Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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3,800 | Active Orthogonal Matching Pursuit for Sparse Subspace Clustering | cs.LG | Sparse Subspace Clustering (SSC) is a state-of-the-art method for clustering
high-dimensional data points lying in a union of low-dimensional subspaces.
However, while $\ell_1$ optimization-based SSC algorithms suffer from high
computational complexity, other variants of SSC, such as Orthogonal Matching
Pursuit-based S... | computer science |
3,801 | First and Second Order Methods for Online Convolutional Dictionary
Learning | cs.LG | Convolutional sparse representations are a form of sparse representation with
a structured, translation invariant dictionary. Most convolutional dictionary
learning algorithms to date operate in batch mode, requiring simultaneous
access to all training images during the learning process, which results in
very high memo... | computer science |
3,802 | Neural Architectures for Robot Intelligence | cs.RO | We argue that the direct experimental approaches to elucidate the
architecture of higher brains may benefit from insights gained from exploring
the possibilities and limits of artificial control architectures for robot
systems. We present some of our recent work that has been motivated by that
view and that is centered... | computer science |
3,803 | HoME: a Household Multimodal Environment | cs.AI | We introduce HoME: a Household Multimodal Environment for artificial agents
to learn from vision, audio, semantics, physics, and interaction with objects
and other agents, all within a realistic context. HoME integrates over 45,000
diverse 3D house layouts based on the SUNCG dataset, a scale which may
facilitate learni... | computer science |
3,804 | File mapping Rule-based DBMS and Natural Language Processing | cs.CL | This paper describes the system of storage, extract and processing of
information structured similarly to the natural language. For recursive
inference the system uses the rules having the same representation, as the
data. The environment of storage of information is provided with the File
Mapping (SHM) mechanism of op... | computer science |
3,805 | Extracting Biomolecular Interactions Using Semantic Parsing of
Biomedical Text | cs.CL | We advance the state of the art in biomolecular interaction extraction with
three contributions: (i) We show that deep, Abstract Meaning Representations
(AMR) significantly improve the accuracy of a biomolecular interaction
extraction system when compared to a baseline that relies solely on surface-
and syntax-based fe... | computer science |
3,806 | Detecting Events and Patterns in Large-Scale User Generated Textual
Streams with Statistical Learning Methods | cs.LG | A vast amount of textual web streams is influenced by events or phenomena
emerging in the real world. The social web forms an excellent modern paradigm,
where unstructured user generated content is published on a regular basis and
in most occasions is freely distributed. The present Ph.D. Thesis deals with
the problem ... | computer science |
3,807 | Emergent Communication in a Multi-Modal, Multi-Step Referential Game | cs.LG | Inspired by previous work on emergent communication in referential games, we
propose a novel multi-modal, multi-step referential game, where the sender and
receiver have access to distinct modalities of an object, and their information
exchange is bidirectional and of arbitrary duration. The multi-modal multi-step
sett... | computer science |
3,808 | Phoneme recognition in TIMIT with BLSTM-CTC | cs.CL | We compare the performance of a recurrent neural network with the best
results published so far on phoneme recognition in the TIMIT database. These
published results have been obtained with a combination of classifiers.
However, in this paper we apply a single recurrent neural network to the same
task. Our recurrent ne... | computer science |
3,809 | Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri
Multiword Expression (MWE) Identification | cs.CL | This paper deals with the identification of Multiword Expressions (MWEs) in
Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the
Eight Schedule of Indian Constitution. MWE plays an important role in the
applications of Natural Language Processing(NLP) like Machine Translation, Part
of Speech tagg... | computer science |
3,810 | Generating Sequences With Recurrent Neural Networks | cs.NE | This paper shows how Long Short-term Memory recurrent neural networks can be
used to generate complex sequences with long-range structure, simply by
predicting one data point at a time. The approach is demonstrated for text
(where the data are discrete) and online handwriting (where the data are
real-valued). It is the... | computer science |
3,811 | Implementation Of Back-Propagation Neural Network For Isolated Bangla
Speech Recognition | cs.CL | This paper is concerned with the development of Back-propagation Neural
Network for Bangla Speech Recognition. In this paper, ten bangla digits were
recorded from ten speakers and have been recognized. The features of these
speech digits were extracted by the method of Mel Frequency Cepstral
Coefficient (MFCC) analysis... | computer science |
3,812 | Speech Recognition with Deep Recurrent Neural Networks | cs.NE | Recurrent neural networks (RNNs) are a powerful model for sequential data.
