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A Generalization Method of Partitioned Activation Function for Complex NumberFeb 08 2018A method to convert real number partitioned activation function into complex number one is provided. The method has 4em variations; 1 has potential to get holomorphic activation, 2 has potential to conserve complex angle, and the last 1 guarantees interaction ... More
Efficient collective swimming by harnessing vortices through deep reinforcement learningFeb 07 2018Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behaviour has been associated with evolutionary advantages including collective energy savings. How fish ... More
PPFNet: Global Context Aware Local Features for Robust 3D Point MatchingFeb 07 2018We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds. PPFNet learns local descriptors on pure geometry and is highly aware of the global ... More
FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath SimilarityFeb 07 2018Feb 08 2018In the last three decades, human visual attention has been a topic of great interest in various disciplines. In computer vision, many models have been proposed to predict the distribution of human fixations on a visual input. Recently, thanks to the creation ... More
Classification of Things in DBpedia using Deep Neural NetworksFeb 07 2018The Semantic Web aims at representing knowledge about the real world at web scale - things, their attributes and relationships among them can be represented as nodes and edges in an inter-linked semantic graph. In the presence of noisy data, as is typical ... More
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk PredictionFeb 07 2018We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), ... More
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data SetsFeb 07 2018Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables. Through extensive experiments ... More
Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief ProblemFeb 07 2018This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the travelling thief problem, which is known to the Evolutionary Computation community ... More
Recent Advances in Neural Program SynthesisFeb 07 2018In recent years, deep learning has made tremendous progress in a number of fields that were previously out of reach for artificial intelligence. The successes in these problems has led researchers to consider the possibilities for intelligent systems ... More
A Critical Investigation of Deep Reinforcement Learning for NavigationFeb 07 2018The navigation problem is classically approached in two steps: an exploration step, where map-information about the environment is gathered; and an exploitation step, where this information is used to navigate efficiently. Deep reinforcement learning ... More
Learning Role-based Graph EmbeddingsFeb 07 2018Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and graphs ... More
Scalable Meta-Learning for Bayesian OptimizationFeb 06 2018Bayesian optimization has become a standard technique for hyperparameter optimization, including data-intensive models such as deep neural networks that may take days or weeks to train. We consider the setting where previous optimization runs are available, ... More
Efficient Large-Scale Multi-Modal ClassificationFeb 06 2018While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal. Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations ... More
Granger-causal Attentive Mixtures of ExpertsFeb 06 2018Several methods have recently been proposed to detect salient input features for outputs of neural networks. Those methods offer a qualitative glimpse at feature importance, but they fall short of providing quantifiable attributions that can be compared ... More
Improving Variational Encoder-Decoders in Dialogue GenerationFeb 06 2018Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding ... More
A Survey Of Methods For Explaining Black Box ModelsFeb 06 2018In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports ... More
Decoding-History-Based Adaptive Control of Attention for Neural Machine TranslationFeb 06 2018Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includes the past information in the decoder and the attention mechanism, often ... More
Goal Inference Improves Objective and Perceived Performance in Human-Robot CollaborationFeb 06 2018The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user ... More
Local Wealth Redistribution Promotes Cooperation in Multiagent SystemsFeb 05 2018Designing mechanisms that leverage cooperation between agents has been a long-lasting goal in Multiagent Systems. The task is especially challenging when agents are selfish, lack common goals and face social dilemmas, i.e., situations in which individual ... More
Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature QueriesFeb 05 2018We focus on learning the desired objective function for a robot. Although trajectory demonstrations can be very informative of the desired objective, they can also be difficult for users to provide. Answers to comparison queries, asking which of two trajectories ... More
Regularized Evolution for Image Classifier Architecture SearchFeb 05 2018Feb 06 2018The effort devoted to hand-crafting image classifiers has motivated the use of architecture search to discover them automatically. Reinforcement learning and evolution have both shown promise for this purpose. This study employs a regularized version ... More
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilizationFeb 02 2018Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically cause it to forget previously learned tasks. This phenomenon is the result of "catastrophic forgetting", ... More
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to CroatianFeb 02 2018This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement ... More
Scalable Lévy Process Priors for Spectral Kernel LearningFeb 02 2018Gaussian processes are rich distributions over functions, with generalization properties determined by a kernel function. When used for long-range extrapolation, predictions are particularly sensitive to the choice of kernel parameters. It is therefore ... More
Modelling contextuality by probabilistic programs with hypergraph semanticsJan 31 2018Models of a phenomenon are often developed by examining it under different experimental conditions, or measurement contexts. The resultant probabilistic models assume that the underlying random variables, which define a measurable set of outcomes, can ... More
