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3,600 | Stochastic Deconvolutional Neural Network Ensemble Training on
Generative Pseudo-Adversarial Networks | stat.ML | The 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 agent becomes more adept than the other which
results in the decre... | computer science |
3,601 | Batch Kalman Normalization: Towards Training Deep Neural Networks with
Micro-Batches | cs.CV | As an indispensable component, Batch Normalization (BN) has successfully
improved the training of deep neural networks (DNNs) with mini-batches, by
normalizing the distribution of the internal representation for each hidden
layer. However, the effectiveness of BN would diminish with scenario of
micro-batch (e.g., less ... | computer science |
3,602 | Supervised classification of Dermatological diseases by Deep neural
networks | stat.ML | This paper introduces a deep learning based classifier for common skin
ailments, to help people without easy access to dermatologists. We have
confirmed that it can classify at approximately 80% accuracy on average, when
primary care doctors are reported to have 53% success as per recent literature.
Dermatological dise... | computer science |
3,603 | Lipschitz-Margin Training: Scalable Certification of Perturbation
Invariance for Deep Neural Networks | cs.CV | High sensitivity of neural networks against malicious perturbations on inputs
causes security concerns. We aim to ensure perturbation invariance in their
predictions. However, prior work requires strong assumptions on network
structures and massive computational costs, and thus their applications are
limited. In this p... | computer science |
3,604 | DCFNet: Deep Neural Network with Decomposed Convolutional Filters | stat.ML | Filters in a Convolutional Neural Network (CNN) contain model parameters
learned from enormous amounts of data. In this paper, we suggest to decompose
convolutional filters in CNN as a truncated expansion with pre-fixed bases,
namely the Decomposed Convolutional Filters network (DCFNet), where the
expansion coefficient... | computer science |
3,605 | Learning Privacy Preserving Encodings through Adversarial Training | cs.LG | We present a framework to learn privacy-preserving encodings of images (or
other high-dimensional data) to inhibit inference of a chosen private
attribute. Rather than encoding a fixed dataset or inhibiting a fixed
estimator, we aim to to learn an encoding function such that even after this
function is fixed, an estima... | computer science |
3,606 | Mapping Images to Scene Graphs with Permutation-Invariant Structured
Prediction | stat.ML | Structured prediction is concerned with predicting multiple inter-dependent
labels simultaneously. Classical methods like CRF achieve this by maximizing a
score function over the set of possible label assignments. Recent extensions
use neural networks to either implement the score function or in maximization.
The curre... | computer science |
3,607 | cGANs with Projection Discriminator | cs.LG | We propose a novel, projection based way to incorporate the conditional
information into the discriminator of GANs that respects the role of the
conditional information in the underlining probabilistic model. This approach
is in contrast with most frameworks of conditional GANs used in application
today, which use the ... | computer science |
3,608 | Spectral Normalization for Generative Adversarial Networks | cs.LG | One of the challenges in the study of generative adversarial networks is the
instability of its training. In this paper, we propose a novel weight
normalization technique called spectral normalization to stabilize the training
of the discriminator. Our new normalization technique is computationally light
and easy to in... | computer science |
3,609 | Teaching Categories to Human Learners with Visual Explanations | cs.CV | We study the problem of computer-assisted teaching with explanations.
Conventional approaches for machine teaching typically only provide feedback at
the instance level e.g., the category or label of the instance. However, it is
intuitive that clear explanations from a knowledgeable teacher can
significantly improve a ... | computer science |
3,610 | i-RevNet: Deep Invertible Networks | cs.LG | It is widely believed that the success of deep convolutional networks is
based on progressively discarding uninformative variability about the input
with respect to the problem at hand. This is supported empirically by the
difficulty of recovering images from their hidden representations, in most
commonly used network ... | computer science |
3,611 | Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for
Iterative Image Recovery | stat.ML | In "extreme" computational imaging that collects extremely undersampled or
noisy measurements, obtaining an accurate image within a reasonable computing
time is challenging. Incorporating image mapping convolutional neural networks
(CNN) to iterative image recovery has great potential to resolve this issue.
