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EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning | The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious activities from analysis tools. These include a wide range of anti-emulator techn... | false |
Detecting Overfitting via Adversarial Examples | The repeated community-wide reuse of test sets in popular benchmark problems raises doubts about the credibility of reported test-error rates. Verifying whether a learned model is overfitted to a test set is challenging as independent test sets drawn from the same data distribution are usually unavailable, while other... | true |
Application of a Fuzzy Programming Technique to Production Planning in the Textile Industry | Many engineering optimization problems can be considered as linear programming problems where all or some of the parameters involved are linguistic in nature. These can only be quantified using fuzzy sets. The aim of this paper is to solve a fuzzy linear programming problem in which the parameters involved are fuzzy qu... | false |
A Consistent Regularization Approach for Structured Prediction | We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. ... | true |
Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss | We propose a modular framework for multi-relational learning via tensor decomposition. In our learning setting, the training data contains multiple types of relationships among a set of objects, which we represent by a sparse three-mode tensor. The goal is to predict the values of the missing entries. To do so, we mode... | false |
Comparison of ontology alignment algorithms across single matching task via the McNemar test | Ontology alignment is widely used to find the correspondences between different ontologies in diverse fields. After discovering the alignment by methods, several performance scores are available to evaluate them. The scores require the produced alignment by a method and the reference alignment containing the underlying... | false |
Nonparametric Bayesian Storyline Detection from Microtexts | News events and social media are composed of evolving storylines, which capture public attention for a limited period of time. Identifying these storylines would enable many high-impact applications, such as tracking public interest and opinion in ongoing crisis events. However, this requires integrating temporal and l... | false |
Data Analytics using Ontologies of Management Theories: Towards Implementing 'From Theory to Practice' | We explore how computational ontologies can be impactful vis-a-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be impl... | false |
A Question Answering Approach to Emotion Cause Extraction | Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identifica... | true |
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs | This paper presents results of our experiments using the Ubuntu Dialog Corpus - the largest publicly available multi-turn dialog corpus. First, we use an in-house implementation of previously reported models to do an independent evaluation using the same data. Second, we evaluate the performances of various LSTMs, Bi-L... | false |
Attentive Recurrent Comparators | Attentive Recurrent Comparators (ARCs) are a novel class of neural networks built with attention and recurrence that learn to estimate the similarity of a set of objects by cycling through them and making observations. The observations made in one object are conditioned on the observations made in all the other objects... | false |
Video Frame Synthesis using Deep Voxel Flow | We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation). This problem is challenging because video appearance and motion can be highly complex. Traditional optical-flow-based solutions often fail where flow es... | false |
Training Dependency Parsers with Partial Annotation | Recently, these has been a surge on studying how to obtain partially annotated data for model supervision. However, there still lacks a systematic study on how to train statistical models with partial annotation (PA). Taking dependency parsing as our case study, this paper describes and compares two straightforward app... | false |
Fast greedy algorithms for dictionary selection with generalized sparsity constraints | In dictionary selection, several atoms are selected from finite candidates that successfully approximate given data points in the sparse representation. We propose a novel efficient greedy algorithm for dictionary selection. Not only does our algorithm work much faster than the known methods, but it can also handle mor... | true |
Modelling Electricity Consumption in Office Buildings: An Agent Based Approach | In this paper, we develop an agent-based model which integrates four important elements, i.e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, to simulate the electricity consumption in office buildings. Based on a case study,... | false |
Inverse Density as an Inverse Problem: the Fredholm Equation Approach | In this paper we address the problem of estimating the ratio $\frac{q}{p}$ where $p$ is a density function and $q$ is another density, or, more generally an arbitrary function. Knowing or approximating this ratio is needed in various problems of inference and integration, in particular, when one needs to average a func... | true |
Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method and Modified Cuckoo Search | This paper investigates a new method for improving the learning algorithm of Mixture of Experts (ME) model using a hybrid of Modified Cuckoo Search (MCS) and Conjugate Gradient (CG) as a second order optimization technique. The CG technique is combined with Back-Propagation (BP) algorithm to yield a much more efficient... | false |
