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VL-BERT: Pre-training of Generic Visual-Linguistic RepresentationsAug 22 2019We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic ... More
Sequential Latent Spaces for Modeling the Intention During Diverse Image CaptioningAug 22 2019Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent ... More
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision ProcessesAug 22 2019Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the first time the semiparametric efficiency limits ... More
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data RefinementAug 22 2019Generic Image recognition is a fundamental and fairly important visual problem in computer vision. One of the major challenges of this task lies in the fact that single image usually has multiple objects inside while the labels are still one-hot, another ... More
Transfer Learning for Relation Extraction via Relation-Gated Adversarial LearningAug 22 2019Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant supervision. However, the human annotation is expensive, while human-crafted ... More
ColorNet -- Estimating Colorfulness in Natural ImagesAug 22 2019Measuring the colorfulness of a natural or virtual scene is critical for many applications in image processing field ranging from capturing to display. In this paper, we propose the first deep learning-based colorfulness estimation metric. For this purpose, ... More
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health ForumsAug 22 2019Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of ... More
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithmsAug 22 2019One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting ... More
The many Shapley values for model explanationAug 22 2019The Shapley value has become a popular method to attribute the prediction of a machine-learning model on an input to its base features. The Shapley value [1] is known to be the unique method that satisfies certain desirable properties, and this motivates ... More
Data Context Adaptation for Accurate Recommendation with Additional InformationAug 22 2019Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? Many previous studies proved that utilizing the additional information with rating data ... More
On the convergence of single-call stochastic extra-gradient methodsAug 22 2019Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems). ... More
Noise Flow: Noise Modeling with Conditional Normalizing FlowsAug 22 2019Modeling and synthesizing image noise is an important aspect in many computer vision applications. The long-standing additive white Gaussian and heteroscedastic (signal-dependent) noise models widely used in the literature provide only a coarse approximation ... More
Efficient Cross-Validation of Echo State NetworksAug 22 2019Echo State Networks (ESNs) are known for their fast and precise one-shot learning of time series. But they often need good hyper-parameter tuning for best performance. For this good validation is key, but usually, a single validation split is used. In ... More
Improving the dynamics of quantum sensors with reinforcement learningAug 22 2019Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while using classical initial states that are easy to prepare. Here, we use the ... More
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum OptimizationAug 22 2019This paper studies the lower bound complexity for the optimization problem whose objective function is the average of $n$ individual smooth convex functions. We consider the algorithm which gets access to gradient and proximal oracle for each individual ... More
Practical Risk Measures in Reinforcement LearningAug 22 2019Practical application of Reinforcement Learning (RL) often involves risk considerations. We study a generalized approximation scheme for risk measures, based on Monte-Carlo simulations, where the risk measures need not necessarily be \emph{coherent}. ... More
A General Data Renewal Model for Prediction Algorithms in Industrial Data AnalyticsAug 22 2019In industrial data analytics, one of the fundamental problems is to utilize the temporal correlation of the industrial data to make timely predictions in the production process, such as fault prediction and yield prediction. However, the traditional prediction ... More
The compositionality of neural networks: integrating symbolism and connectionismAug 22 2019Despite a multitude of empirical studies, little consensus exists on whether neural networks are able to generalise compositionally, a controversy that, in part, stems from a lack of agreement about what it means for a neural model to be compositional. ... More
LoRAS: An oversampling approach for imbalanced datasetsAug 22 2019The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets.It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the ... More
Text Summarization with Pretrained EncodersAug 22 2019Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully ... More
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated LearningAug 22 2019Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are two main concerns of gradient updates transmission. This paper proposes ... More
The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum EntropyAug 22 2019Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. ... More
Centralized and Distributed Machine Learning-Based QoT Estimation for Sliceable Optical NetworksAug 22 2019Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G's diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work we examine ... More
Deep Green Function Convolution for Improving Saliency in Convolutional Neural NetworksAug 22 2019Current saliency methods require to learn large scale regional features using small convolutional kernels, which is not possible with a simple feed-forward network. Some methods solve this problem by using segmentation into superpixels while others downscale ... More
Measuring the Business Value of Recommender SystemsAug 22 2019Recommender Systems are nowadays successfully used by all major web sites (from e-commerce to social media) to filter content and make suggestions in a personalized way. Academic research largely focuses on the value of recommenders for consumers, e.g., ... More
LEAP nets for power grid perturbationsAug 22 2019We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our architeture LEAP net, ... More
