@inproceedings{proszewska2025gmcv,title={Bernoulli Priors as Efficient Denoising Guides for Diffusion Models},author={Proszewska, Magdalena and Malkin, Nikolay and Siddharth, N.},booktitle={{CVPR} Workshop on Generative Models for Computer Vision (GMCV)},year={2025},month=jun,}
@inproceedings{jian2024pragmatic,title={Are {LLM}s good pragmatic speakers?},author={Jian, Mingyue and Siddharth, N},booktitle={{NeurIPS} Workshop on Behavioural {ML}},year={2024},month=dec,}
DreamDecompiler: Improved Bayesian Program Learning by Decompiling Amortised Knowledge
Alessandro B. Palmarini, Christopher G. Lucas, and N. Siddharth
In International Conference on Machine Learning (ICML), Jul 2024
@inproceedings{palmarini2023dream,title={DreamDecompiler: Improved Bayesian Program Learning by Decompiling Amortised Knowledge},author={Palmarini, Alessandro B. and Lucas, Christopher G. and Siddharth, N.},booktitle={International Conference on Machine Learning (ICML)},month=jul,year={2024},}
Autoencoding Conditional Neural Processes for Representation Learning
Victor Prokhorov, Ivan Titov, and N. Siddharth
In International Conference on Machine Learning (ICML), Jul 2024
@inproceedings{prokhorov2024cnp,title={Autoencoding Conditional Neural Processes for Representation Learning},author={Prokhorov, Victor and Titov, Ivan and Siddharth, N.},booktitle={International Conference on Machine Learning (ICML)},month=jul,year={2024},}
Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding
Chuanhao Sun, Zhihang Yuan, Kai Xu, Luo Mai, and
3 more authors
In International Conference on Machine Learning (ICML), Jul 2024
@inproceedings{sun2024spe,title={Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding},author={Sun, Chuanhao and Yuan, Zhihang and Xu, Kai and Mai, Luo and N, Siddharth and Chen, Shuo and Marina, Mahesh},booktitle={International Conference on Machine Learning (ICML)},month=jul,year={2024},}
MLC Discourse
Multi-Label Classification for Implicit Discourse Relation Recognition
@inproceedings{long2024multi,title={Multi-Label Classification for Implicit Discourse Relation Recognition},author={Long, Wanqiu and Siddharth, N. and Webber, Bonnie},booktitle={ACL Findings},month=may,year={2024},}
DMLR
Graph Kernel Convolutions for Interpretable Classification
Magdalena Proszewska, and N. Siddharth
In ICLR Workshop on Data-Centric Machine Learning Research (DMLR), May 2024
@inproceedings{proszewska2024dmlr,title={Graph Kernel Convolutions for Interpretable Classification},author={Proszewska, Magdalena and Siddharth, N.},booktitle={{ICLR} Workshop on Data-Centric Machine Learning Research (DMLR)},year={2024},month=may,}
2023
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
Mattia Opper, Victor Prokhorov, and N. Siddharth
In Empirical Methods in Natural Language Processing (EMNLP), Dec 2023
@inproceedings{opper2023strae,title={StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure},author={Opper, Mattia and Prokhorov, Victor and Siddharth, N.},booktitle={Empirical Methods in Natural Language Processing (EMNLP)},month=dec,year={2023},}
BabyLM
On the Effect of Curriculum Learning with Developmental Data for Grammar Acquisition
Mattia Opper, James Morrison, and N. Siddharth
In EMNLP Workshop CoNLL-CMCL Shared Task BabyLM Challenge , Dec 2023
@inproceedings{opper2023babylm,title={On the Effect of Curriculum Learning with Developmental Data for Grammar Acquisition},author={Opper, Mattia and Morrison, James and Siddharth, N.},booktitle={{EMNLP} Workshop CoNLL-CMCL Shared Task BabyLM Challenge },year={2023},}
GazeML
FoVAE: Reconstructive Foveation as a Self-Supervised Variational Inference Task for Visual Representation Learning
Ivan Vegner, N Siddharth, and Leonidas A. A. Doumas
In NeuRIPS 2023 Workshop on Gaze Meets ML , Dec 2023
@inproceedings{wegner2023fovae,title={FoVAE: Reconstructive Foveation as a Self-Supervised Variational Inference Task for Visual Representation Learning},author={Vegner, Ivan and Siddharth, N and Doumas, Leonidas A. A.},booktitle={{NeuRIPS} 2023 Workshop on Gaze Meets ML },year={2023},}