This paper ... | computer science |
3,612 | Continuous Relaxation of MAP Inference: A Nonconvex Perspective | cs.CV | In this paper, we study a nonconvex continuous relaxation of MAP inference in
discrete Markov random fields (MRFs). We show that for arbitrary MRFs, this
relaxation is tight, and a discrete stationary point of it can be easily
reached by a simple block coordinate descent algorithm. In addition, we study
the resolution ... | computer science |
3,613 | Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise | cs.LG | We study the robustness of classifiers to various kinds of random noise
models. In particular, we consider noise drawn uniformly from the $\ell\_p$
ball for $p \in [1, \infty]$ and Gaussian noise with an arbitrary covariance
matrix. We characterize this robustness to random noise in terms of the
distance to the decisio... | computer science |
3,614 | Adversarial Examples that Fool both Human and Computer Vision | cs.LG | Machine learning models are vulnerable to adversarial examples: small changes
to images can cause computer vision models to make mistakes such as identifying
a school bus as an ostrich. However, it is still an open question whether
humans are prone to similar mistakes. Here, we create the first adversarial
examples des... | computer science |
3,615 | Hessian-based Analysis of Large Batch Training and Robustness to
Adversaries | cs.CV | Large batch size training of Neural Networks has been shown to incur accuracy
loss when trained with the current methods. The precise underlying reasons for
this are still not completely understood. Here, we study large batch size
training through the lens of the Hessian operator and robust optimization. In
particular,... | computer science |
3,616 | Adversarial vulnerability for any classifier | cs.LG | Despite achieving impressive and often superhuman performance on multiple
benchmarks, state-of-the-art deep networks remain highly vulnerable to
perturbations: adding small, imperceptible, adversarial perturbations can lead
to very high error rates. Provided the data distribution is defined using a
generative model map... | computer science |
3,617 | Deep learning in radiology: an overview of the concepts and a survey of
the state of the art | cs.CV | Deep learning is a branch of artificial intelligence where networks of simple
interconnected units are used to extract patterns from data in order to solve
complex problems. Deep learning algorithms have shown groundbreaking
performance in a variety of sophisticated tasks, especially those related to
images. They have ... | computer science |
3,618 | A DIRT-T Approach to Unsupervised Domain Adaptation | stat.ML | Domain adaptation refers to the problem of leveraging labeled data in a
source domain to learn an accurate model in a target domain where labels are
scarce or unavailable. A recent approach for finding a common representation of
the two domains is via domain adversarial training (Ganin & Lempitsky, 2015),
which attempt... | computer science |
3,619 | Wide Compression: Tensor Ring Nets | cs.LG | Deep neural networks have demonstrated state-of-the-art performance in a
variety of real-world applications. In order to obtain performance gains, these
networks have grown larger and deeper, containing millions or even billions of
parameters and over a thousand layers. The trade-off is that these large
architectures r... | computer science |
3,620 | Adversarial Active Learning for Deep Networks: a Margin Based Approach | cs.LG | We propose a new active learning strategy designed for deep neural networks.
The goal is to minimize the number of data annotation queried from an oracle
during training. Previous active learning strategies scalable for deep networks
were mostly based on uncertain sample selection. In this work, we focus on
examples ly... | computer science |
3,621 | VR-SGD: A Simple Stochastic Variance Reduction Method for Machine
Learning | cs.LG | In this paper, we propose a simple variant of the original SVRG, called
variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of
snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the
two vectors of VR-SGD are set to the average and last iterate of the previous
epoch, respecti... | computer science |
3,622 | Natural data structure extracted from neighborhood-similarity graphs | stat.ML | 'Big' high-dimensional data are commonly analyzed in low-dimensions, after
performing a dimensionality-reduction step that inherently distorts the data
structure. For the same purpose, clustering methods are also often used. These
methods also introduce a bias, either by starting from the assumption of a
particular geo... | computer science |
3,623 | Meta-Learning for Semi-Supervised Few-Shot Classification | cs.LG | In few-shot classification, we are interested in learning algorithms that
train a classifier from only a handful of labeled examples. Recent progress in
few-shot classification has featured meta-learning, in which a parameterized
model for a learning algorithm is defined and trained on episodes representing
different c... | computer science |
3,624 | Deep Bayesian Active Semi-Supervised Learning | cs.LG | In many applications the process of generating label information is expensive
and time consuming. We present a new method that combines active and
semi-supervised deep learning to achieve high generalization performance from a
deep convolutional neural network with as few known labels as possible. In a
setting where a ... | computer science |
