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PLUME: Polyhedral Learning Using Mixture of ExpertsApr 22 2019In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers. We learn the parameters of the classifierusing an expectation maximization algorithm. Wederive the generalization bounds of the proposedapproach. Through ... More
Tracking and Improving Information in the Service of FairnessApr 22 2019As algorithmic prediction systems have become widespread, fears that these systems may inadvertently discriminate against members of underrepresented populations have grown. With the goal of understanding fundamental principles that underpin the growing ... More
Adversarial Dropout for Recurrent Neural NetworksApr 22 2019Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques for RNNs were introduced to respond to these demands, but we conjecture ... More
Revisiting Multi-Step Nonlinearity Compensation with Machine LearningApr 22 2019For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show that carefully designed multi-step approaches can lead to better performance-complexity ... More
A Unified Framework for Structured Graph Learning via Spectral ConstraintsApr 22 2019Graph learning from data represents a canonical problem that has received substantial attention in the literature. However, insufficient work has been done in incorporating prior structural knowledge onto the learning of underlying graphical models from ... More
On Learning Non-Convergent Short-Run MCMC Toward Energy-Based ModelApr 22 2019This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, non-mixing, and non-persistent short-run MCMC toward the current model, ... More
hf0: A hybrid pitch extraction method for multimodal voiceApr 22 2019Pitch or fundamental frequency (f0) extraction is a fundamental problem studied extensively for its potential applications in speech and clinical applications. In literature, explicit mode specific (modal speech or singing voice or emotional/ expressive ... More
Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz ProblemsApr 22 2019The (global) Lipschitz smoothness condition is crucial in establishing the convergence theory for most optimization methods. Unfortunately, most machine learning and signal processing problems are not Lipschitz smooth. This motivates us to generalize ... More
HCFContext: Smartphone Context Inference via Sequential History-based Collaborative FilteringApr 21 2019Mobile context determination is an important step for many context aware services such as location-based services, enterprise policy enforcement, building or room occupancy detection for power or HVAC operation, etc. Especially in enterprise scenarios ... More
TiK-means: $K$-means clustering for skewed groupsApr 21 2019The $K$-means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a $K$-means type algorithm that assigns observations to groups while estimating their skewness-transformation parameters. ... More
Obfuscation for Privacy-preserving Syntactic ParsingApr 21 2019The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data. We introduce an idea for a privacy-preserving transformation on natural language data, ... More
GAN-based Generation and Automatic Selection of Explanations for Neural NetworksApr 21 2019One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising the model input (e.g., an image) to maximally activate specific neurons. However, this requires ... More
LIBS2ML: A Library for Scalable Second Order Machine Learning AlgorithmsApr 20 2019LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i.e., big data problems in machine learning. LIBS2ML has been developed using MEX files, i.e., C++ with MATLAB/Octave interface to take the advantage ... More
Waterfall Bandits: Learning to Sell Ads OnlineApr 20 2019A popular approach to selling online advertising is by a waterfall, where a publisher makes sequential price offers to ad networks for an inventory, and chooses the winner in that order. The publisher picks the order and prices to maximize her revenue. ... More
Minimax Optimal Online Stochastic Learning for Sequences of Convex Functions under Sub-Gradient Observation FailuresApr 19 2019We study online convex optimization under stochastic sub-gradient observation faults, where we introduce adaptive algorithms with minimax optimal regret guarantees. We specifically study scenarios where our sub-gradient observations can be noisy or even ... More
On the Convergence of Adam and BeyondApr 19 2019Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared ... More
Reliable Multi-label Classification: Prediction with Partial AbstentionApr 19 2019In contrast to conventional (single-label) classification, the setting of multi-label classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a ... More
Uncertainty about Uncertainty: Near-Optimal Adaptive Algorithms for Estimating Binary Mixtures of Unknown CoinsApr 19 2019Given a mixture between two populations of coins, "positive" coins that have (unknown and potentially different) probabilities of heads $\geq\frac{1}{2}+\Delta$ and negative coins with probabilities $\leq\frac{1}{2}-\Delta$, we consider the task of estimating ... More
Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional DataApr 19 2019This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call \emph{centered kernel ridge regression} (CKRR), also known in the literature as kernel ridge regression with offset. This modified technique is ... More
Hierarchical Meta LearningApr 19 2019Meta learning is a promising solution to few-shot learning problems. However, existing meta learning methods are restricted to the scenarios where training and application tasks share the same out-put structure. To obtain a meta model applicable to the ... More
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like processApr 19 2019We consider deep networks, trained via stochastic gradient descent to minimize L2 loss, with the training labels perturbed by independent noise at each iteration. We characterize the behavior of the training dynamics near any parameter vector that achieves ... More
EmbraceNet: A robust deep learning architecture for multimodal classificationApr 19 2019Classification using multimodal data arises in many machine learning applications. It is crucial not only to model cross-modal relationship effectively but also to ensure robustness against loss of part of data or modalities. In this paper, we propose ... More
Emergence of Compositional Language with Deep Generational TransmissionApr 19 2019Consider a collaborative task that requires communication. Two agents are placed in an environment and must create a language from scratch in order to coordinate. Recent work has been interested in what kinds of languages emerge when deep reinforcement ... More
Semi-Supervised First-Person Activity Recognition in Body-Worn VideoApr 19 2019Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer ... More
Online Active Learning: Label Complexity vs. Classification ErrorsApr 19 2019We study online active learning for classifying streaming instances. At each time, the decision maker decides whether to query for the label of the current instance and, in the event of no query, self labels the instance. The objective is to minimize ... More
ProductNet: a Collection of High-Quality Datasets for Product Representation LearningApr 18 2019ProductNet is a collection of high-quality product datasets for better product understanding. Motivated by ImageNet, ProductNet aims at supporting product representation learning by curating product datasets of high quality with properly chosen taxonomy. ... More
When is a Prediction Knowledge?Apr 18 2019Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behaviour, and time. This work can be seen not only as a collection of architectural ... More
Graph Element Networks: adaptive, structured computation and memoryApr 18 2019We explore the use of graph neural networks (GNNs) to model spatial processes in which there is a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on ... More
SpecAugment: A Simple Data Augmentation Method for Automatic Speech RecognitionApr 18 2019We present SpecAugment, a simple data augmentation method for speech recognition. SpecAugment is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients). The augmentation policy consists of warping the features, masking ... More
Decoding Molecular Graph Embeddings with Reinforcement LearningApr 18 2019We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previously for graph-to-graph autoencoding, but these approaches require sophisticated ... More
Efficient two-sample functional estimation and the super-oracle phenomenonApr 18 2019We consider the estimation of two-sample integral functionals, of the type that occur naturally, for example, when the object of interest is a divergence between unknown probability densities. Our first main result is that, in wide generality, a weighted ... More
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane GraphsApr 18 2019Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable. In this paper, we apply deep neural networks to the nonlinear spatiotemporal physics ... More
Deep Residual Auto-Encoders for Expectation Maximization-based Dictionary LearningApr 18 2019Convolutional dictionary learning (CDL) has become a popular method for learning sparse representations from data. State-of-the-art algorithms perform dictionary learning (DL) through an optimization-based alternating-minimization procedure that comprises ... More
Influence Maximization via Representation LearningApr 18 2019Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion ... More
A Data Driven Approach for Motion Planning of Autonomous Driving Under Complex ScenarioApr 18 2019To guarantee the safe and efficient motion planning of autonomous driving under dynamic traffic environment, the autonomous vehicle should be equipped with not only the optimal but also a long term efficient policy to deal with complex scenarios. The ... More
edGNN: a Simple and Powerful GNN for Directed Labeled GraphsApr 18 2019The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings. Building on previous work, we theoretically show that edGNN, our model for directed labeled ... More
Convolutional neural networks: a magic bullet for gravitational-wave detection?Apr 18 2019In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature ... More
