Dialogue. CoRR (2018)
Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, N. Siddharth, Philip H. S. Torr
DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis. CoRR (2018)
Daniela Massiceti, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr
Visual Dialogue without Vision or Dialogue. CoRR (2018)
Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, N. Siddharth, Philip H. S. Torr
A Semi-supervised Deep Generative Model for Human Body Analysis. ECCV Workshops (2) (2018)
N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. CoRR (2017)
Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, Frank D. Wood
Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. CoRR (2017)
Daniel Paul Barrett, Andrei Barbu, N. Siddharth, Jeffrey Mark Siskind
Saying What You're Looking For: Linguistics Meets Video Search. IEEE Trans. Pattern Anal. Mach. Intell. 38 (10) (2016)
N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
Inducing Interpretable Representations with Variational Autoencoders. CoRR (2016)
Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N. Siddharth, Philip H. S. Torr
Playing Doom with SLAM-Augmented Deep Reinforcement Learning. CoRR (2016)
Haonan Yu, N. Siddharth, Andrei Barbu, Jeffrey Mark Siskind
A Compositional Framework for Grounding Language Inference, Generation, and Acquisition in Video. J. Artif. Intell. Res. 52 (2015)
Andreas Stuhlmüller, Robert X. D. Hawkins, N. Siddharth, Noah D. Goodman
Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs. CoRR (2015)
N. Siddharth, Andrei Barbu, Jeffrey Mark Siskind
Seeing What You're Told: Sentence-Guided Activity Recognition in Video. CVPR (2014)