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8,700 | Mathematics of Deep Learning | cs.LG | Recently there has been a dramatic increase in the performance of recognition
systems due to the introduction of deep architectures for representation
learning and classification. However, the mathematical reasons for this success
remain elusive. This tutorial will review recent work that aims to provide a
mathematical... | computer science |
8,701 | Learning Low-shot facial representations via 2D warping | cs.CV | In this work, we mainly study the influence of the 2D warping module for
one-shot face recognition. | computer science |
8,702 | Point-wise Convolutional Neural Network | cs.CV | Deep learning with 3D data such as reconstructed point clouds and CAD models
has received great research interests recently. However, the capability of
using point clouds with convolutional neural network has been so far not fully
explored. In this technical report, we present a convolutional neural network
for semanti... | computer science |
8,703 | The exploding gradient problem demystified - definition, prevalence,
impact, origin, tradeoffs, and solutions | cs.LG | Whereas it is believed that techniques such as Adam, batch normalization and,
more recently, SeLU nonlinearities "solve" the exploding gradient problem, we
show that this is not the case in general and that in a range of popular MLP
architectures, exploding gradients exist and that they limit the depth to which
network... | computer science |
8,704 | Pre-training Attention Mechanisms | cs.LG | Recurrent neural networks with differentiable attention mechanisms have had
success in generative and classification tasks. We show that the classification
performance of such models can be enhanced by guiding a randomly initialized
model to attend to salient regions of the input in early training iterations.
We furthe... | computer science |
8,705 | Super-sparse Learning in Similarity Spaces | cs.CV | In several applications, input samples are more naturally represented in
terms of similarities between each other, rather than in terms of feature
vectors. In these settings, machine-learning algorithms can become very
computationally demanding, as they may require matching the test samples
against a very large set of ... | computer science |
8,706 | Objects that Sound | cs.CV | In this paper our objectives are, first, networks that can embed audio and
visual inputs into a common space that is suitable for cross-modal retrieval;
and second, a network that can localize the object that sounds in an image,
given the audio signal. We achieve both these objectives by training from
unlabelled video ... | computer science |
8,707 | End-to-end weakly-supervised semantic alignment | cs.CV | We tackle the task of semantic alignment where the goal is to compute dense
semantic correspondence aligning two images depicting objects of the same
category. This is a challenging task due to large intra-class variation,
changes in viewpoint and background clutter. We present the following three
principal contributio... | computer science |
8,708 | Deep Learning with Lung Segmentation and Bone Shadow Exclusion
Techniques for Chest X-Ray Analysis of Lung Cancer | cs.LG | The recent progress of computing, machine learning, and especially deep
learning, for image recognition brings a meaningful effect for automatic
detection of various diseases from chest X-ray images (CXRs). Here efficiency
of lung segmentation and bone shadow exclusion techniques is demonstrated for
analysis of 2D CXRs... | computer science |
8,709 | Learning More Universal Representations for Transfer-Learning | cs.CV | Transfer learning is commonly used to address the problem of the prohibitive
need in annotated data when one want to classify visual content with a
Convolutional Neural Network (CNN). We address the problem of the universality
of the CNN-based representation of images in such a context. The
state-of-the-art consists in... | computer science |
8,710 | Stratified Transfer Learning for Cross-domain Activity Recognition | cs.CV | In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global doma... | computer science |
8,711 | Reducing Deep Network Complexity with Fourier Transform Methods | cs.CV | We propose a novel way that uses shallow densely connected neuron network
architectures to achieve superior performance to convolution based neural
networks (CNNs) approaches with the added benefits of lower computation burden
requiring dramatically less training examples to achieve high prediction
accuracy ($>98\%$). ... | computer science |
8,712 | Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial
Data | cs.CV | The k-Nearest Neighbor (kNN) classification approach is conceptually simple -
yet widely applied since it often performs well in practical applications.
However, using a global constant k does not always provide an optimal solution,
e.g., for datasets with an irregular density distribution of data points. This
paper pr... | computer science |
8,713 | LaVAN: Localized and Visible Adversarial Noise | cs.CV | Most works on adversarial examples for deep-learning based image classifiers
use noise that, while small, covers the entire image. We explore the case where
the noise is allowed to be visible but confined to a small, localized patch of
the image, without covering any of the main object(s) in the image. We show
that it ... | computer science |
8,714 | Meta-Tracker: Fast and Robust Online Adaptation for Visual Object
Trackers | cs.CV | This paper improves state-of-the-art visual object trackers that use online
adaptation. Our core contribution is an offline meta-learning-based method to
adjust the initial deep networks used in online adaptation-based tracking. The
meta learning is driven by the goal of deep networks that can quickly be
adapted to rob... | computer science |
8,715 | Conditional Probability Models for Deep Image Compression | cs.CV | Deep Neural Networks trained as image auto-encoders have recently emerged as
a promising direction for advancing the state of the art in image compression.