3,625 | GAN-based Synthetic Medical Image Augmentation for increased CNN
Performance in Liver Lesion Classification | cs.CV | Deep learning methods, and in particular convolutional neural networks
(CNNs), have led to an enormous breakthrough in a wide range of computer vision
tasks, primarily by using large-scale annotated datasets. However, obtaining
such datasets in the medical domain remains a challenge. In this paper, we
present methods f... | computer science |
3,626 | Improving the Improved Training of Wasserstein GANs: A Consistency Term
and Its Dual Effect | cs.CV | Despite being impactful on a variety of problems and applications, the
generative adversarial nets (GANs) are remarkably difficult to train. This
issue is formally analyzed by \cite{arjovsky2017towards}, who also propose an
alternative direction to avoid the caveats in the minmax two-player training of
GANs. The corres... | computer science |
3,627 | Local Distance Metric Learning for Nearest Neighbor Algorithm | cs.CV | Distance metric learning is a successful way to enhance the performance of
the nearest neighbor classifier. In most cases, however, the distribution of
data does not obey a regular form and may change in different parts of the
feature space. Regarding that, this paper proposes a novel local distance
metric learning met... | computer science |
3,628 | Multi-class Active Learning: A Hybrid Informative and Representative
Criterion Inspired Approach | cs.LG | Labeling each instance in a large dataset is extremely labor- and time-
consuming . One way to alleviate this problem is active learning, which aims to
which discover the most valuable instances for labeling to construct a powerful
classifier. Considering both informativeness and representativeness provides a
promising... | computer science |
3,629 | Exponential Discriminative Metric Embedding in Deep Learning | cs.CV | With the remarkable success achieved by the Convolutional Neural Networks
(CNNs) in object recognition recently, deep learning is being widely used in
the computer vision community. Deep Metric Learning (DML), integrating deep
learning with conventional metric learning, has set new records in many fields,
especially in... | computer science |
3,630 | Noise2Noise: Learning Image Restoration without Clean Data | cs.CV | We apply basic statistical reasoning to signal reconstruction by machine
learning -- learning to map corrupted observations to clean signals -- with a
simple and powerful conclusion: under certain common circumstances, it is
possible to learn to restore signals without ever observing clean ones, at
performance close or... | computer science |
3,631 | A Probabilistic Disease Progression Model for Predicting Future Clinical
Outcome | cs.LG | In this work, we consider the problem of predicting the course of a
progressive disease, such as cancer or Alzheimer's. Progressive diseases often
start with mild symptoms that might precede a diagnosis, and each patient
follows their own trajectory. Patient trajectories exhibit wild variability,
which can be associate... | computer science |
3,632 | A Multi-Modal Approach to Infer Image Affect | cs.CV | The group affect or emotion in an image of people can be inferred by
extracting features about both the people in the picture and the overall makeup
of the scene. The state-of-the-art on this problem investigates a combination
of facial features, scene extraction and even audio tonality. This paper
combines three addit... | computer science |
3,633 | Fast Subspace Clustering Based on the Kronecker Product | cs.LG | Subspace clustering is a useful technique for many computer vision
applications in which the intrinsic dimension of high-dimensional data is often
smaller than the ambient dimension. Spectral clustering, as one of the main
approaches to subspace clustering, often takes on a sparse representation or a
low-rank represent... | computer science |
3,634 | Deep Component Analysis via Alternating Direction Neural Networks | cs.LG | Despite a lack of theoretical understanding, deep neural networks have
achieved unparalleled performance in a wide range of applications. On the other
hand, shallow representation learning with component analysis is associated
with rich intuition and theory, but smaller capacity often limits its
usefulness. To bridge t... | computer science |
3,635 | Constrained Deep Learning using Conditional Gradient and Applications in
Computer Vision | cs.LG | A number of results have recently demonstrated the benefits of incorporating
various constraints when training deep architectures in vision and machine
learning. The advantages range from guarantees for statistical generalization
to better accuracy to compression. But support for general constraints within
widely used ... | computer science |
3,636 | A Mixture of Views Network with Applications to the Classification of
Breast Microcalcifications | cs.CV | In this paper we examine data fusion methods for multi-view data
classification. We present a decision concept which explicitly takes into
account the input multi-view structure, where for each case there is a
different subset of relevant views. This data fusion concept, which we dub
Mixture of Views, is implemented by... | computer science |
3,637 | Asymmetric kernel in Gaussian Processes for learning target variance | cs.LG | This work incorporates the multi-modality of the data distribution into a
Gaussian Process regression model. We approach the problem from a
discriminative perspective by learning, jointly over the training data, the
