Svetha Venkatesh

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Deakin University ∙ Curtin University

Australian Laureate Fellow Alfred Deakin Professor and Director Center for Pattern Recognition and Data Analytics (PRaDA) at Deakin University

Logically Consistent Loss for Visual Question Answering

Given an image, a back-ground knowledge, and a set of questions about an...
11/19/2020 ∙ by Anh Cat Le Ngo, et al. ∙ 0 ∙ share

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Hierarchical Conditional Relation Networks for Multimodal Video Question Answering

Video QA challenges modelers in multiple fronts. Modeling video necessit...
10/18/2020 ∙ by Thao Minh Le, et al. ∙ 11 ∙ share

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Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs

Artificial Neural Networks are uniquely adroit at machine learning by pr...
09/24/2020 ∙ by Hung Le, et al. ∙ 12 ∙ share

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Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning

Guilt aversion induces experience of a utility loss in people if they be...
09/16/2020 ∙ by Dung Nguyen, et al. ∙ 5 ∙ share

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Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition

We propose an algorithm for Bayesian functional optimisation - that is, ...
09/08/2020 ∙ by Alistair Shilton, et al. ∙ 8 ∙ share

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Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces

Bayesian optimisation is a popular method for efficient optimisation of ...
09/05/2020 ∙ by Hung Tran-The, et al. ∙ 7 ∙ share

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Learning to Abstract and Predict Human Actions

Human activities are naturally structured as hierarchies unrolled over t...
08/20/2020 ∙ by Romero Morais, et al. ∙ 3 ∙ share

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Distributional Reinforcement Learning with Maximum Mean Discrepancy

Distributional reinforcement learning (RL) has achieved state-of-the-art...
07/24/2020 ∙ by Thanh Tang Nguyen, et al. ∙ 0 ∙ share

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Bayesian Optimization with Missing Inputs

Bayesian optimization (BO) is an efficient method for optimizing expensi...
06/19/2020 ∙ by Phuc Luong, et al. ∙ 20 ∙ share

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Scalable Backdoor Detection in Neural Networks

Recently, it has been shown that deep learning models are vulnerable to ...
06/10/2020 ∙ by Haripriya Harikumar, et al. ∙ 0 ∙ share

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Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

In order to improve the performance of Bayesian optimisation, we develop...
06/08/2020 ∙ by Julian Berk, et al. ∙ 0 ∙ share

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DeepCoDA: personalized interpretability for compositional health

Interpretability allows the domain-expert to directly evaluate the model...
06/02/2020 ∙ by Thomas P. Quinn, et al. ∙ 16 ∙ share

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HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network

We propose a framework called HyperVAE for encoding distributions of dis...
05/18/2020 ∙ by Phuoc Nguyen, et al. ∙ 56 ∙ share

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Dynamic Language Binding in Relational Visual Reasoning

We present Language-binding Object Graph Network, the first neural reaso...
04/30/2020 ∙ by Thao Minh Le, et al. ∙ 6 ∙ share

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Incorporating Expert Prior in Bayesian Optimisation via Space Warping

Bayesian optimisation is a well-known sample-efficient method for the op...
03/27/2020 ∙ by Anil Ramachandran, et al. ∙ 3 ∙ share

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Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling

Scientific experiments are usually expensive due to complex experimental...
02/26/2020 ∙ by Cheng Li, et al. ∙ 4 ∙ share

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Hierarchical Conditional Relation Networks for Video Question Answering

Video question answering (VideoQA) is challenging as it requires modelin...
02/25/2020 ∙ by Thao Minh Le, et al. ∙ 6 ∙ share

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Self-Assttentive Associative Memory

Heretofore, neural networks with external memory are restricted to singl...
02/10/2020 ∙ by Hung Le, et al. ∙ 13 ∙ share

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Self-Attentive Associative Memory

Heretofore, neural networks with external memory are restricted to singl...
02/10/2020 ∙ by Hung Le, et al. ∙ 12 ∙ share

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Distributionally Robust Bayesian Quadrature Optimization

Bayesian quadrature optimization (BQO) maximizes the expectation of an e...
01/19/2020 ∙ by Thanh Tang Nguyen, et al. ∙ 23 ∙ share

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Bayesian Optimization for Categorical and Category-Specific Continuous Inputs

Many real-world functions are defined over both categorical and category...
11/28/2019 ∙ by Dang Nguyen, et al. ∙ 12 ∙ share

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Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization

Scaling Bayesian optimisation (BO) to high-dimensional search spaces is ...
11/27/2019 ∙ by Hung Tran-The, et al. ∙ 0 ∙ share

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Bayesian Optimization with Unknown Search Space

Applying Bayesian optimization in problems wherein the search space is u...
10/29/2019 ∙ by Huong Ha, et al. ∙ 0 ∙ share

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Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning

Prior access to domain knowledge could significantly improve the perform...
09/10/2019 ∙ by Thommen George Karimpanal, et al. ∙ 14 ∙ share

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Cost-aware Multi-objective Bayesian optimisation

The notion of expense in Bayesian optimisation generally refers to the u...
09/09/2019 ∙ by Majid Abdolshah, et al. ∙ 0 ∙ share