The key challenge in learning such networks is twofold: to deal with
quantization, and to control the trade-off between reconstruction error
(distortion) and entrop... | computer science |
8,716 | Visual Data Augmentation through Learning | cs.CV | The rapid progress in machine learning methods has been empowered by i) huge
datasets that have been collected and annotated, ii) improved engineering (e.g.
data pre-processing/normalization). The existing datasets typically include
several million samples, which constitutes their extension a colossal task. In
addition... | computer science |
8,717 | Curvature-based Comparison of Two Neural Networks | cs.LG | In this paper we show the similarities and differences of two deep neural
networks by comparing the manifolds composed of activation vectors in each
fully connected layer of them. The main contribution of this paper includes 1)
a new data generating algorithm which is crucial for determining the dimension
of manifolds;... | computer science |
8,718 | Convolutional Networks in Visual Environments | cs.CV | The puzzle of computer vision might find new challenging solutions when we
realize that most successful methods are working at image level, which is
remarkably more difficult than processing directly visual streams. In this
paper, we claim that their processing naturally leads to formulate the motion
invariance princip... | computer science |
8,719 | NDDR-CNN: Layer-wise Feature Fusing in Multi-Task CNN by Neural
Discriminative Dimensionality Reduction | cs.CV | State-of-the-art Convolutional Neural Network (CNN) benefits much from
multi-task learning (MTL), which learns multiple related tasks simultaneously
to obtain shared or mutually related representations for different tasks. The
most widely used MTL CNN structure is based on an empirical or heuristic split
on a specific ... | computer science |
8,720 | Neural Algebra of Classifiers | cs.CV | The world is fundamentally compositional, so it is natural to think of visual
recognition as the recognition of basic visually primitives that are composed
according to well-defined rules. This strategy allows us to recognize unseen
complex concepts from simple visual primitives. However, the current trend in
visual re... | computer science |
8,721 | Object category learning and retrieval with weak supervision | cs.CV | We consider the problem of retrieving objects from image data and learning to
classify them into meaningful semantic categories with minimal supervision. To
that end, we propose a fully differentiable unsupervised deep clustering
approach to learn semantic classes in an end-to-end fashion without individual
class label... | computer science |
8,722 | Document Image Classification with Intra-Domain Transfer Learning and
Stacked Generalization of Deep Convolutional Neural Networks | cs.CV | In this work, a region-based Deep Convolutional Neural Network framework is
proposed for document structure learning. The contribution of this work
involves efficient training of region based classifiers and effective
ensembling for document image classification. A primary level of `inter-domain'
transfer learning is u... | computer science |
8,723 | A Survey of Recent Advances in Texture Representation | cs.CV | Texture is a fundamental characteristic of many types of images, and texture
representation is one of the essential and challenging problems in computer
vision and pattern recognition which has attracted extensive research
attention. Since 2000, texture representations based on Bag of Words (BoW) and
on Convolutional N... | computer science |
8,724 | Real-time Prediction of Intermediate-Horizon Automotive Collision Risk | cs.CV | Advanced collision avoidance and driver hand-off systems can benefit from the
ability to accurately predict, in real time, the probability a vehicle will be
involved in a collision within an intermediate horizon of 10 to 20 seconds. The
rarity of collisions in real-world data poses a significant challenge to
developing... | computer science |
8,725 | The steerable graph Laplacian and its application to filtering image
data-sets | cs.CV | In recent years, improvements in various scientific image acquisition
techniques gave rise to the need for adaptive processing methods aimed for
large data-sets corrupted by noise and deformations. In this work, we consider
data-sets of images sampled from an underlying low-dimensional manifold (i.e.
an image-valued ma... | computer science |
8,726 | On the Feasibility of Generic Deep Disaggregation for Single-Load
Extraction | cs.LG | Recently, and with the growing development of big energy datasets,
data-driven learning techniques began to represent a potential solution to the
energy disaggregation problem outperforming engineered and hand-crafted models.