Cluster-based Approach to Improve Affect Recognition from Passively Sensed DataJan 31 2018Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via ... More
Deep Predictive Models in Interactive MusicJan 31 2018Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making ... More
Deep Learning Works in Practice. But Does it Work in Theory?Jan 31 2018Deep learning relies on a very specific kind of neural networks: those superposing several neural layers. In the last few years, deep learning achieved major breakthroughs in many tasks such as image analysis, speech recognition, natural language processing, ... More
Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive AccountJan 30 2018The Availability bias, manifested in the over-representation of extreme eventualities in decision-making, is a well-known cognitive bias, and is generally taken as evidence of human irrationality. In this work, we present the first rational, metacognitive ... More
Personalized Survival Prediction with Contextual Explanation NetworksJan 30 2018Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices. In this paper, we design a model that concurrently learns to accurately predict patient-specific ... More
The Intriguing Properties of Model ExplanationsJan 30 2018Linear approximations to the decision boundary of a complex model have become one of the most popular tools for interpreting predictions. In this paper, we study such linear explanations produced either post-hoc by a few recent methods or generated along ... More
Using deep Q-learning to understand the tax evasion behavior of risk-averse firmsJan 29 2018Designing tax policies that are effective in curbing tax evasion and maximize state revenues requires a rigorous understanding of taxpayer behavior. This work explores the problem of determining the strategy a self-interested, risk-averse tax entity is ... More
On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and ErrorJan 29 2018Feb 04 2018We propose two general and falsifiable hypotheses about expectations on generalization error when learning in the context of concept drift. One posits that as drift rate increases, the forgetting rate that minimizes generalization error will also increase ... More
A Cyber Science Based Ontology for Artificial General Intelligence ContainmentJan 28 2018The development of artificial general intelligence is considered by many to be inevitable. What such intelligence does after becoming aware is not so certain. To that end, research suggests that the likelihood of artificial general intelligence becoming ... More
HONE: Higher-Order Network EmbeddingsJan 28 2018This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs. The HONE framework is highly expressive and flexible with many interchangeable components. The experimental results demonstrate ... More
Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0Jan 24 2018The foundations of all methodologies for the measurement and verification (M&V) of energy savings are based on the same five key principles: accuracy, completeness, conservatism, consistency and transparency. The most widely accepted methodologies tend ... More
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning AgentsJan 24 2018Feb 04 2018Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al. 2016). Psychlab enables implementations of classical laboratory psychological experiments so that they work with both human and artificial ... More
Intrinsic dimension of concept latticesJan 24 2018Geometric analysis is a very capable theory to understand the influence of the high dimensionality of the input data in machine learning (ML) and knowledge discovery (KD). With our approach we can assess how far the application of a specific KD/ML-algorithm ... More
MaskGAN: Better Text Generation via Filling in the ______Jan 23 2018Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation ... More
Expectation Learning for Adaptive Crossmodal Stimuli AssociationJan 23 2018The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this ... More
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral-Regularization AlgorithmsJan 22 2018We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes ... More
A Deep Reinforcement Learning Chatbot (Short Version)Jan 20 2018We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both ... More
Learning model-based strategies in simple environments with hierarchical q-networksJan 20 2018Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn generalizable strategies remains a pressing challenge. This critical limitation ... More
A high-performance analog Max-SAT solver and its application to Ramsey numbersJan 20 2018Jan 28 2018We introduce a continuous-time analog solver for MaxSAT, a quintessential class of NP-hard discrete optimization problems, where the task is to find a truth assignment for a set of Boolean variables satisfying the maximum number of given logical constraints. ... More
Demonstration of Topological Data Analysis on a Quantum ProcessorJan 19 2018Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure. Recently, an efficient quantum algorithm was proposed [Lloyd, Garnerone, Zanardi, Nat. Commun. 7, 10138 ... More
Innateness, AlphaZero, and Artificial IntelligenceJan 17 2018The concept of innateness is rarely discussed in the context of artificial intelligence. When it is discussed, or hinted at, it is often the context of trying to reduce the amount of innate machinery in a given system. In this paper, I consider as a test ... More
Time Series Segmentation through Automatic Feature LearningJan 16 2018Jan 26 2018Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and activity recognition. In order to leverage these data, automatic knowledge ... More
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine LearningJan 16 2018In order for people to be able to trust and take advantage of the results of advanced machine learning and artificial intelligence solutions for real decision making, people need to be able to understand the machine rationale for given output. Research ... More