Pruning Random Forests for Prediction on a Budget | We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose pruning RFs as a novel 0-1 integer program with linear constraints that encourages feature re-use. We establish total unimodularity of the con... | true |
Contextual Stochastic Block Models | We provide the first information theoretical tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities. Our work bridges recent theoretical breakthroughs in detection of latent community structure without node... | true |
Learning Disentangled Representation for Robust Person Re-identification | We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. The key challenge is to learn person representations robust to intra-class variations, as different persons can have the same attribute and the same person's ... | true |
CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations | We present a submission to the CogALex 2016 shared task on the corpus-based identification of semantic relations, using LexNET (Shwartz and Dagan, 2016), an integrated path-based and distributional method for semantic relation classification. The reported results in the shared task bring this submission to the third pl... | false |
Multiclass Total Variation Clustering | Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a g... | true |
Extended Lifted Inference with Joint Formulas | The First-Order Variable Elimination (FOVE) algorithm allows exact inference to be applied directly to probabilistic relational models, and has proven to be vastly superior to the application of standard inference methods on a grounded propositional model. Still, FOVE operators can be applied under restricted condition... | false |
Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm | Correct inference of genetic regulations inside a cell is one of the greatest challenges in post genomic era for the biologist and researchers. Several intelligent techniques and models were already proposed to identify the regulatory relations among genes from the biological database like time series microarray data. ... | false |
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks | Electronic health records provide a rich source of data for machine learning methods to learn dynamic treatment responses over time. However, any direct estimation is hampered by the presence of time-dependent confounding, where actions taken are dependent on time-varying variables related to the outcome of interest. D... | true |
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering | In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selected passage, from which th... | false |
b-bit Marginal Regression | We consider the problem of sparse signal recovery from $m$ linear measurements quantized to $b$ bits. $b$-bit Marginal Regression is proposed as recovery algorithm. We study the question of choosing $b$ in the setting of a given budget of bits $B = m \cdot b$ and derive a single easy-to-compute expression characterizin... | true |
Fast Proximal Linearized Alternating Direction Method of Multiplier with Parallel Splitting | The Augmented Lagragian Method (ALM) and Alternating Direction Method of Multiplier (ADMM) have been powerful optimization methods for general convex programming subject to linear constraint. We consider the convex problem whose objective consists of a smooth part and a nonsmooth but simple part. We propose the Fast Pr... | true |
Decision Tree Classification on Outsourced Data | This paper proposes a client-server decision tree learning method for outsourced private data. The privacy model is anatomization/fragmentation: the server sees data values, but the link between sensitive and identifying information is encrypted with a key known only to clients. Clients have limited processing and stor... | false |
A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging | This paper presents a new method to construct a system of transformation rules for the Part-Of-Speech tagging task. Our approach is based on an incremental knowledge acquisition methodology where rules are stored in an exception-structure and new rules are only added to correct errors of existing rules; thus allowing s... | false |
Asymmetric Tri-training for Unsupervised Domain Adaptation | Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks. It is important to apply such models to different domains because collecting many labeled samples in various domains is expensive. In unsupervised domain adaptation, one needs to train a classifier that works well on a ta... | true |
Trolls Identification within an Uncertain Framework | The web plays an important role in people's social lives since the emergence of Web 2.0. It facilitates the interaction between users, gives them the possibility to freely interact, share and collaborate through social networks, online communities forums, blogs, wikis and other online collaborative media. However, an o... | false |
Causal Discovery for Manufacturing Domains | Yield and quality improvement is of paramount importance to any manufacturing company. One of the ways of improving yield is through discovery of the root causal factors affecting yield. We propose the use of data-driven interpretable causal models to identify key factors affecting yield. We focus on factors that are m... | false |
Bounded Regret for Finite-Armed Structured Bandits | We study a new type of K-armed bandit problem where the expected return of one arm may depend on the returns of other arms. We present a new algorithm for this general class of problems and show that under certain circumstances it is possible to achieve finite expected cumulative regret. We also give problem-dependent ... | true |
