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Bayesian inference for bivariate ranksFeb 09 2018A recommender system based on ranks is proposed, where an expert's ranking of a set of objects and a user's ranking of a subset of those objects are combined to make a prediction of the user's ranking of all objects. The rankings are assumed to be induced ... More

Algorithmic Bidding for Virtual Trading in Electricity MarketsFeb 08 2018We consider the problem of optimal bidding for virtual trading in two-settlement electricity markets. A virtual trader aims to arbitrage on the differences between day-ahead and real-time market prices; both prices, however, are random and unknown to ... More

Learning and Querying Fast Generative Models for Reinforcement LearningFeb 08 2018A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations, so-called ... More

Statistical Learnability of Generalized Additive Models based on Total Variation RegularizationFeb 08 2018A generalized additive model (GAM, Hastie and Tibshirani (1987)) is a nonparametric model by the sum of univariate functions with respect to each explanatory variable, i.e., $f({\mathbf x}) = \sum f_j(x_j)$, where $x_j\in\mathbb{R}$ is $j$-th component ... More

Stochastic subgradient method converges at the rate $O(k^{-1/4})$ on weakly convex functionsFeb 08 2018We prove that the projected stochastic subgradient method, applied to a weakly convex problem, drives the gradient of the Moreau envelope to zero at the rate $O(k^{-1/4})$.

Learning Sparse Wavelet RepresentationsFeb 08 2018In this work we propose a method for learning wavelet filters directly from data. We accomplish this by framing the discrete wavelet transform as a modified convolutional neural network. We introduce an autoencoder wavelet transform network that is trained ... More

State Compression of Markov Processes via Empirical Low-Rank EstimationFeb 08 2018Model reduction is a central problem in analyzing complex systems and high-dimensional data. We study the state compression of finite-state Markov process from its empirical trajectories. We adopt a low-rank model which is motivated by the state aggregation ... More

Online Learning: A Comprehensive SurveyFeb 08 2018Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a ... More

Neural Network Renormalization GroupFeb 08 2018We present a variational renormalization group approach using deep generative model composed of bijectors. The model can learn hierarchical transformations between physical variables and renormalized collective variables. It can directly generate statistically ... More

Transductive Adversarial Networks (TAN)Feb 08 2018Transductive Adversarial Networks (TAN) is a novel domain-adaptation machine learning framework that is designed for learning a conditional probability distribution on unlabelled input data in a target domain, while also only having access to: (1) easily ... More

Learning Inductive Biases with Simple Neural NetworksFeb 08 2018People use rich prior knowledge about the world in order to efficiently learn new concepts. These priors - also known as "inductive biases" - pertain to the space of internal models considered by a learner, and they help the learner make inferences that ... More

Completely Distributed Power Allocation using Deep Neural Network for Device to Device communication Underlaying LTEFeb 08 2018Device to device (D2D) communication underlaying LTE can be used to distribute traffic loads of eNBs. However, a conventional D2D link is controlled by an eNB, and it still remains burdens to the eNB. We propose a completely distributed power allocation ... More

Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator AbstractionFeb 08 2018To overcome the limitations of Neural Programmer-Interpreters (NPI) in its universality and learnability, we propose the incorporation of combinator abstraction into neural programing and a new NPI architecture to support this abstraction, which we call ... More

Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural NetsFeb 08 2018The insect olfactory system, which includes the antennal lobe (AL), mushroom body (MB), and ancillary structures, is a relatively simple neural system capable of learning. Its structural features, which are widespread in biological neural systems, process ... More

Geometry Score: A Method For Comparing Generative Adversarial NetworksFeb 07 2018One of the biggest challenges in the research of generative adversarial networks (GANs) is assessing the quality of generated samples and detecting various levels of mode collapse. In this work, we construct a novel measure of performance of a GAN by ... More

Gradient conjugate priors and deep neural networksFeb 07 2018The paper deals with learning the probability distribution of the observed data by artificial neural networks. We suggest a so-called gradient conjugate prior (GCP) update appropriate for neural networks, which is a modification of the classical Bayesian ... More

Tight Lower Bounds for Locally Differentially Private SelectionFeb 07 2018We prove a tight lower bound (up to constant factors) on the sample complexity of any non-interactive local differentially private protocol for optimizing a linear function over the simplex. This lower bound also implies a tight lower bound (again, up ... More

Recognition of Acoustic Events Using Masked Conditional Neural NetworksFeb 07 2018Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms. ... More

VISER: Visual Self-RegularizationFeb 07 2018In this work, we propose the use of large set of unlabeled images as a source of regularization data for learning robust visual representation. Given a visual model trained by a labeled dataset in a supervised fashion, we augment our training samples ... More

Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal CorporaFeb 07 2018Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour. A typical work-flow includes the manual annotation (also called coding) of social ... More

A Game-Theoretic Approach to Design Secure and Resilient Distributed Support Vector MachinesFeb 07 2018Distributed Support Vector Machines (DSVM) have been developed to solve large-scale classification problems in networked systems with a large number of sensors and control units. However, the systems become more vulnerable as detection and defense are ... More

Semi-Amortized Variational AutoencodersFeb 07 2018Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network. While AVI has enabled efficient training of deep generative models such as variational autoencoders (VAE), recent empirical work suggests ... More

Learning One Convolutional Layer with Overlapping PatchesFeb 07 2018We give the first provably efficient algorithm for learning a one hidden layer convolutional network with respect to a general class of (potentially overlapping) patches. Additionally, our algorithm requires only mild conditions on the underlying distribution. ... More

Directly and Efficiently Optimizing Prediction Error and AUC of Linear ClassifiersFeb 07 2018The predictive quality of machine learning models is typically measured in terms of their (approximate) expected prediction error or the so-called Area Under the Curve (AUC) for a particular data distribution. However, when the models are constructed ... 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

Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive ModelsFeb 07 2018We consider the problem in regression analysis of identifying subpopulations that exhibit different patterns of response, where each subpopulation requires a different underlying model. Unlike statistical cohorts, these subpopulations are not known a ... More

Spectral Learning of Binomial HMMs for DNA Methylation DataFeb 07 2018We consider learning parameters of Binomial Hidden Markov Models, which may be used to model DNA methylation data. The standard algorithm for the problem is EM, which is computationally expensive for sequences of the scale of the mammalian genome. Recently ... More

Stochastic Deconvolutional Neural Network Ensemble Training on Generative Pseudo-Adversarial NetworksFeb 07 2018The training of Generative Adversarial Networks is a difficult task mainly due to the nature of the networks. One such issue is when the generator and discriminator start oscillating, rather than converging to a fixed point. Another case can be when one ... More

Improved Oracle Complexity of Variance Reduced Methods for Nonsmooth Convex Stochastic Composition OptimizationFeb 07 2018Feb 08 2018We consider the nonsmooth convex composition optimization problem where the objective is a composition of two finite-sum functions and analyze stochastic compositional variance reduced gradient (\textsf{SCVRG}) methods for them. \textsf{SCVRG} and its ... More

Spectral Image Visualization Using Generative Adversarial NetworksFeb 07 2018Spectral images captured by satellites and radio-telescopes are analyzed to obtain information about geological compositions distributions, distant asters as well as undersea terrain. Spectral images usually contain tens to hundreds of continuous narrow ... More

From Game-theoretic Multi-agent Log Linear Learning to Reinforcement LearningFeb 07 2018Multi-agent Systems (MASs) have found a variety of industrial applications from economics to robotics, owing to their high adaptability, scalability and applicability. However, with the increasing complexity of MASs, multi-agent control has become a challenging ... More

Universal Deep Neural Network CompressionFeb 07 2018Compression of deep neural networks (DNNs) for memory- and computation-efficient compact feature representations becomes a critical problem particularly for deployment of DNNs on resource-limited platforms. In this paper, we investigate lossy compression ... 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

Système de traduction automatique statistique Anglais-ArabeFeb 06 2018Machine translation (MT) is the process of translating text written in a source language into text in a target language. In this article, we present our English-Arabic statistical machine translation system. First, we present the general process for setting ... 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

DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary SupervisionFeb 06 2018Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically ... 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

The steerable graph Laplacian and its application to filtering image data-setsFeb 06 2018In recent years, improvements in various scientific image acquisition techniques gave rise to the need for adaptive processing methods aimed for large data-sets corrupted by noise and deformations. In this work, we consider data-sets of images sampled ... More

Texygen: A Benchmarking Platform for Text Generation ModelsFeb 06 2018We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality ... 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

Near-Optimal Coresets of Kernel Density EstimatesFeb 06 2018We construct near-optimal coresets for kernel density estimate for points in $\mathbb{R^d}$ when the kernel is positive definite. Specifically we show a polynomial time construction for a coreset of size $O(\sqrt{d\log (1/\epsilon)}/\epsilon)$, and we ... More

Re-Weighted Learning for Sparsifying Deep Neural NetworksFeb 05 2018This paper addresses the topic of sparsifying deep neural networks (DNN's). While DNN's are powerful models that achieve state-of-the-art performance on a large number of tasks, the large number of model parameters poses serious storage and computational ... More

Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong ConvexityFeb 05 2018We consider the convex-concave saddle point problem $\min_{x}\max_{y} f(x)+y^\top A x-g(y)$ where $f$ is smooth and convex and $g$ is smooth and strongly convex. We prove that if the coupling matrix $A$ has full column rank, the vanilla primal-dual gradient ... More