End-to-end training methods such as Connectionist Temporal Classification make
it possible to train RNNs for sequence labelling problems where the
input-output alignment is unknown. The combination of these methods with the
Long Short-term Memor... | computer science |
3,813 | Reasoning About Pragmatics with Neural Listeners and Speakers | cs.CL | We present a model for pragmatically describing scenes, in which contrastive
behavior results from a combination of inference-driven pragmatics and learned
semantics. Like previous learned approaches to language generation, our model
uses a simple feature-driven architecture (here a pair of neural "listener" and
"speak... | computer science |
3,814 | Generating Chinese Classical Poems with RNN Encoder-Decoder | cs.CL | We take the generation of Chinese classical poem lines as a
sequence-to-sequence learning problem, and build a novel system based on the
RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a
topic word as input. Our system can jointly learn semantic meaning within a
single line, semantic releva... | computer science |
3,815 | Parsing using a grammar of word association vectors | cs.CL | This paper was was first drafted in 2001 as a formalization of the system
described in U.S. patent U.S. 7,392,174. It describes a system for implementing
a parser based on a kind of cross-product over vectors of contextually similar
words. It is being published now in response to nascent interest in vector
combination ... | computer science |
3,816 | Convolutional Neural Networks for Sentence Classification | cs.CL | We report on a series of experiments with convolutional neural networks (CNN)
trained on top of pre-trained word vectors for sentence-level classification
tasks. We show that a simple CNN with little hyperparameter tuning and static
vectors achieves excellent results on multiple benchmarks. Learning
task-specific vecto... | computer science |
3,817 | Non-linear Learning for Statistical Machine Translation | cs.CL | Modern statistical machine translation (SMT) systems usually use a linear
combination of features to model the quality of each translation hypothesis.
The linear combination assumes that all the features are in a linear
relationship and constrains that each feature interacts with the rest features
in an linear manner, ... | computer science |
3,818 | End-To-End Memory Networks | cs.NE | We introduce a neural network with a recurrent attention model over a
possibly large external memory. The architecture is a form of Memory Network
(Weston et al., 2015) but unlike the model in that work, it is trained
end-to-end, and hence requires significantly less supervision during training,
making it more generall... | computer science |
3,819 | Genetic approach for arabic part of speech tagging | cs.CL | With the growing number of textual resources available, the ability to
understand them becomes critical. An essential first step in understanding
these sources is the ability to identify the part of speech in each sentence.
