N. Siddharth | DeepAI

DeepAI

Featured Co-authors

Philip H. S. Torr
163 publications
Joshua B. Tenenbaum
152 publications
Yee Whye Teh
115 publications
Pushmeet Kohli
91 publications
Shimon Whiteson
91 publications
Frank Wood
76 publications
Gabriel Synnaeve
64 publications
Philip Torr
44 publications
Jan-Willem van de Meent
42 publications
Tom Rainforth
41 publications
Noah D. Goodman
39 publications

∙ 06/03/2022

Drawing out of Distribution with Neuro-Symbolic Generative Models

Learning general-purpose representations from perceptual inputs is a hal...

Yichao Liang, et al. ∙

∙ 01/31/2022

Adversarial Masking for Self-Supervised Learning

We propose ADIOS, a masked image model (MIM) framework for self-supervis...

Yuge Shi, et al. ∙

∙ 06/25/2021

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

We introduce a simple and effective method for learning VAEs with contro...

Ning Miao, et al. ∙

∙ 06/23/2021

Learning Multimodal VAEs through Mutual Supervision

Multimodal VAEs seek to model the joint distribution over heterogeneous ...

Tom Joy, et al. ∙

∙ 04/20/2021

Gradient Matching for Domain Generalization

Machine learning systems typically assume that the distributions of trai...

Yuge Shi, et al. ∙

∙ 07/02/2020

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

Multimodal learning for generative models often refers to the learning o...

Yuge Shi, et al. ∙

∙ 06/17/2020

Rethinking Semi-Supervised Learning in VAEs

We present an alternative approach to semi-supervision in variational au...

Tom Joy, et al. ∙

∙ 05/14/2020

Simulation-Based Inference for Global Health Decisions

The COVID-19 pandemic has highlighted the importance of in-silico epidem...

Christian Schroeder de Witt, et al. ∙

∙ 04/20/2020

A Revised Generative Evaluation of Visual Dialogue

Evaluating Visual Dialogue, the task of answering a sequence of question...

Daniela Massiceti, et al. ∙

∙ 12/13/2019

Lessons from reinforcement learning for biological representations of space

Neuroscientists postulate 3D representations in the brain in a variety o...

Alex Muryy, et al. ∙

∙ 11/08/2019

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

Learning generative models that span multiple data modalities, such as v...

Yuge Shi, et al. ∙

∙ 04/01/2019

Multitask Soft Option Learning

We present Multitask Soft Option Learning (MSOL), a hierarchical multita...

Maximilian Igl, et al. ∙

∙ 12/16/2018

Visual Dialogue without Vision or Dialogue

We characterise some of the quirks and shortcomings in the exploration o...

Daniela Massiceti, et al. ∙

∙ 05/26/2018

Revisiting Reweighted Wake-Sleep

Discrete latent-variable models, while applicable in a variety of settin...

Tuan Anh Le, et al. ∙

∙ 04/17/2018

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

Deep generative modelling for robust human body analysis is an emerging ...

Rodrigo de Bem, et al. ∙

∙ 02/11/2018

FlipDial: A Generative Model for Two-Way Visual Dialogue

We present FlipDial, a generative model for visual dialogue that simulta...

Daniela Massiceti, et al. ∙

∙ 12/01/2017

Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference

In learning deep generative models, the encoder for variational inferenc...

Stefan Webb, et al. ∙

∙ 06/01/2017

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Variational autoencoders (VAEs) learn representations of data by jointly...

N. Siddharth, et al. ∙

∙ 12/01/2016

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

A number of recent approaches to policy learning in 2D game domains have...

Shehroze Bhatti, et al. ∙

∙ 11/22/2016

Inducing Interpretable Representations with Variational Autoencoders

We develop a framework for incorporating structured graphical models in ...

N. Siddharth, et al. ∙

∙ 09/09/2015

Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs

Many practical techniques for probabilistic inference require a sequence...

Andreas Stuhlmüller, et al. ∙

∙ 09/20/2013

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

We present an approach to searching large video corpora for video clips ...

Andrei Barbu, et al. ∙

∙ 08/19/2013

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

We present a system that demonstrates how the compositional structure of...

N. Siddharth, et al. ∙