target space variance in the neighborhood of a certain sample through metric
learning. We start by using... | computer science |
3,638 | Improving Transferability of Adversarial Examples with Input Diversity | cs.CV | Though convolutional neural networks have achieved state-of-the-art
performance on various vision tasks, they are extremely vulnerable to
adversarial examples, which are obtained by adding human-imperceptible
perturbations to the original images. Adversarial examples can thus be used as
an useful tool to evaluate and s... | computer science |
3,639 | Diagnostic Classification Of Lung Nodules Using 3D Neural Networks | cs.CV | Lung cancer is the leading cause of cancer-related death worldwide. Early
diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides
an opportunity for designing effective treatment and making financial and care
plans. In this paper, we consider the problem of diagnostic classification
between beni... | computer science |
3,640 | Adversarial Defense based on Structure-to-Signal Autoencoders | cs.LG | Adversarial attack methods have demonstrated the fragility of deep neural
networks. Their imperceptible perturbations are frequently able fool
classifiers into potentially dangerous misclassifications. We propose a novel
way to interpret adversarial perturbations in terms of the effective input
signal that classifiers ... | computer science |
3,641 | Learning to Generate Posters of Scientific Papers | cs.AI | Researchers often summarize their work in the form of posters. Posters
provide a coherent and efficient way to convey core ideas from scientific
papers. Generating a good scientific poster, however, is a complex and time
consuming cognitive task, since such posters need to be readable, informative,
and visually aesthet... | computer science |
3,642 | Conversational flow in Oxford-style debates | cs.CL | Public debates are a common platform for presenting and juxtaposing diverging
views on important issues. In this work we propose a methodology for tracking
how ideas flow between participants throughout a debate. We use this approach
in a case study of Oxford-style debates---a competitive format where the winner
is det... | computer science |
3,643 | Conversational Markers of Constructive Discussions | cs.CL | Group discussions are essential for organizing every aspect of modern life,
from faculty meetings to senate debates, from grant review panels to papal
conclaves. While costly in terms of time and organization effort, group
discussions are commonly seen as a way of reaching better decisions compared to
solutions that do... | computer science |
3,644 | Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy
Game | cs.CL | Interpersonal relations are fickle, with close friendships often dissolving
into enmity. In this work, we explore linguistic cues that presage such
transitions by studying dyadic interactions in an online strategy game where
players form alliances and break those alliances through betrayal. We
characterize friendships ... | computer science |
3,645 | Introduction to Formal Concept Analysis and Its Applications in
Information Retrieval and Related Fields | cs.IR | This paper is a tutorial on Formal Concept Analysis (FCA) and its
applications. FCA is an applied branch of Lattice Theory, a mathematical
discipline which enables formalisation of concepts as basic units of human
thinking and analysing data in the object-attribute form. Originated in early
80s, during the last three d... | computer science |
3,646 | Exploring Latent Semantic Factors to Find Useful Product Reviews | cs.AI | Online reviews provided by consumers are a valuable asset for e-Commerce
platforms, influencing potential consumers in making purchasing decisions.
However, these reviews are of varying quality, with the useful ones buried deep
within a heap of non-informative reviews. In this work, we attempt to
automatically identify... | computer science |
3,647 | Item Recommendation with Evolving User Preferences and Experience | cs.AI | Current recommender systems exploit user and item similarities by
collaborative filtering. Some advanced methods also consider the temporal
evolution of item ratings as a global background process. However, all prior
methods disregard the individual evolution of a user's experience level and how
this is expressed in th... | computer science |
3,648 | People on Drugs: Credibility of User Statements in Health Communities | cs.AI | Online health communities are a valuable source of information for patients
and physicians. However, such user-generated resources are often plagued by
inaccuracies and misinformation. In this work we propose a method for
automatically establishing the credibility of user-generated medical statements
and the trustworth... | computer science |
3,649 | People on Media: Jointly Identifying Credible News and Trustworthy
Citizen Journalists in Online Communities | cs.AI | Media seems to have become more partisan, often providing a biased coverage
of news catering to the interest of specific groups. It is therefore essential
to identify credible information content that provides an objective narrative
of an event. News communities such as digg, reddit, or newstrust offer
recommendations,... | computer science |
3,650 | Credible Review Detection with Limited Information using Consistency
Analysis | cs.AI | Online reviews provide viewpoints on the strengths and shortcomings of
products/services, influencing potential customers' purchasing decisions.