A kernel-based method for coarse graining complex dynamical systemsApr 18 2019We present a novel kernel-based machine learning algorithm for identifying the low-dimensional geometry of the effective dynamics of high-dimensional multiscale stochastic systems. Recently, the authors developed a mathematical framework for the computation ... More
Disentangled Representation Learning with Information Maximizing AutoencoderApr 18 2019Learning disentangled representation from any unlabelled data is a non-trivial problem. In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information ... More
Explaining Deep Classification of Time-Series Data with Learned PrototypesApr 18 2019The emergence of deep learning networks raises a need for algorithms to explain their decisions so that users and domain experts can be confident using algorithmic recommendations for high-risk decisions. In this paper we leverage the information-rich ... More
Reducing Noise in GAN Training with Variance Reduced ExtragradientApr 18 2019Using large mini-batches when training generative adversarial networks (GANs) has been recently shown to significantly improve the quality of the generated samples. This can be seen as a simple but computationally expensive way of reducing the noise of ... More
One-dimensional Deep Image Prior for Time Series Inverse ProblemsApr 18 2019We extend the Deep Image Prior (DIP) framework to one-dimensional signals. DIP is using a randomly initialized convolutional neural network (CNN) to solve linear inverse problems by optimizing over weights to fit the observed measurements. Our main finding ... More
On Low-rank Trace Regression under General Sampling DistributionApr 18 2019A growing number of modern statistical learning problems involve estimating a large number of parameters from a (smaller) number of observations. In a subset of these problems (matrix completion, matrix compressed sensing, and multi-task learning) the ... More
SPONGE: A generalized eigenproblem for clustering signed networksApr 18 2019We introduce a principled and theoretically sound spectral method for $k$-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values. Our approach is motivated by social balance theory, where the ... More
Inpatient2Vec: Medical Representation Learning for InpatientsApr 18 2019Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be ... More
Gotta Catch 'Em All: Using Concealed Trapdoors to Detect Adversarial Attacks on Neural NetworksApr 18 2019Deep neural networks are vulnerable to adversarial attacks. Numerous efforts have focused on defenses that either try to patch `holes' in trained models or try to make it difficult or costly to compute adversarial examples exploiting these holes. In our ... More
A New Class of Time Dependent Latent Factor Models with ApplicationsApr 18 2019In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as temperature, humidity, power consumption and noise levels. These random, observed ... More
Deep Representation Learning for Social Network AnalysisApr 18 2019Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other ... More
Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel SurveyApr 18 2019We develop ensemble Convolutional Neural Networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models ... More
Memory-Sample Tradeoffs for Linear Regression with Small ErrorApr 18 2019We consider the problem of performing linear regression over a stream of $d$-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. ... More
Generative Model for Zero-Shot Sketch-Based Image RetrievalApr 18 2019We present a probabilistic model for Sketch-Based Image Retrieval (SBIR) where, at retrieval time, we are given sketches from novel classes, that were not present at training time. Existing SBIR methods, most of which rely on learning class-wise correspondences ... More
Matrix Completion With Selective SamplingApr 17 2019Matrix completion is a classical problem in data science wherein one attempts to reconstruct a low-rank matrix while only observing some subset of the entries. Previous authors have phrased this problem as a nuclear norm minimization problem. Almost all ... More
Robust Exploration with Tight Bayesian Plausibility SetsApr 17 2019Optimism about the poorly understood states and actions is the main driving force of exploration for many provably-efficient reinforcement learning algorithms. We propose optimism in the face of sensible value functions (OFVF)- a novel data-driven Bayesian ... More
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural NetworksApr 17 2019Neural Network classifiers have been used successfully in a wide range of applications. However, their underlying assumption of attack free environment has been defied by adversarial examples. Researchers tried to develop defenses; however, existing approaches ... More
DeepNovoV2: Better de novo peptide sequencing with deep learningApr 17 2019We introduce DeepNovoV2, the state-of-the-art neural networks based model for de novo peptide sequencing. Contrary to existing models like DeepNovo or DeepMatch which represents each spectrum as a long sparse vector, in DeepNovoV2, we propose to directly ... More
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUsApr 17 2019The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients' movements, detachment of monitoring sensors, or different sources of noise ... More