Arabic is a morphologically rich language, wich presents a challenge for part
of speech tagging.... | computer science |
3,820 | Constructing Long Short-Term Memory based Deep Recurrent Neural Networks
for Large Vocabulary Speech Recognition | cs.CL | Long short-term memory (LSTM) based acoustic modeling methods have recently
been shown to give state-of-the-art performance on some speech recognition
tasks. To achieve a further performance improvement, in this research, deep
extensions on LSTM are investigated considering that deep hierarchical model
has turned out t... | computer science |
3,821 | Recurrent-Neural-Network for Language Detection on Twitter
Code-Switching Corpus | cs.NE | Mixed language data is one of the difficult yet less explored domains of
natural language processing. Most research in fields like machine translation
or sentiment analysis assume monolingual input. However, people who are capable
of using more than one language often communicate using multiple languages at
the same ti... | computer science |
3,822 | Neural CRF Parsing | cs.CL | This paper describes a parsing model that combines the exact dynamic
programming of CRF parsing with the rich nonlinear featurization of neural net
approaches. Our model is structurally a CRF that factors over anchored rule
productions, but instead of linear potential functions based on sparse
features, we use nonlinea... | computer science |
3,823 | Depth-Gated LSTM | cs.NE | In this short note, we present an extension of long short-term memory (LSTM)
neural networks to using a depth gate to connect memory cells of adjacent
layers. Doing so introduces a linear dependence between lower and upper layer
recurrent units. Importantly, the linear dependence is gated through a gating
function, whi... | computer science |
3,824 | Very Deep Multilingual Convolutional Neural Networks for LVCSR | cs.CL | Convolutional neural networks (CNNs) are a standard component of many current
state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR)
systems. However, CNNs in LVCSR have not kept pace with recent advances in
other domains where deeper neural networks provide superior performance. In
this paper we propo... | computer science |
3,825 | Semi-supervised Question Retrieval with Gated Convolutions | cs.CL | Question answering forums are rapidly growing in size with no effective
automated ability to refer to and reuse answers already available for previous
posted questions. In this paper, we develop a methodology for finding
semantically related questions. The task is difficult since 1) key pieces of
information are often ... | computer science |
3,826 | Investigating gated recurrent neural networks for speech synthesis | cs.CL | Recently, recurrent neural networks (RNNs) as powerful sequence models have
re-emerged as a potential acoustic model for statistical parametric speech
synthesis (SPSS). The long short-term memory (LSTM) architecture is
particularly attractive because it addresses the vanishing gradient problem in
standard RNNs, making ... | computer science |
3,827 | Long Short-Term Memory-Networks for Machine Reading | cs.CL | In this paper we address the question of how to render sequence-level
networks better at handling structured input. We propose a machine reading
simulator which processes text incrementally from left to right and performs
shallow reasoning with memory and attention. The reader extends the Long
Short-Term Memory archite... | computer science |
3,828 | Recurrent Neural Network Grammars | cs.CL | We introduce recurrent neural network grammars, probabilistic models of
sentences with explicit phrase structure. We explain efficient inference
procedures that allow application to both parsing and language modeling.
Experiments show that they provide better parsing in English than any single
previously published supe... | computer science |
3,829 | Latent Predictor Networks for Code Generation | cs.CL | Many language generation tasks require the production of text conditioned on
both structured and unstructured inputs. We present a novel neural network
architecture which generates an output sequence conditioned on an arbitrary
number of input functions. Crucially, our approach allows both the choice of
conditioning co... | computer science |
3,830 | System Combination for Short Utterance Speaker Recognition | cs.CL | For text-independent short-utterance speaker recognition (SUSR), the
performance often degrades dramatically. This paper presents a combination
approach to the SUSR tasks with two phonetic-aware systems: one is the
DNN-based i-vector system and the other is our recently proposed
subregion-based GMM-UBM system. The form... | computer science |
3,831 | TheanoLM - An Extensible Toolkit for Neural Network Language Modeling | cs.CL | We present a new tool for training neural network language models (NNLMs),