Enhancing Multi-Class Classification of Random Forest using Random Vector Functional Neural Network and Oblique Decision SurfacesFeb 05 2018Both neural networks and decision trees are popular machine learning methods and are widely used to solve problems from diverse domains. These two classifiers are commonly used base classifiers in an ensemble framework. In this paper, we first present ... More

Non-Gaussian information from weak lensing data via deep learningFeb 04 2018Feb 06 2018Weak lensing maps contain information beyond two-point statistics on small scales. Much recent work has tried to extract this information through a range of different observables or via nonlinear transformations of the lensing field. Here we train and ... More

MotifNet: a motif-based Graph Convolutional Network for directed graphsFeb 04 2018Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community. Many of such techniques explore the analogy between the graph Laplacian eigenvectors and the ... More

Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesFeb 04 2018Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. Here we propose a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal ... 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

A Generative Model for Natural Sounds Based on Latent Force ModellingFeb 02 2018Recent advances in analysis of subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitudes to be a crucial component of perception. Probabilistic latent variable analysis is particularly revealing, but ... More

Representation Learning for Resource Usage PredictionFeb 02 2018Creating a model of a computer system that can be used for tasks such as predicting future resource usage and detecting anomalies is a challenging problem. Most current systems rely on heuristics and overly simplistic assumptions about the workloads and ... 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

Analysis of Fast Alternating Minimization for Structured Dictionary LearningFeb 01 2018Methods exploiting sparsity have been popular in imaging and signal processing applications including compression, denoising, and imaging inverse problems. Data-driven approaches such as dictionary learning and transform learning enable one to discover ... More

Sensitivity Sampling Over Dynamic Geometric Data Streams with Applications to $k$-ClusteringFeb 01 2018Sensitivity based sampling is crucial for constructing nearly-optimal coreset for $k$-means / median clustering. In this paper, we provide a novel data structure that enables sensitivity sampling over a dynamic data stream, where points from a high dimensional ... More

On Polynomial time Constructions of Minimum Height Decision TreeFeb 01 2018In this paper we study a polynomial time algorithms that for an input $A\subseteq {B_m}$ outputs a decision tree for $A$ of minimum depth. This problem has many applications that include, to name a few, computer vision, group testing, exact learning from ... More

Deep Learning of Constrained Autoencoders for Enhanced Understanding of DataJan 31 2018Feb 03 2018Unsupervised feature extractors are known to perform an efficient and discriminative representation of data. Insight into the mappings they perform and human ability to understand them, however, remain very limited. This is especially prominent when multilayer ... More

Kernel Distillation for Gaussian ProcessesJan 31 2018Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference ... More

Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory NetworksJan 30 2018Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses ... 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

Nonlinear Dimensionality Reduction on GraphsJan 29 2018In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient ... 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

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional NetworksJan 29 2018It is desirable to train convolutional networks (CNNs) to run more efficiently during inference. In many cases however, the computational budget that the system has for inference cannot be known beforehand during training, or the inference budget is dependent ... More

Correlated Components Analysis --- Extracting Reliable Dimensions in Multivariate DataJan 26 2018How does one find data dimensions that are reliably expressed across repetitions? For example, in neuroscience one may want to identify combinations of brain signals that are reliably activated across multiple trials or subjects. For a clinical assessment ... More

Deep Learning Angiography (DLA): Three-dimensional C-arm Cone Beam CT Angiography Using Deep LearningJan 26 2018Background and Purpose: Our purpose was to develop a deep learning angiography (DLA) method to generate 3D cerebral angiograms from a single contrast-enhanced acquisition. Material and Methods: Under an approved IRB protocol 105 3D-DSA exams were randomly ... More

21 Million Opportunities: A 19 Facility Investigation of Factors Affecting Hand Hygiene Compliance via Linear Predictive ModelsJan 26 2018This large-scale study, consisting of 21.3 million hand hygiene opportunities from 19 distinct facilities in 10 different states, uses linear predictive models to expose factors that may affect hand hygiene compliance. We examine the use of features such ... More

Deep Learning in Pharmacogenomics: From Gene Regulation to Patient StratificationJan 25 2018This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: (1) identification of novel regulatory variants located in noncoding domains and their function as applied to pharmacoepigenomics; (2) ... More

CommanderSong: A Systematic Approach for Practical Adversarial Voice RecognitionJan 24 2018ASR (automatic speech recognition) systems like Siri, Alexa, Google Voice or Cortana has become quite popular recently. One of the key techniques enabling the practical use of such systems in people's daily life is deep learning. Though deep learning ... More