However, most proposed deep disaggregation models are load-dependent in the
sense that either ... | computer science |
8,727 | Automated dataset generation for image recognition using the example of
taxonomy | cs.CV | This master thesis addresses the subject of automatically generating a
dataset for image recognition, which takes a lot of time when being done
manually. As the thesis was written with motivation from the context of the
biodiversity workgroup at the City University of Applied Sciences Bremen, the
classification of taxo... | computer science |
8,728 | A machine learning approach to reconstruction of heart surface
potentials from body surface potentials | cs.LG | Invasive cardiac catheterisation is a common procedure that is carried out
before surgical intervention. Yet, invasive cardiac diagnostics are full of
risks, especially for young children. Decades of research has been conducted on
the so called inverse problem of electrocardiography, which can be used to
reconstruct He... | computer science |
8,729 | A Spatial Mapping Algorithm with Applications in Deep Learning-Based
Structure Classification | cs.CV | Convolutional Neural Network (CNN)-based machine learning systems have made
breakthroughs in feature extraction and image recognition tasks in two
dimensions (2D). Although there is significant ongoing work to apply CNN
technology to domains involving complex 3D data, the success of such efforts
has been constrained, i... | computer science |
8,730 | VISER: Visual Self-Regularization | cs.CV | In this work, we propose the use of large set of unlabeled images as a source
of regularization data for learning robust visual representation. Given a
visual model trained by a labeled dataset in a supervised fashion, we augment
our training samples by incorporating large number of unlabeled data and train
a semi-supe... | computer science |
8,731 | Combinets: Learning New Classifiers via Recombination | cs.LG | Problems with few examples of a new class of objects prove challenging to
most classifiers. One solution to is to reuse existing data through transfer
methods such as one-shot learning or domain adaption. However these approaches
require an explicit hand-authored or learned definition of how reuse can occur.
We present... | computer science |
8,732 | On the Universal Approximability of Quantized ReLU Neural Networks | cs.LG | Compression is a key step to deploy large neural networks on
resource-constrained platforms. As a popular compression technique,
quantization constrains the number of distinct weight values and thus reducing
the number of bits required to represent and store each weight. In this paper,
we study the representation power... | computer science |
8,733 | Systematic Weight Pruning of DNNs using Alternating Direction Method of
Multipliers | cs.LG | We present a systematic weight pruning framework of deep neural networks
(DNNs) using the alternating direction method of multipliers (ADMM). We first
formulate the weight pruning problem of DNNs as a constrained nonconvex
optimization problem, and then adopt the ADMM framework for systematic weight
pruning. We show th... | computer science |
8,734 | Locally Adaptive Learning Loss for Semantic Image Segmentation | cs.CV | We propose a novel locally adaptive learning estimator for enhancing the
inter- and intra- discriminative capabilities of Deep Neural Networks, which
can be used as improved loss layer for semantic image segmentation tasks. Most
loss layers compute pixel-wise cost between feature maps and ground truths,
ignoring spatia... | computer science |
8,735 | Convolutional Neural Networks combined with Runge-Kutta Methods | cs.CV | A convolutional neural network for image classification can be constructed
following some mathematical ways since it models the ventral stream in visual
cortex which is regarded as a multi-period dynamical system. In this paper, a
new point of view is proposed for constructing network models as well as
providing a dire... | computer science |
8,736 | Deep learning for conifer/deciduous classification of airborne LiDAR 3D
point clouds representing individual trees | cs.LG | The purpose of this study was to investigate the use of deep learning for
coniferous/deciduous classification of individual trees from airborne LiDAR
data. To enable efficient processing by a deep convolutional neural network
(CNN), we designed two discrete representations using leaf-off and leaf-on
LiDAR data: a digit... | computer science |
8,737 | Solving Inverse Computational Imaging Problems using Deep Pixel-level
Prior | cs.CV | Generative models based on deep neural networks are quite powerful in
modelling natural image statistics. In particular, deep auto-regressive models
provide state of the art performance, in terms of log likelihood scores, by
modelling tractable densities over the image manifold. In this work, we employ
a learned deep a... | computer science |
8,738 | Query-Free Attacks on Industry-Grade Face Recognition Systems under
Resource Constraints | cs.LG | To attack a deep neural network (DNN) based Face Recognition (FR) system, one
needs to build \textit{substitute} models to simulate the target, so the