Structured Prediction Theory Based on Factor Graph Complexity | We present a general theoretical analysis of structured prediction with a series of new results. We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition. These are the tightest margin... | true |
Optimal Neural Codes for Control and Estimation | Agents acting in the natural world aim at selecting appropriate actions based on noisy and partial sensory observations. Many behaviors leading to decision making and action selection in a closed loop setting are naturally phrased within a control theoretic framework. Within the framework of optimal Control Theory, one... | true |
A scaled Bregman theorem with applications | Bregman divergences play a central role in the design and analysis of a range of machine learning algorithms. This paper explores the use of Bregman divergences to establish reductions between such algorithms and their analyses. We present a new scaled isodistortion theorem involving Bregman divergences (scaled Bregman... | true |
Semi-flat minima and saddle points by embedding neural networks to overparameterization | We theoretically study the landscape of the training error for neural networks in overparameterized cases. We consider three basic methods for embedding a network into a wider one with more hidden units, and discuss whether a minimum point of the narrower network gives a minimum or saddle point of the wider one. Our r... | true |
A Preliminary Study on the Learning Informativeness of Data Subsets | Estimating the internal state of a robotic system is complex: this is performed from multiple heterogeneous sensor inputs and knowledge sources. Discretization of such inputs is done to capture saliences, represented as symbolic information, which often presents structure and recurrence. As these sequences are used to ... | false |
Unsupervised Risk Estimation Using Only Conditional Independence Structure | We show how to estimate a model's test error from unlabeled data, on distributions very different from the training distribution, while assuming only that certain conditional independencies are preserved between train and test. We do not need to assume that the optimal predictor is the same between train and test, or t... | true |
Une mesure d'expertise pour le crowdsourcing | Crowdsourcing, a major economic issue, is the fact that the firm outsources internal task to the crowd. It is a form of digital subcontracting for the general public. The evaluation of the participants work quality is a major issue in crowdsourcing. Indeed, contributions must be controlled to ensure the effectiveness a... | false |
Geometry of Compositionality | This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way. The test is computationally simple, relying on no external resources and only uses a set of trained word vectors. Experiments show that the proposed method is competitive with state of the art and... | true |
The Role of Normalization in the Belief Propagation Algorithm | An important part of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov Random Field. The belief propagation algorithm, which is an exact procedure to compute these marginals when the underlying graph is a tree, has gained its popularity as a... | false |
fastFM: A Library for Factorization Machines | Factorization Machines (FM) are only used in a narrow range of applications and are not part of the standard toolbox of machine learning models. This is a pity, because even though FMs are recognized as being very successful for recommender system type applications they are a general model to deal with sparse and high ... | false |
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses | The design of convex, calibrated surrogate losses, whose minimization entails consistency with respect to a desired target loss, is an important concept to have emerged in the theory of machine learning in recent years. We give an explicit construction of a convex least-squares type surrogate loss that can be designed ... | true |
An efficient algorithm for learning with semi-bandit feedback | We consider the problem of online combinatorial optimization under semi-bandit feedback. The goal of the learner is to sequentially select its actions from a combinatorial decision set so as to minimize its cumulative loss. We propose a learning algorithm for this problem based on combining the Follow-the-Perturbed-Lea... | false |
A System for Probabilistic Linking of Thesauri and Classification Systems | This paper presents a system which creates and visualizes probabilistic semantic links between concepts in a thesaurus and classes in a classification system. For creating the links, we build on the Polylingual Labeled Topic Model (PLL-TM). PLL-TM identifies probable thesaurus descriptors for each class in the classifi... | false |
Divide-and-Conquer based Ensemble to Spot Emotions in Speech using MFCC and Random Forest | Besides spoken words, speech signals also carry information about speaker gender, age, and emotional state which can be used in a variety of speech analysis applications. In this paper, a divide and conquer strategy for ensemble classification has been proposed to recognize emotions in speech. Intrinsic hierarchy in em... | false |
Lifelong Learning with Non-i.i.d. Tasks | In this work we aim at extending theoretical foundations of lifelong learning. Previous work analyzing this scenario is based on the assumption that the tasks are sampled i.i.d. from a task environment or limited to strongly constrained data distributions. Instead we study two scenarios when lifelong learning is possib... | true |