scoring sentences, and generating text. The tool has been written using Python
library Theano, which allows researcher to easily extend it and tune any aspect
of the training process. Regardless of the flexibility, Theano is able to
generate ext... | computer science |
3,832 | Iterative Alternating Neural Attention for Machine Reading | cs.CL | We propose a novel neural attention architecture to tackle machine
comprehension tasks, such as answering Cloze-style queries with respect to a
document. Unlike previous models, we do not collapse the query into a single
vector, instead we deploy an iterative alternating attention mechanism that
allows a fine-grained e... | computer science |
3,833 | Rationalizing Neural Predictions | cs.CL | Prediction without justification has limited applicability. As a remedy, we
learn to extract pieces of input text as justifications -- rationales -- that
are tailored to be short and coherent, yet sufficient for making the same
prediction. Our approach combines two modular components, generator and
encoder, which are t... | computer science |
3,834 | Word Representation Models for Morphologically Rich Languages in Neural
Machine Translation | cs.NE | Dealing with the complex word forms in morphologically rich languages is an
open problem in language processing, and is particularly important in
translation. In contrast to most modern neural systems of translation, which
discard the identity for rare words, in this paper we propose several
architectures for learning ... | computer science |
3,835 | Query-Reduction Networks for Question Answering | cs.CL | In this paper, we study the problem of question answering when reasoning over
multiple facts is required. We propose Query-Reduction Network (QRN), a variant
of Recurrent Neural Network (RNN) that effectively handles both short-term
(local) and long-term (global) sequential dependencies to reason over multiple
facts. Q... | computer science |
3,836 | Guided Alignment Training for Topic-Aware Neural Machine Translation | cs.CL | In this paper, we propose an effective way for biasing the attention
mechanism of a sequence-to-sequence neural machine translation (NMT) model
towards the well-studied statistical word alignment models. We show that our
novel guided alignment training approach improves translation quality on
real-life e-commerce texts... | computer science |
3,837 | Consensus Attention-based Neural Networks for Chinese Reading
Comprehension | cs.CL | Reading comprehension has embraced a booming in recent NLP research. Several
institutes have released the Cloze-style reading comprehension data, and these
have greatly accelerated the research of machine comprehension. In this work,
we firstly present Chinese reading comprehension datasets, which consist of
People Dai... | computer science |
3,838 | Separating Answers from Queries for Neural Reading Comprehension | cs.CL | We present a novel neural architecture for answering queries, designed to
optimally leverage explicit support in the form of query-answer memories. Our
model is able to refine and update a given query while separately accumulating
evidence for predicting the answer. Its architecture reflects this separation
with dedica... | computer science |
3,839 | Attention-over-Attention Neural Networks for Reading Comprehension | cs.CL | Cloze-style queries are representative problems in reading comprehension.
Over the past few months, we have seen much progress that utilizing neural
network approach to solve Cloze-style questions. In this paper, we present a
novel model called attention-over-attention reader for the Cloze-style reading
comprehension t... | computer science |
3,840 | Neural Discourse Modeling of Conversations | cs.CL | Deep neural networks have shown recent promise in many language-related tasks
such as the modeling of conversations. We extend RNN-based sequence to sequence
models to capture the long range discourse across many turns of conversation.
We perform a sensitivity analysis on how much additional context affects
performance... | computer science |
3,841 | Neural Machine Translation with Recurrent Attention Modeling | cs.NE | Knowing which words have been attended to in previous time steps while
generating a translation is a rich source of information for predicting what
words will be attended to in the future. We improve upon the attention model of
Bahdanau et al. (2014) by explicitly modeling the relationship between previous
and subseque... | computer science |
3,842 | Trainable Frontend For Robust and Far-Field Keyword Spotting | cs.CL | Robust and far-field speech recognition is critical to enable true hands-free
communication. In far-field conditions, signals are attenuated due to distance.