adversarial examples discovered could also mislead the target. Such
\textit{transferability} is achieved in recent studies through querying the
target to obtain data for... | computer science |
8,739 | Extracting V2V Encountering Scenarios from Naturalistic Driving Database | cs.LG | It is necessary to thoroughly evaluate the effectiveness and safety of
Connected Vehicles (CVs) algorithm before their release and deployment. Current
evaluation approach mainly relies on simulation platform with the
single-vehicle driving model. The main drawback of it is the lack of network
realism. To overcome this ... | computer science |
8,740 | Tell Me Where to Look: Guided Attention Inference Network | cs.CV | Weakly supervised learning with only coarse labels can obtain visual
explanations of deep neural network such as attention maps by back-propagating
gradients. These attention maps are then available as priors for tasks such as
object localization and semantic segmentation. In one common framework we
address three short... | computer science |
8,741 | DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally
Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images | cs.CV | Given that the neural and connective tissues of the optic nerve head (ONH)
exhibit complex morphological changes with the development and progression of
glaucoma, their simultaneous isolation from optical coherence tomography (OCT)
images may be of great interest for the clinical diagnosis and management of
this pathol... | computer science |
8,742 | Satellite imagery analysis for operational damage assessment in
Emergency situations | cs.CV | When major disaster occurs the questions are raised how to estimate the
damage in time to support the decision making process and relief efforts by
local authorities or humanitarian teams. In this paper we consider the use of
Machine Learning and Computer Vision on remote sensing imagery to improve time
efficiency of a... | computer science |
8,743 | Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural
Network | cs.CV | Coronary calcium causes beam hardening and blooming artifacts on cardiac
computed tomography angiography (CTA) images, which lead to overestimation of
lumen stenosis and reduction of diagnostic specificity. To properly remove
coronary calcification and restore arterial lumen precisely, we propose a
machine learning-bas... | computer science |
8,744 | Knowledge Transfer with Jacobian Matching | cs.LG | Classical distillation methods transfer representations from a "teacher"
neural network to a "student" network by matching their output activations.
Recent methods also match the Jacobians, or the gradient of output activations
with the input. However, this involves making some ad hoc decisions, in
particular, the choi... | computer science |
8,745 | Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with
Category Shift | cs.LG | Unsupervised domain adaptation (UDA) conventionally assumes labeled source
samples coming from a single underlying source distribution. Whereas in
practical scenario, labeled data are typically collected from diverse sources.
The multiple sources are different not only from the target but also from each
other, thus, do... | computer science |
8,746 | Deep Continuous Clustering | cs.LG | Clustering high-dimensional datasets is hard because interpoint distances
become less informative in high-dimensional spaces. We present a clustering
algorithm that performs nonlinear dimensionality reduction and clustering
jointly. The data is embedded into a lower-dimensional space by a deep
autoencoder. The autoenco... | computer science |
8,747 | Totally Looks Like - How Humans Compare, Compared to Machines | cs.CV | Perceptual judgment of image similarity by humans relies on rich internal
representations ranging from low-level features to high-level concepts, scene
properties and even cultural associations. However, existing methods and
datasets attempting to explain perceived similarity use stimuli which arguably
do not cover the... | computer science |
8,748 | ST-GAN: Spatial Transformer Generative Adversarial Networks for Image
Compositing | cs.CV | We address the problem of finding realistic geometric corrections to a
foreground object such that it appears natural when composited into a
background image. To achieve this, we propose a novel Generative Adversarial
Network (GAN) architecture that utilizes Spatial Transformer Networks (STNs) as
the generator, which w... | computer science |
8,749 | The Contextual Loss for Image Transformation with Non-Aligned Data | cs.CV | Feed-forward CNNs trained for image transformation problems rely on loss
functions that measure the similarity between the generated image and a target
image. Most of the common loss functions assume that these images are spatially
aligned and compare pixels at corresponding locations. However, for many tasks,
aligned ... | computer science |
8,750 | Early Start Intention Detection of Cyclists Using Motion History Images
and a Deep Residual Network | cs.CV | In this article, we present a novel approach to detect starting motions of
cyclists in real world traffic scenarios based on Motion History Images (MHIs).