Learning stochastic differential equations using RNN with log signature featuresAug 22 2019This paper contributes to the challenge of learning a function on streamed multimodal data through evaluation. The core of the result of our paper is the combination of two quite different approaches to this problem. One comes from the mathematically ... More
Two-Stage Session-based Recommendations with Candidate Rank EmbeddingsAug 22 2019Recent advances in Session-based recommender systems have gained attention due to their potential of providing real-time personalized recommendations with high recall, especially when compared to traditional methods like matrix factorization and item-based ... More
Adaptive Configuration Oracle for Online Portfolio Selection MethodsAug 22 2019Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data. One fundamental problem is online portfolio selection, the goal of which is to exploit this data to sequentially select portfolios of assets to ... More
Distributed Cooperative Online Estimation With Random Observation Matrices, Communication Graphs and Time-DelaysAug 22 2019We analyze convergence of distributed cooperative online estimation algorithms by a network of multiple nodes via information exchanging in an uncertain environment. Each node has a linear observation of an unknown parameter with randomly time-varying ... More
motif2vec: Motif Aware Node Representation Learning for Heterogeneous NetworksAug 22 2019Recent years have witnessed a surge of interest in machine learning on graphs and networks with applications ranging from vehicular network design to IoT traffic management to social network recommendations. Supervised machine learning tasks in networks ... More
NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local OperationsAug 22 2019Road extraction from very high resolution satellite images is one of the most important topics in the field of remote sensing. For the road segmentation problem, spatial properties of the data can usually be captured using Convolutional Neural Networks. ... More
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste InspectionAug 22 2019Surface mount technology (SMT) is a process for producing printed circuit boards. Solder paste printer (SPP), package mounter, and solder reflow oven are used for SMT. The board on which the solder paste is deposited from the SPP is monitored by solder ... More
Finite Precision Stochastic Optimisation -- Accounting for the BiasAug 22 2019We consider first order stochastic optimization where the oracle must quantize each subgradient estimate to $r$ bits. We treat two oracle models: the first where the Euclidean norm of the oracle output is almost surely bounded and the second where it ... More
Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI SystemAug 22 2019A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision ... More
Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniquesAug 22 2019Air conditioning (AC) accounts for a critical portion of the global energy consumption. To improve its energy performance, it is important to fairly benchmark its energy performance and provide the evaluation feedback to users. However, this task has ... More
Semi-supervised Adversarial Active Learning on Attributed GraphsAug 22 2019Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label sparsity issues with the conventional independent and identically distributed ... More
Intra-day Equity Price Prediction using Deep Learning as a Measure of Market EfficiencyAug 22 2019In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively ... More
A Neural Network for Semi-Supervised Learning on ManifoldsAug 21 2019Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural networks operating in the online setting. Here, we propose a feed-forward ... More
Transferability and Hardness of Supervised Classification TasksAug 21 2019We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models. Instead, we estimate these values ... More
BRIDGE: Byzantine-resilient Decentralized Gradient DescentAug 21 2019Decentralized optimization techniques are increasingly being used to learn machine learning models from data distributed over multiple locations without gathering the data at any one location. Unfortunately, methods that are designed for faultless networks ... More
Dynamic Scheduling of MPI-based Distributed Deep Learning Training JobsAug 21 2019There is a general trend towards solving problems suited to deep learning with more complex deep learning architectures trained on larger training sets. This requires longer compute times and greater data parallelization or model parallelization. Both ... More
Boundary Aware Networks for Medical Image SegmentationAug 21 2019Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, ... More
Automated quantum programming via reinforcement learning for combinatorial optimizationAug 21 2019We develop a general method for incentive-based programming of hybrid quantum-classical computing systems using reinforcement learning, and apply this to solve combinatorial optimization problems on both simulated and real gate-based quantum computers. ... More
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance EstimatesAug 21 2019We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution. We ... More
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy AdaptationAug 21 2019We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is to learn policies over multiple competing objectives whose relative ... More
Coarse-to-fine Optimization for Speech EnhancementAug 21 2019In this paper, we propose the coarse-to-fine optimization for the task of speech enhancement. Cosine similarity loss [1] has proven to be an effective metric to measure similarity of speech signals. However, due to the large variance of the enhanced speech ... More
Testing Robustness Against Unforeseen AdversariesAug 21 2019Considerable work on adversarial defense has studied robustness to a fixed, known family of adversarial distortions, most frequently L_p-bounded distortions. In reality, the specific form of attack will rarely be known and adversaries are free to employ ... More
A tree-based radial basis function method for noisy parallel surrogate optimizationAug 21 2019Parallel surrogate optimization algorithms have proven to be efficient methods for solving expensive noisy optimization problems. In this work we develop a new parallel surrogate optimization algorithm (ProSRS), using a novel tree-based "zoom strategy" ... More