Neural Networks and Rational Functions | Neural networks and rational functions efficiently approximate each other. In more detail, it is shown here that for any ReLU network, there exists a rational function of degree $O(\text{polylog}(1/\epsilon))$ which is $\epsilon$-close, and similarly for any rational function there exists a ReLU network of size $O(\tex... | true |
Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition | Text embeddings have played a key role in obtaining state-of-the-art results in natural language processing. Word2Vec and its variants have successfully mapped words with similar syntactic or semantic meanings to nearby vectors. However, extracting universal embeddings of longer word-sequences remains a challenging tas... | false |
Using Dimension Reduction to Improve the Classification of High-dimensional Data | In this work we show that the classification performance of high-dimensional structural MRI data with only a small set of training examples is improved by the usage of dimension reduction methods. We assessed two different dimension reduction variants: feature selection by ANOVA F-test and feature transformation by PCA... | false |
Political Speech Generation | In this report we present a system that can generate political speeches for a desired political party. Furthermore, the system allows to specify whether a speech should hold a supportive or opposing opinion. The system relies on a combination of several state-of-the-art NLP methods which are discussed in this report. T... | false |
Machine learning on images using a string-distance | We present a new method for image feature-extraction which is based on representing an image by a finite-dimensional vector of distances that measure how different the image is from a set of image prototypes. We use the recently introduced Universal Image Distance (UID) \cite{RatsabyChesterIEEE2012} to compare the simi... | false |
Interest-Driven Discovery of Local Process Models | Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process models that describe highly frequent behavior, but these models do not always provide useful answers for questio... | false |
A Diversity-Promoting Objective Function for Neural Conversation Models | Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., \textit{I don't know}) regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (responses) given input (messages) is unsuited to r... | true |
Mining GOLD Samples for Conditional GANs | Conditional generative adversarial networks (cGANs) have gained a considerable attention in recent years due to its class-wise controllability and superior quality for complex generation tasks. We introduce a simple yet effective approach to improving cGANs by measuring the discrepancy between the data distribution and... | true |
Ontology Based SMS Controller for Smart Phones | Text analysis includes lexical analysis of the text and has been widely studied and used in diverse applications. In the last decade, researchers have proposed many efficient solutions to analyze / classify large text dataset, however, analysis / classification of short text is still a challenge because 1) the data is ... | false |
Linear Regression with Limited Observation | We consider the most common variants of linear regression, including Ridge, Lasso and Support-vector regression, in a setting where the learner is allowed to observe only a fixed number of attributes of each example at training time. We present simple and efficient algorithms for these problems: for Lasso and Ridge reg... | true |
Partitioning Structure Learning for Segmented Linear Regression Trees | This paper proposes a partitioning structure learning method for segmented linear regression trees (SLRT), which assigns linear predictors over the terminal nodes. The recursive partitioning process is driven by an adaptive split selection algorithm that maximizes, at each node, a criterion function based on a conditio... | true |
Identifying and Categorizing Anomalies in Retinal Imaging Data | The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabul... | false |
TSEB: More Efficient Thompson Sampling for Policy Learning | In model-based solution approaches to the problem of learning in an unknown environment, exploring to learn the model parameters takes a toll on the regret. The optimal performance with respect to regret or PAC bounds is achievable, if the algorithm exploits with respect to reward or explores with respect to the model ... | false |
Computational Algorithms Based on the Paninian System to Process Euphonic Conjunctions for Word Searches | Searching for words in Sanskrit E-text is a problem that is accompanied by complexities introduced by features of Sanskrit such as euphonic conjunctions or sandhis. A word could occur in an E-text in a transformed form owing to the operation of rules of sandhi. Simple word search would not yield these transformed forms... | false |
Identifying Bengali Multiword Expressions using Semantic Clustering | One of the key issues in both natural language understanding and generation is the appropriate processing of Multiword Expressions (MWEs). MWEs pose a huge problem to the precise language processing due to their idiosyncratic nature and diversity in lexical, syntactical and semantic properties. The semantics of a MWE c... | false |
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities | The softmax representation of probabilities for categorical variables plays a prominent role in modern machine learning with numerous applications in areas such as large scale classification, neural language modeling and recommendation systems. However, softmax estimation is very expensive for large scale inference bec... | true |