To improve robustness to loudness variation, we introduce a novel frontend
called per-channel energy normalization (PCEN). The key ingredient of PCEN is
the use o... | computer science |
3,843 | Compositional Sequence Labeling Models for Error Detection in Learner
Writing | cs.CL | 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 ... | computer science |
3,844 | Numerically Grounded Language Models for Semantic Error Correction | cs.CL | Semantic error detection and correction is an important task for applications
such as fact checking, speech-to-text or grammatical error correction. Current
approaches generally focus on relatively shallow semantics and do not account
for numeric quantities. Our approach uses language models grounded in numbers
within ... | computer science |
3,845 | Multimodal Attention for Neural Machine Translation | cs.CL | The attention mechanism is an important part of the neural machine
translation (NMT) where it was reported to produce richer source representation
compared to fixed-length encoding sequence-to-sequence models. Recently, the
effectiveness of attention has also been explored in the context of image
captioning. In this wo... | computer science |
3,846 | Long Short-Term Memory based Convolutional Recurrent Neural Networks for
Large Vocabulary Speech Recognition | cs.CL | Long short-term memory (LSTM) recurrent neural networks (RNNs) have been
shown to give state-of-the-art performance on many speech recognition tasks, as
they are able to provide the learned dynamically changing contextual window of
all sequence history. On the other hand, the convolutional neural networks
(CNNs) have b... | computer science |
3,847 | Cached Long Short-Term Memory Neural Networks for Document-Level
Sentiment Classification | cs.CL | Recently, neural networks have achieved great success on sentiment
classification due to their ability to alleviate feature engineering. However,
one of the remaining challenges is to model long texts in document-level
sentiment classification under a recurrent architecture because of the
deficiency of the memory unit.... | computer science |
3,848 | Deep Biaffine Attention for Neural Dependency Parsing | cs.CL | This paper builds off recent work from Kiperwasser & Goldberg (2016) using
neural attention in a simple graph-based dependency parser. We use a larger but
more thoroughly regularized parser than other recent BiLSTM-based approaches,
with biaffine classifiers to predict arcs and labels. Our parser gets state of
the art ... | computer science |
3,849 | Structural Attention Neural Networks for improved sentiment analysis | cs.CL | We introduce a tree-structured attention neural network for sentences and
small phrases and apply it to the problem of sentiment classification. Our
model expands the current recursive models by incorporating structural
information around a node of a syntactic tree using both bottom-up and top-down
information propagat... | computer science |
3,850 | LIDE: Language Identification from Text Documents | cs.CL | The increase in the use of microblogging came along with the rapid growth on
short linguistic data. On the other hand deep learning is considered to be the
new frontier to extract meaningful information out of large amount of raw data
in an automated manner. In this study, we engaged these two emerging fields to
come u... | computer science |
3,851 | A practical approach to dialogue response generation in closed domains | cs.CL | We describe a prototype dialogue response generation model for the customer
service domain at Amazon. The model, which is trained in a weakly supervised
fashion, measures the similarity between customer questions and agent answers
using a dual encoder network, a Siamese-like neural network architecture.
Answer template... | computer science |
3,852 | Attention Strategies for Multi-Source Sequence-to-Sequence Learning | cs.CL | Modeling attention in neural multi-source sequence-to-sequence learning
remains a relatively unexplored area, despite its usefulness in tasks that
incorporate multiple source languages or modalities. We propose two novel
approaches to combine the outputs of attention mechanisms over each source
sequence, flat and hiera... | computer science |
3,853 | Translating Neuralese | cs.CL | Several approaches have recently been proposed for learning decentralized
deep multiagent policies that coordinate via a differentiable communication
channel. While these policies are effective for many tasks, interpretation of
their induced communication strategies has remained a challenge. Here we
propose to interpre... | computer science |
3,854 | Learning Distributed Representations of Texts and Entities from
Knowledge Base | cs.CL | We describe a neural network model that jointly learns distributed
representations of texts and knowledge base (KB) entities. Given a text in the