QCNN: Quantile Convolutional Neural NetworkAug 21 2019A dilated causal one-dimensional convolutional neural network architecture is proposed for quantile regression. The model can forecast any arbitrary quantile, and it can be trained jointly on multiple similar time series. An application to Value at Risk ... More
Assessing the Impact of a User-Item Collaborative Attack on Class of UsersAug 21 2019Collaborative Filtering (CF) models lie at the core of most recommendation systems due to their state-of-the-art accuracy. They are commonly adopted in e-commerce and online services for their impact on sales volume and/or diversity, and their impact ... More
Estimation of perceptual scales using ordinal embeddingAug 21 2019In this paper, we address the problem of measuring and analysing sensation, the subjective magnitude of one's experience. We do this in the context of the method of triads: the sensation of the stimulus is evaluated via relative judgments of the form: ... More
DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging AnalysisAug 21 2019Calcium imaging is one of the most important tools in neurophysiology as it enables the observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution. In order to use the data gained with calcium imaging, it is necessary ... More
DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging AnalysisAug 21 2019Aug 22 2019Calcium imaging is one of the most important tools in neurophysiology as it enables the observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution. In order to use the data gained with calcium imaging, it is necessary ... More
Design Space of Behaviour Planning for Autonomous DrivingAug 21 2019We explore the complex design space of behaviour planning for autonomous driving. Design choices that successfully address one aspect of behaviour planning can critically constrain others. To aid the design process, in this work we decompose the design ... More
Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the MoviesAug 21 2019The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today's world. Films can help us learn and inspire societal change. But they can also negatively ... More
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial ExamplesAug 21 2019Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for many tasks such as image and text classification, especially as research ... More
Enabling hyperparameter optimization in sequential autoencoders for spiking neural dataAug 21 2019Aug 22 2019Continuing advances in neural interfaces have enabled simultaneous monitoring of spiking activity from hundreds to thousands of neurons. To interpret these large-scale data, several methods have been proposed to infer latent dynamic structure from high-dimensional ... More
Enabling hyperparameter optimization in sequential autoencoders for spiking neural dataAug 21 2019Continuing advances in neural interfaces have enabled simultaneous monitoring of spiking activity from hundreds to thousands of neurons. To interpret these large-scale data, several methods have been proposed to infer latent dynamic structure from high-dimensional ... More
Representation Disentanglement for Multi-task Learning with application to Fetal UltrasoundAug 21 2019One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the ... More
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy ProtectionAug 21 2019In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. Theoretically, we prove that a differentially private learning algorithm used for training the GAN ... More
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for ClassificationAug 21 2019In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, ... More
Federated Learning: Challenges, Methods, and Future DirectionsAug 21 2019Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges ... More
A CNN toolbox for skin cancer classificationAug 21 2019We describe a software toolbox for the configuration of deep neural networks in the domain of skin cancer classification. The implemented software architecture allows developers to quickly set up new convolutional neural network (CNN) architectures and ... More
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation LearningAug 21 2019The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an L2-norm between the ground truth CT and the pCT. However, given that the ... More
Exploring Offline Policy Evaluation for the Continuous-Armed Bandit ProblemAug 21 2019The (contextual) multi-armed bandit problem (MAB) provides a formalization of sequential decision-making which has many applications. However, validly evaluating MAB policies is challenging; we either resort to simulations which inherently include debatable ... More
Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial predictionAug 21 2019Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is ... More
Minimum Description Length RevisitedAug 21 2019This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern recognition. While MDL was originally ... More
U-Net Training with Instance-Layer NormalizationAug 21 2019Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most ... More
Dialog State Tracking with Reinforced Data AugmentationAug 21 2019Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data. In this paper, we address this difficulty by proposing a reinforcement learning (RL) based framework for data augmentation that can ... More
Decentralized Federated Learning: A Segmented Gossip ApproachAug 21 2019The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited ... More
Boosting the Rating Prediction with Click Data and Textual ContentsAug 21 2019Matrix factorization (MF) is one of the most efficient methods for rating predictions. MF learns user and item representations by factorizing the user-item rating matrix. Further, textual contents are integrated to conventional MF to address the cold-start ... More
Restricted Recurrent Neural NetworksAug 21 2019Recurrent Neural Network (RNN) and its variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have become standard building blocks for learning online data of sequential nature in many research areas, including natural language ... More