Better Runtime Guarantees Via Stochastic DominationJan 13 2018Apart from few exceptions, the mathematical runtime analysis of evolutionary algorithms is mostly concerned with expected runtimes. In this work, we argue that stochastic domination is a notion that should be used more frequently in this area. Stochastic ... More
Neural Program Synthesis with Priority Queue TrainingJan 10 2018We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best ... More
Reasoning about Unforeseen Possibilities During Policy LearningJan 10 2018Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is known in advance and does not change during learning. This is an unrealistic ... More
Entropy production rate as a criterion for inconsistency in decision theoryJan 05 2018Evaluating pairwise comparisons breaks down complex decision problems into tractable ones. Pairwise comparison matrices (PCMs) are regularly used to solve multiple-criteria decision-making (MCDM) problems using Saaty's analytic hierarchy process (AHP) ... More
Learning audio and image representations with bio-inspired trainable feature extractorsJan 02 2018Recent advancements in pattern recognition and signal processing concern the automatic learning of data representations from labeled training samples. Typical approaches are based on deep learning and convolutional neural networks, which require large ... More
Deep Learning: A Critical AppraisalJan 02 2018Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) ... More
Learning Continuous User Representations through Hybrid Filtering with doc2vecDec 31 2017Players in the online ad ecosystem are struggling to acquire the user data required for precise targeting. Audience look-alike modeling has the potential to alleviate this issue, but models' performance strongly depends on quantity and quality of available ... More
Kernel Robust Bias-Aware Prediction under Covariate ShiftDec 28 2017Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challenging machine learning task. Robust Bias-Aware (RBA) prediction provides ... More
The information bottleneck and geometric clusteringDec 27 2017The information bottleneck (IB) approach to clustering takes a joint distribution $P\!\left(X,Y\right)$ and maps the data $X$ to cluster labels $T$ which retain maximal information about $Y$ (Tishby et al., 1999). This objective results in an algorithm ... More
Pseudorehearsal in actor-critic agents with neural network function approximationDec 20 2017Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested ... More
Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography imagesDec 20 2017Breast cancer is already one of the most common form of cancer worldwide. Mammography image analysis is still the most effective diagnostic method to promote the early detection of breast cancer. Accurately segmenting tumors in digital mammography images ... More
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital PathologyDec 18 2017Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The ... More
Towards Fully Environment-Aware UAVs: Real-Time Path Planning with Online 3D Wind Field Prediction in Complex TerrainDec 10 2017Today, low-altitude fixed-wing Unmanned Aerial Vehicles (UAVs) are largely limited to primitively follow user-defined waypoints. To allow fully-autonomous remote missions in complex environments, real-time environment-aware navigation is required both ... More
Social Emotion Mining Techniques for Facebook Posts Reaction PredictionDec 08 2017As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages ... More
Fuzzy-Based Dialectical Non-Supervised Image Classification and ClusteringDec 03 2017The materialist dialectical method is a philosophical investigative method to analyze aspects of reality. These aspects are viewed as complex processes composed by basic units named poles, which interact with each other. Dialectics has experienced considerable ... More
A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic imagesDec 03 2017According to the World Health Organization, breast cancer is the most common form of cancer in women. It is the second leading cause of death among women round the world, becoming the most fatal form of cancer. Mammographic image segmentation is a fundamental ... More
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local MinimaDec 03 2017We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation function, i.e., $f(\mathbf{Z}; \mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^\top\mathbf{Z}_j)$, in which both the ... More
Evaluation of Alzheimer's Disease by Analysis of MR Images using Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the ADC MapsDec 03 2017Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed ... More
Visual Features for Context-Aware Speech RecognitionDec 01 2017Automatic transcriptions of consumer-generated multi-media content such as "Youtube" videos still exhibit high word error rates. Such data typically occupies a very broad domain, has been recorded in challenging conditions, with cheap hardware and a focus ... More
HoME: a Household Multimodal EnvironmentNov 29 2017We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts ... More
Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena GamesNov 17 2017Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy ... More
Learning with Options that Terminate Off-PolicyNov 10 2017Dec 02 2017A temporally abstract action, or an option, is specified by a policy and a termination condition: the policy guides option behavior, and the termination condition roughly determines its length. Generally, learning with longer options (like learning with ... More
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and AuthenticationNov 08 2017This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of unknown authors in a way that is not easy to be deceived by forged art. ... More
Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging RecordingNov 04 2017Individual Neurons in the nervous systems exploit various dynamics. To capture these dynamics for single neurons, we tune the parameters of an electrophysiological model of nerve cells, to fit experimental data obtained by calcium imaging. A search for ... More
Beautiful and damned. Combined effect of content quality and social ties on user engagementNov 01 2017User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate ... More
Erratum: Link prediction in drug-target interactions network using similarity indicesNov 01 2017Background: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network ... More