Compositional Sequence Labeling Models for Error Detection in Learner Writing | In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task ... | true |
An Evaluation of Information Sharing Parking Guidance Policies Using a Bayesian Approach | Real-time parking occupancy information is critical for a parking management system to facilitate drivers to park more efficiently. Recent advances in connected and automated vehicle technologies enable sensor-equipped cars (probe cars) to detect and broadcast available parking spaces when driving through parking lots.... | false |
On the "Calligraphy" of Books | Authorship attribution is a natural language processing task that has been widely studied, often by considering small order statistics. In this paper, we explore a complex network approach to assign the authorship of texts based on their mesoscopic representation, in an attempt to capture the flow of the narrative. Ind... | false |
Breaking Symmetries in Graph Search with Canonizing Sets | There are many complex combinatorial problems which involve searching for an undirected graph satisfying given constraints. Such problems are often highly challenging because of the large number of isomorphic representations of their solutions. This paper introduces effective and compact, complete symmetry breaking con... | false |
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN | In this paper, we propose a novel regularization method for Generative Adversarial Networks that allows the model to learn discriminative yet compact binary representations of image patches (image descriptors). We exploit the dimensionality reduction that takes place in the intermediate layers of the discriminator netw... | true |
Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention | Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing. For such a complex text processing task, different from previous studies, we argue that it is necessary to repeatedly read the arguments and dynamically exploit the efficient features useful for rec... | true |
An n-ary Constraint for the Stable Marriage Problem | We present an n-ary constraint for the stable marriage problem. This constraint acts between two sets of integer variables where the domains of those variables represent preferences. Our constraint enforces stability and disallows bigamy. For a stable marriage instance with $n$ men and $n$ women we require only one of ... | false |
Deep Action Sequence Learning for Causal Shape Transformation | Deep learning (DL) became the method of choice in recent years for solving problems ranging from object recognition and speech recognition to robotic perception and human disease prediction. In this paper, we present a hybrid architecture of convolutional neural networks (CNN) and stacked autoencoders (SAE) to learn a ... | false |
Adversarially Robust Generalization Requires More Data | Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning ... | true |
A Hybrid Solution to improve Iteration Efficiency in the Distributed Learning | Currently, many machine learning algorithms contain lots of iterations. When it comes to existing large-scale distributed systems, some slave nodes may break down or have lower efficiency. Therefore traditional machine learning algorithm may fail because of the instability of distributed system.We presents a hybrid app... | false |
A complex network approach to stylometry | Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studie... | false |
Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations | We propose a language-agnostic way of automatically generating sets of semantically similar clusters of entities along with sets of "outlier" elements, which may then be used to perform an intrinsic evaluation of word embeddings in the outlier detection task. We used our methodology to create a gold-standard dataset, w... | false |
Importance Weighting and Variational Inference | Recent work used importance sampling ideas for better variational bounds on likelihoods. We clarify the applicability of these ideas to pure probabilistic inference, by showing the resulting Importance Weighted Variational Inference (IWVI) technique is an instance of augmented variational inference, thus identifying th... | true |
Safe and Efficient Off-Policy Reinforcement Learning | In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace(lambda), with three desired properties: (1) it has low variance; (2) it safely uses samples collected from any behaviour policy, ... | true |
Diversified Top-k Partial MaxSAT Solving | We introduce a diversified top-k partial MaxSAT problem, a combination of partial MaxSAT problem and enumeration problem. Given a partial MaxSAT formula F and a positive integer k, the diversified top-k partial MaxSAT is to find k maximal solutions for F such that the k maximal solutions satisfy the maximum number of s... | false |
Generating Images from Captions with Attention | Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Microsoft COCO, we compare our model with several ba... | true |
Online Learning with Switching Costs and Other Adaptive Adversaries | We study the power of different types of adaptive (nonoblivious) adversaries in the setting of prediction with expert advice, under both full information and bandit feedback. We measure the player's performance using a new notion of regret, also known as policy regret, which better captures the adversary's adaptiveness... | true |
Measuring Thematic Fit with Distributional Feature Overlap | In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensio... | true |
