
Logically Consistent Loss for Visual Question Answering
Given an image, a background knowledge, and a set of questions about an...
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Hierarchical Conditional Relation Networks for Multimodal Video Question Answering
Video QA challenges modelers in multiple fronts. Modeling video necessit...
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Neurocoder: Learning GeneralPurpose Computation Using Stored Neural Programs
Artificial Neural Networks are uniquely adroit at machine learning by pr...
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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...
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Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
We propose an algorithm for Bayesian functional optimisation  that is, ...
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Sublinear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Bayesian optimisation is a popular method for efficient optimisation of ...
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Learning to Abstract and Predict Human Actions
Human activities are naturally structured as hierarchies unrolled over t...
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Distributional Reinforcement Learning with Maximum Mean Discrepancy
Distributional reinforcement learning (RL) has achieved stateoftheart...
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Bayesian Optimization with Missing Inputs
Bayesian optimization (BO) is an efficient method for optimizing expensi...
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Scalable Backdoor Detection in Neural Networks
Recently, it has been shown that deep learning models are vulnerable to ...
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Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
In order to improve the performance of Bayesian optimisation, we develop...
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DeepCoDA: personalized interpretability for compositional health
Interpretability allows the domainexpert to directly evaluate the model...
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HyperVAE: A Minimum Description Length Variational HyperEncoding Network
We propose a framework called HyperVAE for encoding distributions of dis...
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Dynamic Language Binding in Relational Visual Reasoning
We present Languagebinding Object Graph Network, the first neural reaso...
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Incorporating Expert Prior in Bayesian Optimisation via Space Warping
Bayesian optimisation is a wellknown sampleefficient method for the op...
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Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Scientific experiments are usually expensive due to complex experimental...
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Hierarchical Conditional Relation Networks for Video Question Answering
Video question answering (VideoQA) is challenging as it requires modelin...
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SelfAssttentive Associative Memory
Heretofore, neural networks with external memory are restricted to singl...
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SelfAttentive Associative Memory
Heretofore, neural networks with external memory are restricted to singl...
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Distributionally Robust Bayesian Quadrature Optimization
Bayesian quadrature optimization (BQO) maximizes the expectation of an e...
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Bayesian Optimization for Categorical and CategorySpecific Continuous Inputs
Many realworld functions are defined over both categorical and category...
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Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Scaling Bayesian optimisation (BO) to highdimensional search spaces is ...
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Bayesian Optimization with Unknown Search Space
Applying Bayesian optimization in problems wherein the search space is u...
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Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
Prior access to domain knowledge could significantly improve the perform...
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Costaware Multiobjective Bayesian optimisation
The notion of expense in Bayesian optimisation generally refers to the u...
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Accelerating Experimental Design by Incorporating Experimenter Hunches
Experimental design is a process of obtaining a product with target prop...
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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...
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Sparse Spectrum Gaussian Process for Bayesian Optimisation
We propose a novel sparse spectrum approximation of Gaussian process (GP...
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Neural Storedprogram Memory
Neural networks powered with external memory simulate computer behaviors...
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Memorizing Normality to Detect Anomaly: Memoryaugmented Deep Autoencoder for Unsupervised Anomaly Detection
Deep autoencoder has been extensively used for anomaly detection. Traini...
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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...
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Stable Bayesian Optimisation via Direct Stability Quantification
In this paper we consider the problem of finding stable maxima of expens...
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Multiobjective Bayesian optimisation with preferences over objectives
We present a Bayesian multiobjective optimisation algorithm that allows...
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Improving Generalization and Stability of Generative Adversarial Networks
Generative Adversarial Networks (GANs) are one of the most popular tools...
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Fast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives
In this paper we develop a Bayesian optimization based hyperparameter tu...
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Learning to Remember More with Less Memorization
Memoryaugmented neural networks consisting of a neural controller and a...
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Graph Transformation Policy Network for Chemical Reaction Prediction
We address a fundamental problem in chemistry known as chemical reaction...
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Practical Batch Bayesian Optimization for Less Expensive Functions
Bayesian optimization (BO) and its batch extensions are successful for o...
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Bayesian functional optimisation with shape prior
Real world experiments are expensive, and thus it is important to reach ...
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Relational dynamic memory networks
Working memory is an essential component of reasoning  the capacity to...
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Variational Memory EncoderDecoder
Introducing variability while maintaining coherence is a core task in le...
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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...
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Kernel PreTraining in Feature Space via mKernels
This paper presents a novel approach to kernel tuning. The method presen...
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Attentional Multilabel Learning over Graphs  A message passing approach
We address a largely open problem of multilabel classification over grap...
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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...
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High Dimensional Bayesian Optimization Using Dropout
Scaling Bayesian optimization to high dimensions is challenging task as ...
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Covariance Function PreTraining with mKernels for Accelerated Bayesian Optimisation
The paper presents a novel approach to direct covariance function learni...
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Dual Control Memory Augmented Neural Networks for Treatment Recommendations
Machineassisted treatment recommendations hold a promise to reduce phys...
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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 ...
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Dual Memory Neural Computer for Asynchronous Twoview Sequential Learning
One of the core task in multiview learning is to capture all relations ...
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Svetha Venkatesh
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Australian Laureate Fellow Alfred Deakin Professor and Director Center for Pattern Recognition and Data Analytics (PRaDA) at Deakin University