2022
Drawing out of Distribution with Neuro-Symbolic Generative Models
Yichao Liang, Joshua B Tenenbaum, Tuan Anh Le, and N Siddharth
In Advances in Neural Information Processing Systems (NeurIPS), Dec 2022
@inproceedings{liang2022drawing,title={Drawing out of Distribution with Neuro-Symbolic Generative Models},author={Liang, Yichao and Tenenbaum, Joshua B and Le, Tuan Anh and Siddharth, N},booktitle={Advances in Neural Information Processing Systems (NeurIPS)},month=dec,year={2022},}
Adversarial Masking for Self-Supervised Learning
Yuge Shi, N. Siddharth, Philip Torr, and Adam R Kosiorek
In International Conference on Machine Learning (ICML), Jun 2022
@inproceedings{shi2022adversarial,title={Adversarial Masking for Self-Supervised Learning},author={Shi, Yuge and Siddharth, N. and Torr, Philip and Kosiorek, Adam R},booktitle={International Conference on Machine Learning (ICML)},month=jun,year={2022},}
On incorporating inductive biases into VAEs
Ning Miao, Emile Mathieu, N Siddharth, Yee Whye Teh, and
1 more author
In International Conference on Learning Representations (ICLR), May 2022
@inproceedings{mao2022incorporating,title={On incorporating inductive biases into VAEs},author={Miao, Ning and Mathieu, Emile and Siddharth, N and Teh, Yee Whye and Rainforth, Tom},booktitle={International Conference on Learning Representations (ICLR)},month=may,year={2022},}
Gradient Matching for Domain Generalization
Yuge Shi, Jeffrey Seely, Philip Torr, N. Siddharth, and
3 more authors
In International Conference on Learning Representations (ICLR), May 2022
@inproceedings{shi2022gradient,title={Gradient Matching for Domain Generalization},author={Shi, Yuge and Seely, Jeffrey and Torr, Philip and Siddharth, N. and Hannun, Awni and Usunier, Nicolas and Synnaeve, Gabriel},booktitle={International Conference on Learning Representations (ICLR)},month=may,year={2022},}
Learning multimodal VAEs through mutual supervision
Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, and
2 more authors
In International Conference on Learning Representations (ICLR), May 2022
@inproceedings{joy2022learning,title={Learning multimodal VAEs through mutual supervision},author={Joy, Tom and Shi, Yuge and Torr, Philip H.~S. and Rainforth, Tom and Schmon, Sebastian M and Siddharth, N.},booktitle={International Conference on Learning Representations (ICLR)},month=may,year={2022},}
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
Tuan Anh Le, Katherine M Collins, Luke Hewitt, Kevin Ellis, and
3 more authors
In International Conference on Learning Representations (ICLR), May 2022
@inproceedings{le2022hybrid,title={Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface},author={Le, Tuan Anh and Collins, Katherine M and Hewitt, Luke and Ellis, Kevin and Siddharth, N. and Gershman, Samuel J and Tenenbaum, Joshua B},booktitle={International Conference on Learning Representations (ICLR)},month=may,year={2022},}
UM-IoS
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
Mattia Opper, Victor Prokhorov, and N. Siddharth
In EMNLP Workshop on Unimodal & Multimodal Induction of Linguistic Structures, May 2022
@inproceedings{opper2022straeios,title={StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure},author={Opper, Mattia and Prokhorov, Victor and Siddharth, N.},booktitle={{EMNLP} Workshop on Unimodal \& Multimodal Induction of Linguistic Structures},year={2022},}
2020
UDL
Learning Generative Models from Classifier Uncertainties
N. Siddharth, and Brooks Paige
In ICML Workshop on Uncertainty & Robustness in Deep Learning, May 2020
@inproceedings{siddharth2020learning,title={Learning Generative Models from Classifier Uncertainties},author={Siddharth, N. and Paige, Brooks},booktitle={{ICML} Workshop on Uncertainty \& Robustness in Deep Learning},year={2020},}