Learning Interpretable Disentangled Representations using Adversarial VAEsApr 17 2019Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact ... More
LCC: Learning to Customize and Combine Neural Networks for Few-Shot LearningApr 17 2019Meta-learning has been shown to be an effective strategy for few-shot learning. The key idea is to leverage a large number of similar few-shot tasks in order to meta-learn how to best initiate a (single) base-learner for novel few-shot tasks. While meta-learning ... More
A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration ConceptsApr 17 2019Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses ... More
Off-Policy Policy Gradient with State Distribution CorrectionApr 17 2019We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approaches have generally ignored the mismatch between the distribution of states ... More
Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs ModelsApr 17 2019The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis ... More
Defensive Quantization: When Efficiency Meets RobustnessApr 17 2019Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial ... More
FLARe: Forecasting by Learning Anticipated RepresentationsApr 17 2019Computational models that forecast the progression of Alzheimer's disease at the patient level are extremely useful tools for identifying high risk cohorts for early intervention and treatment planning. The state-of-the-art work in this area proposes ... More
SACOBRA with Online Whitening for Solving Optimization Problems with High ConditioningApr 17 2019Real-world optimization problems often have expensive objective functions in terms of cost and time. It is desirable to find near-optimal solutions with very few function evaluations. Surrogate-assisted optimizers tend to reduce the required number of ... More
Casting Light on Invisible Cities: Computationally Engaging with Literary CriticismApr 17 2019Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains a challenge in digital humanities. While most ... More
Vid2Game: Controllable Characters Extracted from Real-World VideosApr 17 2019We are given a video of a person performing a certain activity, from which we extract a controllable model. The model generates novel image sequences of that person, according to arbitrary user-defined control signals, typically marking the displacement ... More
Dynamic Evaluation of Transformer Language ModelsApr 17 2019This research note combines two methods that have recently improved the state of the art in language modeling: Transformers and dynamic evaluation. Transformers use stacked layers of self-attention that allow them to capture long range dependencies in ... More
Relay: A High-Level IR for Deep LearningApr 17 2019Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing. Extending these frameworks to accommodate the rapidly diversifying landscape of DL ... More
Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical SystemsApr 17 2019This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in stochastic ... More
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor FailuresApr 17 2019Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, ... More
A large-scale field test on word-image classification in large historical document collections using a traditional and two deep-learning methodsApr 17 2019This technical report describes a practical field test on word-image classification in a very large collection of more than 300 diverse handwritten historical manuscripts, with 1.6 million unique labeled images and more than 11 million images used in ... More
Bonsai - Diverse and Shallow Trees for Extreme Multi-label ClassificationApr 17 2019Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a shallow tree-based algorithm, called Bonsai, which promotes diversity of the label space ... More
X-Armed Bandits: Optimizing Quantiles and Other RisksApr 17 2019We propose and analyze StoROO, an algorithm for risk optimization on stochastic black-box functions derived from StoOO. Motivated by risk-averse decision making fields like agriculture, medicine, biology or finance, we do not focus on the mean payoff ... More
Compositional Network EmbeddingApr 17 2019Apr 18 2019Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding ... More
Compositional Network EmbeddingApr 17 2019Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding ... More
Deep learning investigation for chess player attention prediction using eye-tracking and game dataApr 17 2019This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical ... More
2D Car Detection in Radar Data with PointNetsApr 17 2019For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for ... More
Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networksApr 17 2019Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design. For the purpose, we propose a novel approach for predicting DTI using a GNN that directly incorporates the 3D structure of a protein-ligand complex. We also apply ... More
Rogue-Gym: A New Challenge for Generalization in Reinforcement LearningApr 17 2019This paper presents Rogue-Gym, that enables agents to learn and play a subset of the original Rogue game with the OpenAI Gym interface. In roguelike games, a player explores a dungeon where each floor is two dimensional grid maze with enemies, golds, ... More