KB, we train our proposed model to predict entities that are relevant to the
text. Our model is designed to be generic with the ability to address various
NLP tasks with ease... | computer science |
3,855 | Plan, Attend, Generate: Character-level Neural Machine Translation with
Planning in the Decoder | cs.CL | We investigate the integration of a planning mechanism into an
encoder-decoder architecture with an explicit alignment for character-level
machine translation. We develop a model that plans ahead when it computes
alignments between the source and target sequences, constructing a matrix of
proposed future alignments and... | computer science |
3,856 | CUNI System for the WMT17 Multimodal Translation Task | cs.CL | In this paper, we describe our submissions to the WMT17 Multimodal
Translation Task. For Task 1 (multimodal translation), our best scoring system
is a purely textual neural translation of the source image caption to the
target language. The main feature of the system is the use of additional data
that was acquired by s... | computer science |
3,857 | Exploring Neural Transducers for End-to-End Speech Recognition | cs.CL | In this work, we perform an empirical comparison among the CTC,
RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech
recognition. We show that, without any language model, Seq2Seq and
RNN-Transducer models both outperform the best reported CTC models with a
language model, on the popular Hub5'00 ben... | computer science |
3,858 | Analogs of Linguistic Structure in Deep Representations | cs.CL | We investigate the compositional structure of message vectors computed by a
deep network trained on a communication game. By comparing truth-conditional
representations of encoder-produced message vectors to human-produced referring
expressions, we are able to identify aligned (vector, utterance) pairs with the
same me... | computer science |
3,859 | Natural Language Processing with Small Feed-Forward Networks | cs.CL | We show that small and shallow feed-forward neural networks can achieve near
state-of-the-art results on a range of unstructured and structured language
processing tasks while being considerably cheaper in memory and computational
requirements than deep recurrent models. Motivated by resource-constrained
environments l... | computer science |
3,860 | Revisiting Activation Regularization for Language RNNs | cs.CL | Recurrent neural networks (RNNs) serve as a fundamental building block for
many sequence tasks across natural language processing. Recent research has
focused on recurrent dropout techniques or custom RNN cells in order to improve
performance. Both of these can require substantial modifications to the machine
learning ... | computer science |
3,861 | Training RNNs as Fast as CNNs | cs.CL | Common recurrent neural network architectures scale poorly due to the
intrinsic difficulty in parallelizing their state computations. In this work,
we propose the Simple Recurrent Unit (SRU) architecture, a recurrent unit that
simplifies the computation and exposes more parallelism. In SRU, the majority
of computation ... | computer science |
3,862 | Dynamic Evaluation of Neural Sequence Models | cs.NE | We present methodology for using dynamic evaluation to improve neural
sequence models. Models are adapted to recent history via a gradient descent
based mechanism, causing them to assign higher probabilities to re-occurring
sequential patterns. Dynamic evaluation outperforms existing adaptation
approaches in our compar... | computer science |
3,863 | Learning with Latent Language | cs.CL | The named concepts and compositional operators present in natural language
provide a rich source of information about the kinds of abstractions humans use
to navigate the world. Can this linguistic background knowledge improve the
generality and efficiency of learned classifiers and control policies? This
paper aims to... | computer science |
3,864 | Finer Grained Entity Typing with TypeNet | cs.CL | We consider the challenging problem of entity typing over an extremely fine
grained set of types, wherein a single mention or entity can have many
simultaneous and often hierarchically-structured types. Despite the importance
of the problem, there is a relative lack of resources in the form of
fine-grained, deep type h... | computer science |
3,865 | Deep-FSMN for Large Vocabulary Continuous Speech Recognition | cs.NE | In this paper, we present an improved feedforward sequential memory networks
(FSMN) architecture, namely Deep-FSMN (DFSMN), by introducing skip connections
between memory blocks in adjacent layers. These skip connections enable the
information flow across different layers and thus alleviate the gradient
vanishing probl... | computer science |
3,866 | Data Smashing | cs.LG | Investigation of the underlying physics or biology from empirical data
requires a quantifiable notion of similarity - when do two observed data sets