The method uses a deep Convolutional Neural Network (CNN) with a residual
network architecture (ResNet), which is commonly used in image classification
and detection... | computer science |
8,751 | Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable
Deep Neural Network | cs.CV | The Sentinel-2 satellite mission delivers multi-spectral imagery with 13
spectral bands, acquired at three different spatial resolutions. The aim of
this research is to super-resolve the lower-resolution (20 m and 60 m Ground
Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data
cube at the maxima... | computer science |
8,752 | Training of Convolutional Networks on Multiple Heterogeneous Datasets
for Street Scene Semantic Segmentation | cs.CV | We propose a convolutional network with hierarchical classifiers for
per-pixel semantic segmentation, which is able to be trained on multiple,
heterogeneous datasets and exploit their semantic hierarchy. Our network is the
first to be simultaneously trained on three different datasets from the
intelligent vehicles doma... | computer science |
8,753 | Local Spectral Graph Convolution for Point Set Feature Learning | cs.CV | Feature learning on point clouds has shown great promise, with the
introduction of effective and generalizable deep learning frameworks such as
pointnet++. Thus far, however, point features have been abstracted in an
independent and isolated manner, ignoring the relative layout of neighboring
points as well as their fe... | computer science |
8,754 | I Know What You See: Power Side-Channel Attack on Convolutional Neural
Network Accelerators | cs.CV | Deep learning has become the de-facto computational paradigm for various
kinds of perception problems, including many privacy-sensitive applications
such as online medical image analysis. No doubt to say, the data privacy of
these deep learning systems is a serious concern. Different from previous
research focusing on ... | computer science |
8,755 | Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on
Multimodal MR Image Using Convolutional Neural Network | cs.CV | The patient with ischemic stroke can benefit most from the earliest possible
definitive diagnosis. While the high quality medical resources are quite scarce
across the globe, an automated diagnostic tool is expected in analyzing the
magnetic resonance (MR) images to provide reference in clinical diagnosis. In
this pape... | computer science |
8,756 | Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with
Local Point Spread Function Estimation By Use Of Convolutional Neural
Networks | cs.CV | We present a semi-blind, spatially-variant deconvolution technique aimed at
optical microscopy that combines a local estimation step of the point spread
function (PSF) and deconvolution using a spatially variant, regularized
Richardson-Lucy algorithm. To find the local PSF map in a computationally
tractable way, we tra... | computer science |
8,757 | Dynamic Sampling Convolutional Neural Networks | cs.CV | We present Dynamic Sampling Convolutional Neural Networks (DSCNN), where the
position-specific kernels learn from not only the current position but also
multiple sampled neighbour regions. During sampling, residual learning is
introduced to ease training and an attention mechanism is applied to fuse
features from diffe... | computer science |
8,758 | Unsupervised Representation Learning by Predicting Image Rotations | cs.CV | Over the last years, deep convolutional neural networks (ConvNets) have
transformed the field of computer vision thanks to their unparalleled capacity
to learn high level semantic image features. However, in order to successfully
learn those features, they usually require massive amounts of manually labeled
data, which... | computer science |
8,759 | What do Deep Networks Like to See? | cs.CV | We propose a novel way to measure and understand convolutional neural
networks by quantifying the amount of input signal they let in. To do this, an
autoencoder (AE) was fine-tuned on gradients from a pre-trained classifier with
fixed parameters. We compared the reconstructed samples from AEs that were
fine-tuned on a ... | computer science |
8,760 | Group Normalization | cs.CV | Batch Normalization (BN) is a milestone technique in the development of deep
learning, enabling various networks to train. However, normalizing along the
batch dimension introduces problems --- BN's error increases rapidly when the
batch size becomes smaller, caused by inaccurate batch statistics estimation.