A Survey on Traffic Signal Control MethodsApr 17 2019Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on ... More
Batched Stochastic Bayesian Optimization via Combinatorial Constraints DesignApr 17 2019In many high-throughput experimental design settings, such as those common in biochemical engineering, batched queries are more cost effective than one-by-one sequential queries. Furthermore, it is often not possible to directly choose items to query. ... More
An Online Learning Approach for Dengue Fever ClassificationApr 17 2019This paper introduces a novel approach for dengue fever classification based on online learning paradigms. The proposed approach is suitable for practical implementation as it enables learning using only a few training samples. With time, the proposed ... More
Adversarial Defense Through Network Profiling Based Path ExtractionApr 17 2019Recently, researchers have started decomposing deep neural network models according to their semantics or functions. Recent work has shown the effectiveness of decomposed functional blocks for defending adversarial attacks, which add small input perturbation ... More
Self-Attention Graph PoolingApr 17 2019Apr 18 2019Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution ... More
Self-Attention Graph PoolingApr 17 2019Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution ... More
Text Classification Algorithms: A SurveyApr 17 2019In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches ... More
Forecasting with time series imagingApr 17 2019Feature-based time series representation has attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model selection and model averaging has been an emerging research focus ... More
Neural Painters: A learned differentiable constraint for generating brushstroke paintingsApr 17 2019We explore neural painters, a generative model for brushstrokes learned from a real non-differentiable and non-deterministic painting program. We show that when training an agent to "paint" images using brushstrokes, using a differentiable neural painter ... More
Sparseout: Controlling Sparsity in Deep NetworksApr 17 2019Dropout is commonly used to help reduce overfitting in deep neural networks. Sparsity is a potentially important property of neural networks, but is not explicitly controlled by Dropout-based regularization. In this work, we propose Sparseout a simple ... More
Neural Message Passing for Multi-Label ClassificationApr 17 2019Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to efficiently ... More
Inductive Graph Representation Learning with Recurrent Graph Neural NetworksApr 17 2019In this paper, we study the problem of node representation learning with graph neural networks. We present a graph neural network class named recurrent graph neural network (RGNN), that address the shortcomings of prior methods. By using recurrent units ... More
People infer recursive visual concepts from just a few examplesApr 17 2019Machine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that ... More
Multi-Interest Network with Dynamic Routing for Recommendation at TmallApr 17 2019Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items. The matching stage retrieves candidate items relevant to user interests, while the ranking stage sorts ... More
Fast object detection in compressed JPEG ImagesApr 16 2019Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been achieved with numerous industrial applications. Most of these deep learning models rely on RGB images ... More
SynC: A Unified Framework for Generating Synthetic Population with Gaussian CopulaApr 16 2019Synthetic population generation is the process of combining multiple socioeonomic and demographic datasets from various sources and at different granularity, and downscaling them to an individual level. Although it is a fundamental step for many data ... More
Machine learning for early prediction of circulatory failure in the intensive care unitApr 16 2019Apr 19 2019Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and ... More
Machine learning for early prediction of circulatory failure in the intensive care unitApr 16 2019Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and ... More
Reducing Adversarial Example Transferability Using Gradient RegularizationApr 16 2019Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples transfer between different architectures trained on different datasets. ... More
Detection and Prediction of Cardiac Anomalies Using Wireless Body Sensors and Bayesian Belief NetworksApr 16 2019Intricating cardiac complexities are the primary factor associated with healthcare costs and the highest cause of death rate in the world. However, preventive measures like the early detection of cardiac anomalies can prevent severe cardiovascular arrests ... More
DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Ability to Learn Discrete-World DatasetApr 16 2019Is cognition a collection of loosely connected functions tuned to different tasks, or can there be a general learning algorithm? If such an hypothetical general algorithm did exist, tuned to our world, could it adapt seamlessly to a world with different ... More