indicate nearly identical generating processes, and when they do not. The
discriminating characteristics to look for in data is often determined by
heuristics designed by ... | computer science |
3,867 | Optimal Sparse Linear Auto-Encoders and Sparse PCA | cs.LG | Principal components analysis (PCA) is the optimal linear auto-encoder of
data, and it is often used to construct features. Enforcing sparsity on the
principal components can promote better generalization, while improving the
interpretability of the features. We study the problem of constructing optimal
sparse linear a... | computer science |
3,868 | On the High-dimensional Power of Linear-time Kernel Two-Sample Testing
under Mean-difference Alternatives | math.ST | Nonparametric two sample testing deals with the question of consistently
deciding if two distributions are different, given samples from both, without
making any parametric assumptions about the form of the distributions. The
current literature is split into two kinds of tests - those which are
consistent without any a... | computer science |
3,869 | Robustness in sparse linear models: relative efficiency based on robust
approximate message passing | math.ST | Understanding efficiency in high dimensional linear models is a longstanding
problem of interest. Classical work with smaller dimensional problems dating
back to Huber and Bickel has illustrated the benefits of efficient loss
functions. When the number of parameters $p$ is of the same order as the sample
size $n$, $p \... | computer science |
3,870 | Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance
based High Dimensional Two Sample Testing | math.ST | Nonparametric two sample testing is a decision theoretic problem that
involves identifying differences between two random variables without making
parametric assumptions about their underlying distributions. We refer to the
most common settings as mean difference alternatives (MDA), for testing
differences only in firs... | computer science |
3,871 | Boosting in the presence of outliers: adaptive classification with
non-convex loss functions | stat.ML | This paper examines the role and efficiency of the non-convex loss functions
for binary classification problems. In particular, we investigate how to design
a simple and effective boosting algorithm that is robust to the outliers in the
data. The analysis of the role of a particular non-convex loss for prediction
accur... | computer science |
3,872 | A Mathematical Theory of Deep Convolutional Neural Networks for Feature
Extraction | cs.IT | Deep convolutional neural networks have led to breakthrough results in
numerous practical machine learning tasks such as classification of images in
the ImageNet data set, control-policy-learning to play Atari games or the board
game Go, and image captioning. Many of these applications first perform feature
extraction ... | computer science |
3,873 | On the Differential Privacy of Bayesian Inference | cs.AI | We study how to communicate findings of Bayesian inference to third parties,
while preserving the strong guarantee of differential privacy. Our main
contributions are four different algorithms for private Bayesian inference on
proba-bilistic graphical models. These include two mechanisms for adding noise
to the Bayesia... | computer science |
3,874 | An Information-Theoretic Framework for Fast and Robust Unsupervised
Learning via Neural Population Infomax | cs.LG | A framework is presented for unsupervised learning of representations based
on infomax principle for large-scale neural populations. We use an asymptotic
approximation to the Shannon's mutual information for a large neural population
to demonstrate that a good initial approximation to the global
information-theoretic o... | computer science |
3,875 | Statistical inference using SGD | cs.LG | We present a novel method for frequentist statistical inference in
$M$-estimation problems, based on stochastic gradient descent (SGD) with a
fixed step size: we demonstrate that the average of such SGD sequences can be
used for statistical inference, after proper scaling. An intuitive analysis
using the Ornstein-Uhlen... | computer science |
3,876 | JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music
with LSTMs | cs.SD | We propose a novel approach for the generation of polyphonic music based on
LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord
progression based on a chord embedding. A second LSTM then generates polyphonic
music from the predicted chord progression. The generated music sounds pleasing
and harm... | computer science |
3,877 | Distributed Regression in Sensor Networks: Training Distributively with
Alternating Projections | cs.LG | Wireless sensor networks (WSNs) have attracted considerable attention in
recent years and motivate a host of new challenges for distributed signal