This limit... | computer science |
8,761 | Automated Pattern Detection--An Algorithm for Constructing Optimally
Synchronizing Multi-Regular Language Filters | cs.CV | In the computational-mechanics structural analysis of one-dimensional
cellular automata the following automata-theoretic analogue of the
\emph{change-point problem} from time series analysis arises: \emph{Given a
string $\sigma$ and a collection $\{\mc{D}_i\}$ of finite automata, identify
the regions of $\sigma$ that b... | computer science |
8,762 | Name Strategy: Its Existence and Implications | cs.CL | It is argued that colour name strategy, object name strategy, and chunking
strategy in memory are all aspects of the same general phenomena, called
stereotyping. It is pointed out that the Berlin-Kay universal partial ordering
of colours and the frequency of traffic accidents classified by colour are
surprisingly simil... | computer science |
8,763 | Events in Property Patterns | cs.SE | A pattern-based approach to the presentation, codification and reuse of
property specifications for finite-state verification was proposed by Dwyer and
his collegues. The patterns enable non-experts to read and write formal
specifications for realistic systems and facilitate easy conversion of
specifications between fo... | computer science |
8,764 | Modeling Ambiguity in a Multi-Agent System | cs.CL | This paper investigates the formal pragmatics of ambiguous expressions by
modeling ambiguity in a multi-agent system. Such a framework allows us to give
a more refined notion of the kind of information that is conveyed by ambiguous
expressions. We analyze how ambiguity affects the knowledge of the dialog
participants a... | computer science |
8,765 | Tree-gram Parsing: Lexical Dependencies and Structural Relations | cs.CL | This paper explores the kinds of probabilistic relations that are important
in syntactic disambiguation. It proposes that two widely used kinds of
relations, lexical dependencies and structural relations, have complementary
disambiguation capabilities. It presents a new model based on structural
relations, the Tree-gra... | computer science |
8,766 | Quantitative Neural Network Model of the Tip-of-the-Tongue Phenomenon
Based on Synthesized Memory-Psycholinguistic-Metacognitive Approach | cs.CL | A new three-stage computer artificial neural network model of the
tip-of-the-tongue phenomenon is proposed. Each word's node is build from some
interconnected learned auto-associative two-layer neural networks each of which
represents separate word's semantic, lexical, or phonological components. The
model synthesizes ... | computer science |
8,767 | Three-Stage Quantitative Neural Network Model of the Tip-of-the-Tongue
Phenomenon | cs.CL | A new three-stage computer artificial neural network model of the
tip-of-the-tongue phenomenon is shortly described, and its stochastic nature
was demonstrated. A way to calculate strength and appearance probability of
tip-of-the-tongue states, neural network mechanism of feeling-of-knowing
phenomenon are proposed. The... | computer science |
8,768 | The tip-of-the-tongue phenomenon: Irrelevant neural network localization
or disruption of its interneuron links ? | cs.CL | On the base of recently proposed three-stage quantitative neural network
model of the tip-of-the-tongue (TOT) phenomenon a possibility to occur of TOT
states coursed by neural network interneuron links' disruption has been
studied. Using a numerical example it was found that TOTs coursed by interneron
links' disruption... | computer science |
8,769 | The partition semantics of questions, syntactically | cs.CL | Groenendijk and Stokhof (1984, 1996; Groenendijk 1999) provide a logically
attractive theory of the semantics of natural language questions, commonly
referred to as the partition theory. Two central notions in this theory are
entailment between questions and answerhood. For example, the question "Who is
going to the pa... | computer science |
8,770 | Question answering: from partitions to Prolog | cs.CL | We implement Groenendijk and Stokhof's partition semantics of questions in a
simple question answering algorithm. The algorithm is sound, complete, and
based on tableau theorem proving. The algorithm relies on a syntactic
characterization of answerhood: Any answer to a question is equivalent to some
formula built up on... | computer science |
8,771 | Bayesian Information Extraction Network | cs.CL | Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various
aspects of language in one model. Many existing algorithms developed for
learning and inference in DBNs are applicable to probabilistic language
modeling. To demonstrate the potential of DBNs for natural language processing,
we employ a DBN in a... | computer science |
8,772 | Multi-dimensional Type Theory: Rules, Categories, and Combinators for
Syntax and Semantics | cs.CL | We investigate the possibility of modelling the syntax and semantics of
natural language by constraints, or rules, imposed by the multi-dimensional
type theory Nabla. The only multiplicity we explicitly consider is two, namely
one dimension for the syntax and one dimension for the semantics, but the
general perspective... | computer science |
8,773 | The role of robust semantic analysis in spoken language dialogue systems | cs.CL | In this paper we summarized a framework for designing grammar-based procedure
for the automatic extraction of the semantic content from spoken queries.
