no code implementations • 2 Apr 2024 • Mattia Opper, N. Siddharth
This paper presents two simple improvements to the Self-Structuring AutoEncoder (Self-StrAE).
no code implementations • 31 Oct 2023 • Mattia Opper, J. Morrison, N. Siddharth
Using BabyBERTa as a probe, we find that grammar acquisition is largely driven by exposure to speech data, and in particular through exposure to two of the BabyLM training corpora: AO-Childes and Open Subtitles.
no code implementations • 13 Jun 2023 • Alessandro B. Palmarini, Christopher G. Lucas, N. Siddharth
The cost of search is amortised by training a neural search policy, reducing search breadth and effectively "compiling" useful information to compose program solutions across tasks.
1 code implementation • 29 May 2023 • Victor Prokhorov, Ivan Titov, N. Siddharth
Conditional neural processes (CNPs) are a flexible and efficient family of models that learn to learn a stochastic process from data.
no code implementations • 9 May 2023 • Mattia Opper, Victor Prokhorov, N. Siddharth
This work presents StrAE: a Structured Autoencoder framework that through strict adherence to explicit structure, and use of a novel contrastive objective over tree-structured representations, enables effective learning of multi-level representations.
no code implementations • 3 Jun 2022 • Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le, N. Siddharth
We then adopt a subset of the Omniglot challenge tasks, and evaluate its ability to generate new exemplars (both unconditionally and conditionally), and perform one-shot classification, showing that DooD matches the state of the art.
1 code implementation • 31 Jan 2022 • Yuge Shi, N. Siddharth, Philip H. S. Torr, Adam R. Kosiorek
We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial objective.
no code implementations • ICLR 2022 • Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, N. Siddharth, Samuel J. Gershman, Joshua B. Tenenbaum
We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization.
1 code implementation • ICLR 2022 • Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth
InteL-VAEs use an intermediary set of latent variables to control the stochasticity of the encoding process, before mapping these in turn to the latent representation using a parametric function that encapsulates our desired inductive bias(es).
1 code implementation • ICLR 2022 • Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, N. Siddharth
Here we introduce a novel alternative, the MEME, that avoids such explicit combinations by repurposing semi-supervised VAEs to combine information between modalities implicitly through mutual supervision.
2 code implementations • ICLR 2022 • Yuge Shi, Jeffrey Seely, Philip H. S. Torr, N. Siddharth, Awni Hannun, Nicolas Usunier, Gabriel Synnaeve
We perform experiments on both the Wilds benchmark, which captures distribution shift in the real world, as well as datasets in DomainBed benchmark that focuses more on synthetic-to-real transfer.
no code implementations • ICLR 2021 • Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth
Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language.
2 code implementations • ICLR 2021 • Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth
We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels.
2 code implementations • 14 May 2020 • Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, Atılım Güneş Baydin
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.
1 code implementation • 20 Apr 2020 • Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr
Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge.
3 code implementations • NeurIPS 2019 • Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr
In this work, we characterise successful learning of such models as the fulfillment of four criteria: i) implicit latent decomposition into shared and private subspaces, ii) coherent joint generation over all modalities, iii) coherent cross-generation across individual modalities, and iv) improved model learning for individual modalities through multi-modal integration.
no code implementations • ICLR 2019 • Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn.
1 code implementation • 1 Apr 2019 • Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference.
2 code implementations • 16 Dec 2018 • Daniela Massiceti, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr
We characterise some of the quirks and shortcomings in the exploration of Visual Dialogue - a sequential question-answering task where the questions and corresponding answers are related through given visual stimuli.
1 code implementation • 6 Dec 2018 • Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh
We develop a generalisation of disentanglement in VAEs---decomposition of the latent representation---characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior.
1 code implementation • ICLR 2019 • Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood
Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables.
no code implementations • 17 Apr 2018 • Rodrigo de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip Torr
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
no code implementations • 6 Apr 2018 • Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem van de Meent
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner.
no code implementations • CVPR 2018 • Daniela Massiceti, N. Siddharth, Puneet K. Dokania, Philip H. S. Torr
We are the first to extend this paradigm to full two-way visual dialogue, where our model is capable of generating both questions and answers in sequence based on a visual input, for which we propose a set of novel evaluation measures and metrics.
no code implementations • NeurIPS 2018 • Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Tom Rainforth, Yee Whye Teh, Frank Wood
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.
1 code implementation • NeurIPS 2017 • N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank Wood, Philip H. S. Torr
We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder.
1 code implementation • 1 Dec 2016 • Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N. Siddharth, Philip H. S. Torr
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions.
no code implementations • 22 Nov 2016 • N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference.
no code implementations • 9 Sep 2015 • Andreas Stuhlmüller, Robert X. D. Hawkins, N. Siddharth, Noah D. Goodman
When models are expressed as probabilistic programs, the models themselves are highly structured objects that can be used to derive annealing sequences that are more sensitive to domain structure.
no code implementations • 20 Sep 2013 • Andrei Barbu, N. Siddharth, Jeffrey Mark Siskind
We present an approach to searching large video corpora for video clips which depict a natural-language query in the form of a sentence.
no code implementations • CVPR 2014 • N. Siddharth, Andrei Barbu, Jeffrey Mark Siskind
We present a system that demonstrates how the compositional structure of events, in concert with the compositional structure of language, can interplay with the underlying focusing mechanisms in video action recognition, thereby providing a medium, not only for top-down and bottom-up integration, but also for multi-modal integration between vision and language.