However, the proliferation of non-credible reviews -- either fake (promoting/
demoting an item), incompetent (involving irrelevant aspects), or biased --
entails the problem o... | computer science |
3,651 | Item Recommendation with Continuous Experience Evolution of Users using
Brownian Motion | cs.AI | Online review communities are dynamic as users join and leave, adopt new
vocabulary, and adapt to evolving trends. Recent work has shown that
recommender systems benefit from explicit consideration of user experience.
However, prior work assumes a fixed number of discrete experience levels,
whereas in reality users gai... | computer science |
3,652 | Probabilistic Graphical Models for Credibility Analysis in Evolving
Online Communities | cs.SI | One of the major hurdles preventing the full exploitation of information from
online communities is the widespread concern regarding the quality and
credibility of user-contributed content. Prior works in this domain operate on
a static snapshot of the community, making strong assumptions about the
structure of the dat... | computer science |
3,653 | Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs | cs.CL | Time delay neural networks (TDNNs) are an effective acoustic model for large
vocabulary speech recognition. The strength of the model can be attributed to
its ability to effectively model long temporal contexts. However, current TDNN
models are relatively shallow, which limits the modelling capability. This
paper propo... | computer science |
3,654 | Between Sense and Sensibility: Declarative narrativisation of mental
models as a basis and benchmark for visuo-spatial cognition and computation
focussed collaborative cognitive systems | cs.AI | What lies between `\emph{sensing}' and `\emph{sensibility}'? In other words,
what kind of cognitive processes mediate sensing capability, and the formation
of sensible impressions ---e.g., abstractions, analogies, hypotheses and theory
formation, beliefs and their revision, argument formation--- in domain-specific
prob... | computer science |
3,655 | Contextual Media Retrieval Using Natural Language Queries | cs.IR | The widespread integration of cameras in hand-held and head-worn devices as
well as the ability to share content online enables a large and diverse visual
capture of the world that millions of users build up collectively every day. We
envision these images as well as associated meta information, such as GPS
coordinates... | computer science |
3,656 | Visualizing Natural Language Descriptions: A Survey | cs.CL | A natural language interface exploits the conceptual simplicity and
naturalness of the language to create a high-level user-friendly communication
channel between humans and machines. One of the promising applications of such
interfaces is generating visual interpretations of semantic content of a given
natural languag... | computer science |
3,657 | GaKCo: a Fast GApped k-mer string Kernel using COunting | cs.LG | String Kernel (SK) techniques, especially those using gapped $k$-mers as
features (gk), have obtained great success in classifying sequences like DNA,
protein, and text. However, the state-of-the-art gk-SK runs extremely slow when
we increase the dictionary size ($\Sigma$) or allow more mismatches ($M$). This
is becaus... | computer science |
3,658 | Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence
Learning | cs.SD | We present Deep Voice 3, a fully-convolutional attention-based neural
text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural
speech synthesis systems in naturalness while training ten times faster. We
scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more
than eight hundred hour... | computer science |
3,659 | The Google Similarity Distance | cs.CL | Words and phrases acquire meaning from the way they are used in society, from
their relative semantics to other words and phrases. For computers the
equivalent of `society' is `database,' and the equivalent of `use' is `way to
search the database.' We present a new theory of similarity between words and
phrases based o... | computer science |
3,660 | Robot Language Learning, Generation, and Comprehension | cs.RO | We present a unified framework which supports grounding natural-language
semantics in robotic driving. This framework supports acquisition (learning
grounded meanings of nouns and prepositions from human annotation of robotic
driving paths), generation (using such acquired meanings to generate sentential
description of... | computer science |
3,661 | A Poisson convolution model for characterizing topical content with word
frequency and exclusivity | cs.LG | An ongoing challenge in the analysis of document collections is how to
summarize content in terms of a set of inferred themes that can be interpreted
substantively in terms of topics. The current practice of parametrizing the
themes in terms of most frequent words limits interpretability by ignoring the
differential us... | computer science |
3,662 | User Model-Based Intent-Aware Metrics for Multilingual Search Evaluation | cs.IR | Despite the growing importance of multilingual aspect of web search, no
appropriate offline metrics to evaluate its quality are proposed so far. At the
same time, personal language preferences can be regarded as intents of a query.