Starting with a case study and following an approach which combines the notions
of fuzziness and robustness in sentence parsing, we showed we built practical
domain-dep... | computer science |
8,774 | Semantic filtering by inference on domain knowledge in spoken dialogue
systems | cs.CL | General natural dialogue processing requires large amounts of domain
knowledge as well as linguistic knowledge in order to ensure acceptable
coverage and understanding. There are several ways of integrating lexical
resources (e.g. dictionaries, thesauri) and knowledge bases or ontologies at
different levels of dialogue... | computer science |
8,775 | Metalinguistic Information Extraction for Terminology | cs.CL | This paper describes and evaluates the Metalinguistic Operation Processor
(MOP) system for automatic compilation of metalinguistic information from
technical and scientific documents. This system is designed to extract
non-standard terminological resources that we have called Metalinguistic
Information Databases (or MI... | computer science |
8,776 | Universal Similarity | cs.IR | We survey a new area of parameter-free similarity distance measures useful in
data-mining, pattern recognition, learning and automatic semantics extraction.
Given a family of distances on a set of objects, a distance is universal up to
a certain precision for that family if it minorizes every distance in the
family bet... | computer science |
8,777 | Explorations in engagement for humans and robots | cs.AI | This paper explores the concept of engagement, the process by which
individuals in an interaction start, maintain and end their perceived
connection to one another. The paper reports on one aspect of engagement among
human interactors--the effect of tracking faces during an interaction. It also
describes the architectu... | computer science |
8,778 | Communication of Social Agents and the Digital City - A Semiotic
Perspective | cs.AI | This paper investigates the concept of digital city. First, a functional
analysis of a digital city is made in the light of the modern study of
urbanism; similarities between the virtual and urban constructions are pointed
out. Next, a semiotic perspective on the subject matter is elaborated, and a
terminological basis... | computer science |
8,779 | Practical Approach to Knowledge-based Question Answering with Natural
Language Understanding and Advanced Reasoning | cs.CL | This research hypothesized that a practical approach in the form of a
solution framework known as Natural Language Understanding and Reasoning for
Intelligence (NaLURI), which combines full-discourse natural language
understanding, powerful representation formalism capable of exploiting
ontological information and reas... | computer science |
8,780 | The Generation of Textual Entailment with NLML in an Intelligent
Dialogue system for Language Learning CSIEC | cs.CL | This research report introduces the generation of textual entailment within
the project CSIEC (Computer Simulation in Educational Communication), an
interactive web-based human-computer dialogue system with natural language for
English instruction. The generation of textual entailment (GTE) is critical to
the further i... | computer science |
8,781 | Mining Meaning from Wikipedia | cs.AI | Wikipedia is a goldmine of information; not just for its many readers, but
also for the growing community of researchers who recognize it as a resource of
exceptional scale and utility. It represents a vast investment of manual effort
and judgment: a huge, constantly evolving tapestry of concepts and relations
that is ... | computer science |
8,782 | Syntactic Topic Models | cs.CL | The syntactic topic model (STM) is a Bayesian nonparametric model of language
that discovers latent distributions of words (topics) that are both
semantically and syntactically coherent. The STM models dependency parsed
corpora where sentences are grouped into documents. It assumes that each word
is drawn from a latent... | computer science |
8,783 | Emotional State Categorization from Speech: Machine vs. Human | cs.CL | This paper presents our investigations on emotional state categorization from
speech signals with a psychologically inspired computational model against
human performance under the same experimental setup. Based on psychological
studies, we propose a multistage categorization strategy which allows
establishing an autom... | computer science |
8,784 | Self reference in word definitions | cs.CL | Dictionaries are inherently circular in nature. A given word is linked to a
set of alternative words (the definition) which in turn point to further
descendants. Iterating through definitions in this way, one typically finds
that definitions loop back upon themselves. The graph formed by such
definitional relations is ... | computer science |
8,785 | Solving puzzles described in English by automated translation to answer
set programming and learning how to do that translation | cs.CL | We present a system capable of automatically solving combinatorial logic
puzzles given in (simplified) English. It involves translating the English
descriptions of the puzzles into answer set programming(ASP) and using ASP
solvers to provide solutions of the puzzles. To translate the descriptions, we
use a lambda-calcu... | computer science |
8,786 | Event in Compositional Dynamic Semantics | cs.CL | We present a framework which constructs an event-style dis- course semantics.