Learning Symmetry and Low-energy LocomotionJan 24 2018Jan 25 2018Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific knowledge to guide the motion generation algorithms. Deep reinforcement ... More

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep LearningJan 24 2018Jan 30 2018The Deep Learning (DL) community sees many novel topologies published each year. Achieving high performance on each new topology remains challenging, as each requires some level of manual effort. This issue is compounded by the proliferation of frameworks ... 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

Machine learning in APOGEE: Unsupervised spectral classification with $K$-meansJan 24 2018Feb 08 2018The data volume generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and unsupervised clustering ... More

Scalable and accurate deep learning for electronic health recordsJan 24 2018Jan 26 2018Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized ... 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

Generalized two-dimensional linear discriminant analysis with regularizationJan 23 2018Recent advances show that two-dimensional linear discriminant analysis (2DLDA) is a successful matrix based dimensionality reduction method. However, 2DLDA may encounter the singularity issue theoretically and the sensitivity to outliers. In this paper, ... 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

Offline A/B testing for Recommender SystemsJan 22 2018Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data. We focus on evaluation methods that compute an estimator of the potential uplift in revenue that could generate this new ... More

Optimal Rates for Spectral-regularized Algorithms with Least-Squares Regression over Hilbert SpacesJan 20 2018In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We investigate a class of spectral-regularized algorithms, including ridge regression, ... 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

An Overview of Machine TeachingJan 18 2018In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is presented as varying along a dimension. The collection of dimensions then form the problem space of machine teaching, such that existing teaching problems can be ... More

On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine LearningJan 16 2018In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive attribute (e.g., race or gender). In this paper, we present an information-theoretic ... 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

Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput ComputingJan 12 2018Grid computing systems require innovative methods and tools to identify cybersecurity incidents and perform autonomous actions i.e. without administrator intervention. They also require methods to isolate and trace job payload activity in order to protect ... More

A3T: Adversarially Augmented Adversarial TrainingJan 12 2018Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier. Most classification models, including ... More

Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis CaseJan 11 2018Latent variable models can be used to probabilistically "fill-in" missing data entries. The variational autoencoder architecture (Kingma and Welling, 2014; Rezende et al., 2014) includes a "recognition" or "encoder" network that infers the latent variables ... More

Improved asynchronous parallel optimization analysis for stochastic incremental methodsJan 11 2018Jan 12 2018As datasets continue to increase in size and multi-core computer architectures are developed, asynchronous parallel optimization algorithms become more and more essential to the field of Machine Learning. Unfortunately, conducting the theoretical analysis ... More

Blessing of dimensionality: mathematical foundations of the statistical physics of dataJan 10 2018The concentration of measure phenomena were discovered as the mathematical background of statistical mechanics at the end of the XIX - beginning of the XX century and were then explored in mathematics of the XX-XXI centuries. At the beginning of the XXI ... More

Approximate FPGA-based LSTMs under Computation Time ConstraintsJan 07 2018Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art accuracy in several emerging Artificial Intelligence tasks. However, the models are becoming increasingly demanding in terms of computational ... More

High-throughput, high-resolution Generated Adversarial Network MicroscopyJan 07 2018We for the first time combine generated adversarial network (GAN) with wide-field light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, ... More

Object segmentation in depth maps with one user click and a synthetically trained fully convolutional networkJan 04 2018With more and more household objects built on planned obsolescence and consumed by a fast-growing population, hazardous waste recycling has become a critical challenge. Given the large variability of household waste, current recycling platforms mostly ... 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

Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural NetworkDec 29 2017Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempt to determine a set of critical proteins that are associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Learning ... 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

Robust Covariate Shift Prediction with General Losses and Feature ViewsDec 28 2017Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions. Unfortunately, common methods for addressing covariate shift by trying to remove the bias ... More

Directional Statistics and Filtering Using libDirectionalDec 28 2017In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions. ... 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

Dropout Feature Ranking for Deep Learning ModelsDec 22 2017Deep neural networks are a promising technology achieving state-of-the-art results in biological and healthcare domains. Unfortunately, DNNs are notorious for their non-interpretability. Clinicians are averse to black boxes and thus interpretability is ... More

Adaptive Stochastic Dual Coordinate Ascent for Conditional Random FieldsDec 22 2017This work investigates training Conditional Random Fields (CRF) by Stochastic Dual Coordinate Ascent (SDCA). SDCA enjoys a linear convergence rate and a strong empirical performance for independent classification problems. However, it has never been used ... More

Note on Attacking Object Detectors with Adversarial StickersDec 21 2017Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks. However, deep learning algorithms are known to be vulnerable to adversarial examples. These adversarial inputs are created such that, ... More

Combining Static and Dynamic Features for Multivariate Sequence ClassificationDec 20 2017Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning ... More