This approach translates the multilingual search problem into a particular task
of searc... | computer science |
3,663 | Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and
Their Combination | cs.CL | This paper covers the two approaches for sentiment analysis: i) lexicon based
method; ii) machine learning method. We describe several techniques to
implement these approaches and discuss how they can be adopted for sentiment
classification of Twitter messages. We present a comparative study of different
lexicon combin... | computer science |
3,664 | Using Deep Learning for Detecting Spoofing Attacks on Speech Signals | cs.SD | It is well known that speaker verification systems are subject to spoofing
attacks. The Automatic Speaker Verification Spoofing and Countermeasures
Challenge -- ASVSpoof2015 -- provides a standard spoofing database, containing
attacks based on synthetic speech, along with a protocol for experiments. This
paper describe... | computer science |
3,665 | Learning Features from Co-occurrences: A Theoretical Analysis | cs.CL | Representing a word by its co-occurrences with other words in context is an
effective way to capture the meaning of the word. However, the theory behind
remains a challenge. In this work, taking the example of a word classification
task, we give a theoretical analysis of the approaches that represent a word X
by a func... | computer science |
3,666 | Unsupervised Learning of Disentangled and Interpretable Representations
from Sequential Data | cs.LG | We present a factorized hierarchical variational autoencoder, which learns
disentangled and interpretable representations from sequential data without
supervision. Specifically, we exploit the multi-scale nature of information in
sequential data by formulating it explicitly within a factorized hierarchical
graphical mo... | computer science |
3,667 | Taming Wild High Dimensional Text Data with a Fuzzy Lash | stat.ML | The bag of words (BOW) represents a corpus in a matrix whose elements are the
frequency of words. However, each row in the matrix is a very high-dimensional
sparse vector. Dimension reduction (DR) is a popular method to address sparsity
and high-dimensionality issues. Among different strategies to develop DR
method, Un... | computer science |
3,668 | Extracting Domain Invariant Features by Unsupervised Learning for Robust
Automatic Speech Recognition | cs.CL | The performance of automatic speech recognition (ASR) systems can be
significantly compromised by previously unseen conditions, which is typically
due to a mismatch between training and testing distributions. In this paper, we
address robustness by studying domain invariant features, such that domain
information become... | computer science |
3,669 | Deep CNN based feature extractor for text-prompted speaker recognition | eess.AS | Deep learning is still not a very common tool in speaker verification field.
We study deep convolutional neural network performance in the text-prompted
speaker verification task. The prompted passphrase is segmented into word
states - i.e. digits -to test each digit utterance separately. We train a
single high-level f... | computer science |
3,670 | Locally Private Bayesian Inference for Count Models | stat.ML | As more aspects of social interaction are digitally recorded, there is a
growing need to develop privacy-preserving data analysis methods. Social
scientists will be more likely to adopt these methods if doing so entails
minimal change to their current methodology. Toward that end, we present a
general and modular metho... | computer science |
3,671 | Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor | cs.CL | Using supporting backchannel (BC) cues can make human-computer interaction
more social. BCs provide a feedback from the listener to the speaker indicating
to the speaker that he is still listened to. BCs can be expressed in different
ways, depending on the modality of the interaction, for example as gestures or
acousti... | computer science |
3,672 | Multi-Agent Cooperation and the Emergence of (Natural) Language | cs.CL | The current mainstream approach to train natural language systems is to
expose them to large amounts of text. This passive learning is problematic if
we are interested in developing interactive machines, such as conversational
agents. We propose a framework for language learning that relies on multi-agent
communication... | computer science |
3,673 | Font Identification in Historical Documents Using Active Learning | cs.CV | Identifying the type of font (e.g., Roman, Blackletter) used in historical
documents can help optical character recognition (OCR) systems produce more
accurate text transcriptions. Towards this end, we present an active-learning
strategy that can significantly reduce the number of labeled samples needed to
train a font... | computer science |
3,674 | Neural Style Representations and the Large-Scale Classification of
