Search Results for author: Anil Anthony Bharath

Found 15 papers, 8 papers with code

High-resolution 3D Maps of Left Atrial Displacements using an Unsupervised Image Registration Neural Network

no code implementations5 Sep 2023 Christoforos Galazis, Anil Anthony Bharath, Marta Varela

Functional analysis of the left atrium (LA) plays an increasingly important role in the prognosis and diagnosis of cardiovascular diseases.

Unsupervised Image Registration

Disentangled Generative Models for Robust Prediction of System Dynamics

1 code implementation26 Aug 2021 Stathi Fotiadis, Mario Lino, Shunlong Hu, Stef Garasto, Chris D Cantwell, Anil Anthony Bharath

Deep neural networks have become increasingly of interest in dynamical system prediction, but out-of-distribution generalization and long-term stability still remains challenging.

Disentanglement Out-of-Distribution Generalization

Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay

1 code implementation17 Aug 2021 Tianhong Dai, Hengyan Liu, Kai Arulkumaran, Guangyu Ren, Anil Anthony Bharath

We evaluate DTGSH on five challenging robotic manipulation tasks in simulated robot environments, where we show that our method can learn more quickly and reach higher performance than other state-of-the-art approaches on all tasks.

Point Processes

Fully Convolutional Approach for Simulating Wave Dynamics

no code implementations1 Jan 2021 Mario Lino Valencia, Chris D Cantwell, Eduardo Pignatelli, Stathi Fotiadis, Anil Anthony Bharath

In this work, we investigate the performance of fully convolutional networks to predict the motion and interaction of surface waves in open and closed complex geometries.

Episodic Self-Imitation Learning with Hindsight

1 code implementation26 Nov 2020 Tianhong Dai, Hengyan Liu, Anil Anthony Bharath

The trajectory selection module is shown to prevent the agent learning undesirable hindsight experiences.

Continuous Control Imitation Learning

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation

no code implementations18 Dec 2019 Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath

Furthermore, even with an improved saliency method introduced in this work, we show that qualitative studies may not always correspond with quantitative measures, necessitating the combination of inspection tools in order to provide sufficient insights into the behaviour of trained agents.

reinforcement-learning Reinforcement Learning (RL)

Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control

1 code implementation21 Nov 2019 Marta Sarrico, Kai Arulkumaran, Andrea Agostinelli, Pierre Richemond, Anil Anthony Bharath

Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data.

Atari Games reinforcement-learning +1

Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means

1 code implementation21 Nov 2019 Andrea Agostinelli, Kai Arulkumaran, Marta Sarrico, Pierre Richemond, Anil Anthony Bharath

Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches.

Atari Games Clustering +3

A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry

1 code implementation28 Nov 2017 Fatmatulzehra Uslu, Anil Anthony Bharath

In this study, we introduce three information sources for width estimation.

A Brief Survey of Deep Reinforcement Learning

no code implementations19 Aug 2017 Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world.

reinforcement-learning Reinforcement Learning (RL)

Denoising Adversarial Autoencoders

1 code implementation3 Mar 2017 Antonia Creswell, Anil Anthony Bharath

Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space.

Denoising General Classification

Inverting The Generator Of A Generative Adversarial Network

no code implementations17 Nov 2016 Antonia Creswell, Anil Anthony Bharath

When the high-dimensional distribution describes images of a particular data set, the network should learn to generate visually similar image samples for latent variables that are close to each other in the latent space.

Generative Adversarial Network Image Classification +3

Improving Sampling from Generative Autoencoders with Markov Chains

1 code implementation28 Oct 2016 Antonia Creswell, Kai Arulkumaran, Anil Anthony Bharath

Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model.

Adversarial Training For Sketch Retrieval

no code implementations10 Jul 2016 Antonia Creswell, Anil Anthony Bharath

Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification.

Image Generation Retrieval +2

Classifying Options for Deep Reinforcement Learning

no code implementations27 Apr 2016 Kai Arulkumaran, Nat Dilokthanakul, Murray Shanahan, Anil Anthony Bharath

In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options.

Hierarchical Reinforcement Learning reinforcement-learning +1

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