The discourse dynamics are encoded in continuation semantics and various
rhetorical relations are embedded in the resulting interpretation of the
framework. We assume discourse and sentence are distinct semantic objects, that
play different r... | computer science |
8,787 | Encoding Phases using Commutativity and Non-commutativity in a Logical
Framework | cs.CL | This article presents an extension of Minimalist Categorial Gram- mars (MCG)
to encode Chomsky's phases. These grammars are based on Par- tially Commutative
Logic (PCL) and encode properties of Minimalist Grammars (MG) of Stabler. The
first implementation of MCG were using both non- commutative properties (to
respect t... | computer science |
8,788 | TopicViz: Semantic Navigation of Document Collections | cs.HC | When people explore and manage information, they think in terms of topics and
themes. However, the software that supports information exploration sees text
at only the surface level. In this paper we show how topic modeling -- a
technique for identifying latent themes across large collections of documents
-- can suppor... | computer science |
8,789 | Pbm: A new dataset for blog mining | cs.AI | Text mining is becoming vital as Web 2.0 offers collaborative content
creation and sharing. Now Researchers have growing interest in text mining
methods for discovering knowledge. Text mining researchers come from variety of
areas like: Natural Language Processing, Computational Linguistic, Machine
Learning, and Statis... | computer science |
8,790 | Modelling Social Structures and Hierarchies in Language Evolution | cs.CL | Language evolution might have preferred certain prior social configurations
over others. Experiments conducted with models of different social structures
(varying subgroup interactions and the role of a dominant interlocutor) suggest
that having isolated agent groups rather than an interconnected agent is more
advantag... | computer science |
8,791 | Establishing linguistic conventions in task-oriented primeval dialogue | cs.CL | In this paper, we claim that language is likely to have emerged as a
mechanism for coordinating the solution of complex tasks. To confirm this
thesis, computer simulations are performed based on the coordination task
presented by Garrod & Anderson (1987). The role of success in task-oriented
dialogue is analytically ev... | computer science |
8,792 | Quantum Interference in Cognition: Structural Aspects of the Brain | cs.AI | We identify the presence of typically quantum effects, namely 'superposition'
and 'interference', in what happens when human concepts are combined, and
provide a quantum model in complex Hilbert space that represents faithfully
experimental data measuring the situation of combining concepts. Our model
shows how 'interf... | computer science |
8,793 | Inferring Informational Goals from Free-Text Queries: A Bayesian
Approach | cs.IR | People using consumer software applications typically do not use technical
jargon when querying an online database of help topics. Rather, they attempt to
communicate their goals with common words and phrases that describe software
functionality in terms of structure and objects they understand. We describe a
Bayesian ... | computer science |
8,794 | A Computational Model to Disentangle Semantic Information Embedded in
Word Association Norms | cs.CL | Two well-known databases of semantic relationships between pairs of words
used in psycholinguistics, feature-based and association-based, are studied as
complex networks. We propose an algorithm to disentangle feature based
relationships from free association semantic networks. The algorithm uses the
rich topology of t... | computer science |
8,795 | Concrete Sentence Spaces for Compositional Distributional Models of
Meaning | cs.CL | Coecke, Sadrzadeh, and Clark (arXiv:1003.4394v1 [cs.CL]) developed a
compositional model of meaning for distributional semantics, in which each word
in a sentence has a meaning vector and the distributional meaning of the
sentence is a function of the tensor products of the word vectors. Abstractly
speaking, this funct... | computer science |
8,796 | Artex is AnotheR TEXt summarizer | cs.IR | This paper describes Artex, another algorithm for Automatic Text
Summarization. In order to rank sentences, a simple inner product is calculated
between each sentence, a document vector (text topic) and a lexical vector
(vocabulary used by a sentence). Summaries are then generated by assembling the
highest ranked sente... | computer science |
8,797 | Opinion Mining for Relating Subjective Expressions and Annual Earnings
in US Financial Statements | cs.CL | Financial statements contain quantitative information and manager's
subjective evaluation of firm's financial status. Using information released in
U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for
quality financial decisions. To extract such opinioned statements from the
reports, we built... | computer science |
8,798 | The automatic creation of concept maps from documents written using
morphologically rich languages | cs.IR | Concept map is a graphical tool for representing knowledge. They have been
used in many different areas, including education, knowledge management,
business and intelligence. Constructing of concept maps manually can be a
complex task; an unskilled person may encounter difficulties in determining and
positioning concep... | computer science |
8,799 | Typed Hilbert Epsilon Operators and the Semantics of Determiner Phrases
(Invited Lecture) | cs.CL | The semantics of determiner phrases, be they definite de- scriptions,
indefinite descriptions or quantified noun phrases, is often as- sumed to be a
fully solved question: common nouns are properties, and determiners are
generalised quantifiers that apply to two predicates: the property
corresponding to the common noun... | computer science |
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