Artistic Style | cs.CV | The artistic style of a painting is a subtle aesthetic judgment used by art
historians for grouping and classifying artwork. The recently introduced
`neural-style' algorithm substantially succeeds in merging the perceived
artistic style of one image or set of images with the perceived content of
another. In light of th... | computer science |
3,675 | Dynamic Policy Programming | cs.LG | In this paper, we propose a novel policy iteration method, called dynamic
policy programming (DPP), to estimate the optimal policy in the
infinite-horizon Markov decision processes. We prove the finite-iteration and
asymptotic l\infty-norm performance-loss bounds for DPP in the presence of
approximation/estimation erro... | computer science |
3,676 | Random design analysis of ridge regression | math.ST | This work gives a simultaneous analysis of both the ordinary least squares
estimator and the ridge regression estimator in the random design setting under
mild assumptions on the covariate/response distributions. In particular, the
analysis provides sharp results on the ``out-of-sample'' prediction error, as
opposed to... | computer science |
3,677 | Learning loopy graphical models with latent variables: Efficient methods
and guarantees | stat.ML | The problem of structure estimation in graphical models with latent variables
is considered. We characterize conditions for tractable graph estimation and
develop efficient methods with provable guarantees. We consider models where
the underlying Markov graph is locally tree-like, and the model is in the
regime of corr... | computer science |
3,678 | A Monte Carlo Algorithm for Universally Optimal Bayesian Sequence
Prediction and Planning | nlin.AO | The aim of this work is to address the question of whether we can in
principle design rational decision-making agents or artificial intelligences
embedded in computable physics such that their decisions are optimal in
reasonable mathematical senses. Recent developments in rare event probability
estimation, recursive ba... | computer science |
3,679 | Towards A Deeper Geometric, Analytic and Algorithmic Understanding of
Margins | math.OC | Given a matrix $A$, a linear feasibility problem (of which linear
classification is a special case) aims to find a solution to a primal problem
$w: A^Tw > \textbf{0}$ or a certificate for the dual problem which is a
probability distribution $p: Ap = \textbf{0}$. Inspired by the continued
importance of "large-margin cla... | computer science |
3,680 | Visualization of Tradeoff in Evaluation: from Precision-Recall & PN to
LIFT, ROC & BIRD | cs.LG | Evaluation often aims to reduce the correctness or error characteristics of a
system down to a single number, but that always involves trade-offs. Another
way of dealing with this is to quote two numbers, such as Recall and Precision,
or Sensitivity and Specificity. But it can also be useful to see more than
this, and ... | computer science |
3,681 | Variational Inference with Normalizing Flows | stat.ML | The choice of approximate posterior distribution is one of the core problems
in variational inference. Most applications of variational inference employ
simple families of posterior approximations in order to allow for efficient
inference, focusing on mean-field or other simple structured approximations.
This restricti... | computer science |
3,682 | Maximizing a Nonnegative, Monotone, Submodular Function Constrained to
Matchings | cs.DS | Submodular functions have many applications. Matchings have many
applications. The bitext word alignment problem can be modeled as the problem
of maximizing a nonnegative, monotone, submodular function constrained to
matchings in a complete bipartite graph where each vertex corresponds to a word
in the two input senten... | computer science |
3,683 | Variational Algorithms for Marginal MAP | stat.ML | The marginal maximum a posteriori probability (MAP) estimation problem, which
calculates the mode of the marginal posterior distribution of a subset of
variables with the remaining variables marginalized, is an important inference
problem in many models, such as those with hidden variables or uncertain
parameters. Unfo... | computer science |
3,684 | Stochastic Backpropagation and Approximate Inference in Deep Generative
Models | stat.ML | We marry ideas from deep neural networks and approximate Bayesian inference
to derive a generalised class of deep, directed generative models, endowed with
a new algorithm for scalable inference and learning. Our algorithm introduces a
recognition model to represent approximate posterior distributions, and that
acts as... | computer science |
3,685 | Tuned Models of Peer Assessment in MOOCs | cs.LG | In massive open online courses (MOOCs), peer grading serves as a critical
tool for scaling the grading of complex, open-ended assignments to courses with
tens or hundreds of thousands of students. But despite promising initial
trials, it does not always deliver accurate results compared to human experts.