Gradient-free Policy Architecture Search and AdaptationOct 16 2017We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks. By learning from both demonstration and environmental reward we develop a model that can learn with relatively ... More
Mining Frequent Patterns in Process ModelsOct 11 2017Process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes. In the discovery of process models, retrieving a complex ... More
ACCBench: A Framework for Comparing Causality AlgorithmsOct 10 2017Modern socio-technical systems are increasingly complex. A fundamental problem is that the borders of such systems are often not well-defined a-priori, which among other problems can lead to unwanted behavior during runtime. Ideally, unwanted behavior ... More
Duality of Graphical Models and Tensor NetworksOct 04 2017In this article we show the duality between tensor networks and undirected graphical models with discrete variables. We study tensor networks on hypergraphs, which we call tensor hypernetworks. We show that the tensor hypernetwork on a hypergraph exactly ... More
Can you fool AI with adversarial examples on a visual Turing test?Sep 25 2017Deep learning has achieved impressive results in many areas of Computer Vision and Natural Language Pro- cessing. Among others, Visual Question Answering (VQA), also referred to a visual Turing test, is considered one of the most compelling problems, ... More
Underwater Multi-Robot Convoying using Visual Tracking by DetectionSep 25 2017We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-detection, which interleaves efficient ... More
FiLM: Visual Reasoning with a General Conditioning LayerSep 22 2017Dec 18 2017We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We ... More
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]Sep 20 2017In knowledge bases such as Wikidata, it is possible to assert a large set of properties for entities, ranging from generic ones such as name and place of birth to highly profession-specific or background-specific ones such as doctoral advisor or medical ... More
When is a Convolutional Filter Easy To Learn?Sep 18 2017We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only ... More
SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSsSep 16 2017The vehicle to represent Knowledge Organization Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organization System (SKOS). SKOS provides a way to assign a URI to each concept, and this URI functions as a ... More
The shortest way to visit all metro lines in ParisSep 13 2017Sep 19 2017What if $\{$a tourist, a train addict, Dr. Sheldon Cooper, somebody who likes to waste time$\}$ wants to visit all metro lines or carriages in a given network in a minimum number of steps? We study this problem with an application to the Parisian metro ... More
Bayesian Optimisation for Safe Navigation under Localisation UncertaintySep 07 2017In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to identify areas that are safe for ... More
Artificial Intelligence and Data Science in the Automotive IndustrySep 06 2017Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future. This article defines the terms "data science" (also referred ... More
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation SetsAug 22 2017Sep 12 2017Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are usually tackled separately (for instance by combining recurrent neural networks ... More
Benchmark Environments for Multitask Learning in Continuous DomainsAug 14 2017As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit. In discrete domains, performance on the Atari game suite has emerged as the de ... More
Motion Planning under Partial Observability using Game-Based AbstractionAug 14 2017We study motion planning problems where agents move inside environments that are not fully observable and subject to uncertainties. The goal is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications. ... More
RegNet: Multimodal Sensor Registration Using Deep Neural NetworksJul 11 2017In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera. Compared to existing ... More
Learning Visual Reasoning Without Strong PriorsJul 10 2017Dec 18 2017Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, ... More
Evaluating Noisy Optimisation Algorithms: First Hitting Time is ProblematicJun 13 2017Jul 12 2017A key part of any evolutionary algorithm is fitness evaluation. When fitness evaluations are corrupted by noise, as happens in many real-world problems as a consequence of various types of uncertainty, a strategy is needed in order to cope with this. ... More
Off The Beaten Lane: AI Challenges In MOBAs Beyond Player ControlJun 09 2017MOBAs represent a huge segment of online gaming and are growing as both an eSport and a casual genre. The natural starting point for AI researchers interested in MOBAs is to develop an AI to play the game better than a human - but MOBAs have many more ... More
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual ConstraintsJun 01 2017We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically ... More
Free energy-based reinforcement learning using a quantum processorMay 29 2017Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free energy-based reinforcement learning (FERL) as an application of quantum ... More
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer TissuesMay 23 2017Jul 24 2017Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive ... More
Mixed Membership Word Embeddings for Computational Social ScienceMay 20 2017May 25 2017Word embeddings improve the performance of NLP systems by revealing the hidden structural relationships between words. These models have recently risen in popularity due to the performance of scalable algorithms trained in the big data setting. Despite ... More
Strategically knowing howMay 15 2017In this paper, we propose a single-agent logic of goal-directed knowing how extending the standard epistemic logic of knowing that with a new knowing how operator. The semantics of the new operator is based on the idea that knowing how to achieve $\phi$ ... More
Scene Text EraserMay 08 2017The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the information if they are published. In this paper, we proposed a scene text erasing ... More