Improved Cardinality Estimation by Learning Queries Containment RatesAug 21 2019The containment rate of query Q1 in query Q2 over database D is the percentage of Q1's result tuples over D that are also in Q2's result over D. We directly estimate containment rates between pairs of queries over a specific database. For this, we use ... More
A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography ComponentAug 21 2019Recently, deep learning, which uses Deep Neural Networks (DNN), plays an important role in many fields. A secure neural network model with a secure training/inference scheme is indispensable to many applications. To accomplish such a task usually needs ... More
Hebbian Graph EmbeddingsAug 21 2019Representation learning has recently been successfully used to create vector representations of entities in language learning, recommender systems and in similarity learning. Graph embeddings exploit the locality structure of a graph and generate embeddings ... More
Asymmetric Non-local Neural Networks for Semantic SegmentationAug 21 2019The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, ... More
Asymmetric Non-local Neural Networks for Semantic SegmentationAug 21 2019Aug 22 2019The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, ... More
Deep Reinforcement Learning for Foreign Exchange TradingAug 21 2019Reinforcement learning can interact with the environment and is suitable for applications in decision control systems. Therefore, we used the reinforcement learning method to establish a foreign exchange transaction, avoiding the long-standing problem ... More
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial AttacksAug 21 2019Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by adding maliciously crafted perturbations to the benign inputs. As a growing ... More
Saccader: Improving Accuracy of Hard Attention Models for VisionAug 20 2019Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, they are often regarded as black boxes. Because they compute a nonlinear function of the entire input image, their decisions ... More
AdaCliP: Adaptive Clipping for Private SGDAug 20 2019Privacy preserving machine learning algorithms are crucial for learning models over user data to protect sensitive information. Motivated by this, differentially private stochastic gradient descent (SGD) algorithms for training machine learning models ... More
How to gamble with non-stationary $\mathcal{X}$-armed bandits and have no regretsAug 20 2019In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem ... More
How to gamble with non-stationary $\mathcal{X}$-armed bandits and have no regretsAug 20 2019Aug 22 2019In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem ... More
P2L: Predicting Transfer Learning for Images and Semantic RelationsAug 20 2019Transfer learning enhances learning across tasks, by leveraging previously learned representations -- if they are properly chosen. We describe an efficient method to accurately estimate the appropriateness of a previously trained model for use in a new ... More
Detecting Gas Vapor Leaks Using Uncalibrated SensorsAug 20 2019Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors. In this work, we use different time-series data sets obtained by infra-red and E-nose sensors in order to detect Volatile ... More
Reinforcement Learning is not a Causal problemAug 20 2019We use an analogy between non-isomorphic mathematical structures defined over the same set and the algebras induced by associative and causal levels of information in order to argue that Reinforcement Learning, in its current formulation, is not a causal ... More
Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient DescentAug 20 2019Stochastic Gradient Descent (SGD) methods are prominent for training machine learning and deep learning models. The performance of these techniques depends on their hyperparameter tuning over time and varies for different models and problems. Manual adjustment ... More
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentationAug 20 2019Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. ... More
Learning document embeddings along with their uncertaintiesAug 20 2019Majority of the text modelling techniques yield only point estimates of document embeddings and lack in capturing the uncertainty of the estimates. These uncertainties give a notion of how well the embeddings represent a document. We present Bayesian ... More
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel AccessAug 20 2019To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt ... More
Developing Creative AI to Generate Sculptural ObjectsAug 20 2019We explore the intersection of human and machine creativity by generating sculptural objects through machine learning. This research raises questions about both the technical details of automatic art generation and the interaction between AI and people, ... More
ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional FeaturesAug 20 2019In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory. These techniques often require researchers to engineer abstract "features" that encode chemical concepts into ... More
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher ProcessesAug 20 2019The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent. One exception is the PAC-Bayes theorem of Kakade, Sridharan, and Tewari (2008), which is established via Rademacher complexity theory by viewing Gibbs classifiers ... More
Robust Graph Neural Network Against Poisoning Attacks via Transfer LearningAug 20 2019Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable modifications on graph topology or nodal features can significantly reduce ... More
Phrase Localization Without Paired Training ExamplesAug 20 2019Localizing phrases in images is an important part of image understanding and can be useful in many applications that require mappings between textual and visual information. Existing work attempts to learn these mappings from examples of phrase-image ... More
Data Management for Causal Algorithmic FairnessAug 20 2019Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflects discrimination, suggesting a data management problem. In this paper, we ... More
Hotel Recommendation SystemAug 20 2019One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we ... More
Hotel Recommendation SystemAug 20 2019Aug 21 2019One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we ... More
LXMERT: Learning Cross-Modality Encoder Representations from TransformersAug 20 2019Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations ... More