In this paper,... | computer science |
3,686 | A New Optimal Stepsize For Approximate Dynamic Programming | math.OC | Approximate dynamic programming (ADP) has proven itself in a wide range of
applications spanning large-scale transportation problems, health care, revenue
management, and energy systems. The design of effective ADP algorithms has many
dimensions, but one crucial factor is the stepsize rule used to update a value
functi... | computer science |
3,687 | Probabilistic Numerics and Uncertainty in Computations | math.NA | We deliver a call to arms for probabilistic numerical methods: algorithms for
numerical tasks, including linear algebra, integration, optimization and
solving differential equations, that return uncertainties in their
calculations. Such uncertainties, arising from the loss of precision induced by
numerical calculation ... | computer science |
3,688 | Objective Variables for Probabilistic Revenue Maximization in
Second-Price Auctions with Reserve | stat.ML | Many online companies sell advertisement space in second-price auctions with
reserve. In this paper, we develop a probabilistic method to learn a profitable
strategy to set the reserve price. We use historical auction data with features
to fit a predictor of the best reserve price. This problem is delicate - the
struct... | computer science |
3,689 | Embarrassingly Parallel Variational Inference in Nonconjugate Models | stat.ML | We develop a parallel variational inference (VI) procedure for use in
data-distributed settings, where each machine only has access to a subset of
data and runs VI independently, without communicating with other machines. This
type of "embarrassingly parallel" procedure has recently been developed for
MCMC inference al... | computer science |
3,690 | Learning Causal Graphs with Small Interventions | cs.AI | We consider the problem of learning causal networks with interventions, when
each intervention is limited in size under Pearl's Structural Equation Model
with independent errors (SEM-IE). The objective is to minimize the number of
experiments to discover the causal directions of all the edges in a causal
graph. Previou... | computer science |
3,691 | Decomposition Bounds for Marginal MAP | cs.LG | Marginal MAP inference involves making MAP predictions in systems defined
with latent variables or missing information. It is significantly more
difficult than pure marginalization and MAP tasks, for which a large class of
efficient and convergent variational algorithms, such as dual decomposition,
exist. In this work,... | computer science |
3,692 | Doubly Robust Off-policy Value Evaluation for Reinforcement Learning | cs.LG | We study the problem of off-policy value evaluation in reinforcement learning
(RL), where one aims to estimate the value of a new policy based on data
collected by a different policy. This problem is often a critical step when
applying RL in real-world problems. Despite its importance, existing general
methods either h... | computer science |
3,693 | How (not) to Train your Generative Model: Scheduled Sampling,
Likelihood, Adversary? | stat.ML | Modern applications and progress in deep learning research have created
renewed interest for generative models of text and of images. However, even
today it is unclear what objective functions one should use to train and
evaluate these models. In this paper we present two contributions.
Firstly, we present a critique... | computer science |
3,694 | Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing | cs.DS | Existing methods for retrieving k-nearest neighbours suffer from the curse of
dimensionality. We argue this is caused in part by inherent deficiencies of
space partitioning, which is the underlying strategy used by most existing
methods. We devise a new strategy that avoids partitioning the vector space and
present a n... | computer science |
3,695 | Classification Accuracy as a Proxy for Two Sample Testing | cs.LG | When data analysts train a classifier and check if its accuracy is
significantly different from random guessing, they are implicitly and
indirectly performing a hypothesis test (two sample testing) and it is of
importance to ask whether this indirect method for testing is statistically
optimal or not. Given that hypoth... | computer science |
3,696 | Toward Deeper Understanding of Neural Networks: The Power of
Initialization and a Dual View on Expressivity | cs.LG | We develop a general duality between neural networks and compositional
kernels, striving towards a better understanding of deep learning. We show that
initial representations generated by common random initializations are
sufficiently rich to express all functions in the dual kernel space. Hence,
though the training ob... | computer science |
3,697 | Reinforcement Learning of POMDPs using Spectral Methods | cs.AI | We propose a new reinforcement learning algorithm for partially observable
Markov decision processes (POMDP) based on spectral decomposition methods.
While spectral methods have been previously employed for consistent learning of
(passive) latent variable models such as hidden Markov models, POMDPs are more
challenging... | computer science |
3,698 | Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of
Pairwise Comparisons | cs.LG | We study methods for aggregating pairwise comparison data in order to
estimate outcome probabilities for future comparisons among a collection of n
items. Working within a flexible framework that imposes only a form of strong
stochastic transitivity (SST), we introduce an adaptivity index defined by the
indifference se... | computer science |
3,699 | Online Influence Maximization under Independent Cascade Model with
Semi-Bandit Feedback | cs.LG | We study the stochastic online problem of learning to influence in a social
network with semi-bandit feedback, where we observe how users influence each
other. The problem combines challenges of limited feedback, because the
learning agent only observes the influenced portion of the network, and
combinatorial number of... | computer science |
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