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Accelerating Experimental Design by Incorporating Experimenter Hunches

Experimental design is a process of obtaining a product with target prop...
07/22/2019 ∙ by Cheng Li, et al. ∙ 0 ∙ share

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Learning to Reason with Relational Video Representation for Question Answering

How does machine learn to reason about the content of a video in answeri...
07/10/2019 ∙ by Thao Minh Le, et al. ∙ 4 ∙ share

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Sparse Spectrum Gaussian Process for Bayesian Optimisation

We propose a novel sparse spectrum approximation of Gaussian process (GP...
06/21/2019 ∙ by Ang Yang, et al. ∙ 0 ∙ share

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Neural Stored-program Memory

Neural networks powered with external memory simulate computer behaviors...
05/25/2019 ∙ by Hung Le, et al. ∙ 0 ∙ share

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Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

Deep autoencoder has been extensively used for anomaly detection. Traini...
04/04/2019 ∙ by Dong Gong, et al. ∙ 34 ∙ share

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Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos

Appearance features have been widely used in video anomaly detection eve...
03/08/2019 ∙ by Romero Morais, et al. ∙ 6 ∙ share

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Stable Bayesian Optimisation via Direct Stability Quantification

In this paper we consider the problem of finding stable maxima of expens...
02/21/2019 ∙ by Alistair Shilton, et al. ∙ 0 ∙ share

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Multi-objective Bayesian optimisation with preferences over objectives

We present a Bayesian multi-objective optimisation algorithm that allows...
02/12/2019 ∙ by Majid Abdolshah, et al. ∙ 0 ∙ share

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Improving Generalization and Stability of Generative Adversarial Networks

Generative Adversarial Networks (GANs) are one of the most popular tools...
02/11/2019 ∙ by Hoang Thanh-Tung, et al. ∙ 26 ∙ share

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Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives

In this paper we develop a Bayesian optimization based hyperparameter tu...
02/06/2019 ∙ by Tinu Theckel Joy, et al. ∙ 0 ∙ share

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Learning to Remember More with Less Memorization

Memory-augmented neural networks consisting of a neural controller and a...
01/05/2019 ∙ by Hung Le, et al. ∙ 4 ∙ share

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Graph Transformation Policy Network for Chemical Reaction Prediction

We address a fundamental problem in chemistry known as chemical reaction...
12/22/2018 ∙ by Kien Do, et al. ∙ 6 ∙ share

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Practical Batch Bayesian Optimization for Less Expensive Functions

Bayesian optimization (BO) and its batch extensions are successful for o...
11/05/2018 ∙ by Vu Nguyen, et al. ∙ 0 ∙ share

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Bayesian functional optimisation with shape prior

Real world experiments are expensive, and thus it is important to reach ...
09/19/2018 ∙ by Pratibha Vellanki, et al. ∙ 8 ∙ share

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Relational dynamic memory networks

Working memory is an essential component of reasoning -- the capacity to...
08/10/2018 ∙ by Trang Pham, et al. ∙ 0 ∙ share

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Variational Memory Encoder-Decoder

Introducing variability while maintaining coherence is a core task in le...
07/26/2018 ∙ by Hung Le, et al. ∙ 0 ∙ share

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On catastrophic forgetting and mode collapse in Generative Adversarial Networks

Generative Adversarial Networks (GAN) are one of the most prominent tool...
07/11/2018 ∙ by Hoang Thanh-Tung, et al. ∙ 2 ∙ share

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Kernel Pre-Training in Feature Space via m-Kernels

This paper presents a novel approach to kernel tuning. The method presen...
05/21/2018 ∙ by Alistair Shilton, et al. ∙ 0 ∙ share

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Attentional Multilabel Learning over Graphs - A message passing approach

We address a largely open problem of multilabel classification over grap...
04/01/2018 ∙ by Kien Do, et al. ∙ 0 ∙ share

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Rapid Bayesian optimisation for synthesis of short polymer fiber materials

The discovery of processes for the synthesis of new materials involves m...
02/16/2018 ∙ by Cheng Li, et al. ∙ 0 ∙ share

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High Dimensional Bayesian Optimization Using Dropout

Scaling Bayesian optimization to high dimensions is challenging task as ...
02/15/2018 ∙ by Cheng Li, et al. ∙ 0 ∙ share

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Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation

The paper presents a novel approach to direct covariance function learni...
02/15/2018 ∙ by Alistair Shilton, et al. ∙ 0 ∙ share

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Dual Control Memory Augmented Neural Networks for Treatment Recommendations

Machine-assisted treatment recommendations hold a promise to reduce phys...
02/11/2018 ∙ by Hung Le, et al. ∙ 0 ∙ share

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Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records

Modern healthcare is ripe for disruption by AI. A game changer would be ...
02/03/2018 ∙ by Phuoc Nguyen, et al. ∙ 0 ∙ share

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Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning

One of the core task in multi-view learning is to capture all relations ...
02/02/2018 ∙ by Hung Le, et al. ∙ 0 ∙ share

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Svetha Venkatesh

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