Efficiently Discovering Hammock Paths from Induced Similarity Networks | Similarity networks are important abstractions in many information management applications such as recommender systems, corpora analysis, and medical informatics. For instance, by inducing similarity networks between movies rated similarly by users, or between documents containing common terms, and or between clinical ... | false |
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits | We study epsilon-best-arm identification, in a setting where during the exploration phase, the cost of each arm pull is proportional to the expected future reward of that arm. We term this setting Pay-Per-Reward. We provide an algorithm for this setting, that with a high probability returns an epsilon-best arm, while ... | true |
Invertibility of Convolutional Generative Networks from Partial Measurements | In this work, we present new theoretical results on convolutional generative neural networks, in particular their invertibility (i.e., the recovery of input latent code given the network output). The study of network inversion problem is motivated by image inpainting and the mode collapse problem in training GAN. Netwo... | true |
Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data | We present in this paper an application which automatically generates textual short-term weather forecasts for every municipality in Galicia (NW Spain), using the real data provided by the Galician Meteorology Agency (MeteoGalicia). This solution combines in an innovative way computing with perceptions techniques and s... | false |
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling | We study the problem of 3D object generation. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets. The benefits of our model are three-fol... | true |
On the Interpretability of Conditional Probability Estimates in the Agnostic Setting | Many classification algorithms produce confidence measures in the form of conditional probability of labels given the features of the target instance. It is desirable to be make these confidence measures calibrated or consistent, in the sense that they correctly capture the belief of the algorithm in the label output. ... | false |
Deep AutoRegressive Networks | We introduce a multilayer deep generative model capable of learning hierarchies of sparse distributed representations from data. The model consists of several layers of stochastic units, with autoregressive connections within each layer, which allows for efficient exact sampling. We train the model efficiently using an... | true |
Turing Test for the Internet of Things | How smart is your kettle? How smart are things in your kitchen, your house, your neighborhood, on the internet? With the advent of Internet of Things, and the move of making devices `smart' by utilizing AI, a natural question arrises, how can we evaluate the progress. The standard way of evaluating AI is through the Tu... | false |
A Fully Convolutional Neural Network for Speech Enhancement | In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a `mapping' between noisy speech spectra and cle... | false |
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache | In many embedded systems, such as imaging sys- tems, the system has a single designated purpose, and same threads are executed repeatedly. Profiling thread behavior, allows the system to allocate each thread its resources in a way that improves overall system performance. We study an online resource al- locationproblem... | false |
Budget Constraints in Prediction Markets | We give a detailed characterization of optimal trades under budget constraints in a prediction market with a cost-function-based automated market maker. We study how the budget constraints of individual traders affect their ability to impact the market price. As a concrete application of our characterization, we give s... | false |
A Unified Approach for Learning the Parameters of Sum-Product Networks | We present a unified approach for learning the parameters of Sum-Product networks (SPNs). We prove that any complete and decomposable SPN is equivalent to a mixture of trees where each tree corresponds to a product of univariate distributions. Based on the mixture model perspective, we characterize the objective functi... | true |
Gaussian Process Prior Variational Autoencoders | Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many important datasets, suc... | true |
Structured Reachability Analysis for Markov Decision Processes | Recent research in decision theoretic planning has focussed on making the solution of Markov decision processes (MDPs) more feasible. We develop a family of algorithms for structured reachability analysis of MDPs that are suitable when an initial state (or set of states) is known. Using compact, structured representati... | false |
Spectral Filtering for General Linear Dynamical Systems | We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix. The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a... | true |
Storytelling Agents with Personality and Adaptivity | We explore the expression of personality and adaptivity through the gestures of virtual agents in a storytelling task. We conduct two experiments using four different dialogic stories. We manipulate agent personality on the extraversion scale, whether the agents adapt to one another in their gestural performance and ag... | false |
Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification | Connectionist temporal classification (CTC) based supervised sequence training of recurrent neural networks (RNNs) has shown great success in many machine learning areas including end-to-end speech and handwritten character recognition. For the CTC training, however, it is required to unroll the RNN by the length of an... | false |
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