processing. The problem of distributed or decentralized estimation has often
been considered in the context of parametric models. However, the success of
parametric methods ... | computer science |
3,878 | Toward a Taxonomy and Computational Models of Abnormalities in Images | cs.CV | The human visual system can spot an abnormal image, and reason about what
makes it strange. This task has not received enough attention in computer
vision. In this paper we study various types of atypicalities in images in a
more comprehensive way than has been done before. We propose a new dataset of
abnormal images s... | computer science |
3,879 | Sparse Subspace Clustering: Algorithm, Theory, and Applications | cs.CV | In many real-world problems, we are dealing with collections of
high-dimensional data, such as images, videos, text and web documents, DNA
microarray data, and more. Often, high-dimensional data lie close to
low-dimensional structures corresponding to several classes or categories the
data belongs to. In this paper, we... | computer science |
3,880 | Near-Optimal Joint Object Matching via Convex Relaxation | cs.LG | Joint matching over a collection of objects aims at aggregating information
from a large collection of similar instances (e.g. images, graphs, shapes) to
improve maps between pairs of them. Given multiple matches computed between a
few object pairs in isolation, the goal is to recover an entire collection of
maps that ... | computer science |
3,881 | Scalable Semidefinite Relaxation for Maximum A Posterior Estimation | cs.LG | Maximum a posteriori (MAP) inference over discrete Markov random fields is a
fundamental task spanning a wide spectrum of real-world applications, which is
known to be NP-hard for general graphs. In this paper, we propose a novel
semidefinite relaxation formulation (referred to as SDR) to estimate the MAP
assignment. A... | computer science |
3,882 | An eigenanalysis of data centering in machine learning | stat.ML | Many pattern recognition methods rely on statistical information from
centered data, with the eigenanalysis of an empirical central moment, such as
the covariance matrix in principal component analysis (PCA), as well as partial
least squares regression, canonical-correlation analysis and Fisher
discriminant analysis. R... | computer science |
3,883 | Finding a sparse vector in a subspace: Linear sparsity using alternating
directions | cs.IT | Is it possible to find the sparsest vector (direction) in a generic subspace
$\mathcal{S} \subseteq \mathbb{R}^p$ with $\mathrm{dim}(\mathcal{S})= n < p$?
This problem can be considered a homogeneous variant of the sparse recovery
problem, and finds connections to sparse dictionary learning, sparse PCA, and
many other ... | computer science |
3,884 | Complete Dictionary Recovery over the Sphere | cs.IT | We consider the problem of recovering a complete (i.e., square and
invertible) matrix $\mathbf A_0$, from $\mathbf Y \in \mathbb R^{n \times p}$
with $\mathbf Y = \mathbf A_0 \mathbf X_0$, provided $\mathbf X_0$ is
sufficiently sparse. This recovery problem is central to the theoretical
understanding of dictionary lear... | computer science |
3,885 | Robust Subspace Clustering via Smoothed Rank Approximation | cs.CV | Matrix rank minimizing subject to affine constraints arises in many
application areas, ranging from signal processing to machine learning. Nuclear
norm is a convex relaxation for this problem which can recover the rank exactly
under some restricted and theoretically interesting conditions. However, for
many real-world ... | computer science |
3,886 | Innovation Pursuit: A New Approach to Subspace Clustering | cs.CV | In subspace clustering, a group of data points belonging to a union of
subspaces are assigned membership to their respective subspaces. This paper
presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of
subspace clustering using a new geometrical idea whereby subspaces are
identified based on the... | computer science |
3,887 | The Projected Power Method: An Efficient Algorithm for Joint Alignment
from Pairwise Differences | cs.IT | Various applications involve assigning discrete label values to a collection
of objects based on some pairwise noisy data. Due to the discrete---and hence
nonconvex---structure of the problem, computing the optimal assignment
(e.g.~maximum likelihood assignment) becomes intractable at first sight. This
paper makes prog... | computer science |
3,888 | Topology Reduction in Deep Convolutional Feature Extraction Networks | stat.ML | Deep convolutional neural networks (CNNs) used in practice employ potentially
hundreds of layers and $10$,$000$s of nodes. Such network sizes entail
significant computational complexity due to the large number of convolutions
that need to be carried out; in addition, a large number of parameters needs to
be learned and... | computer science |
3,889 | Energy Clustering | stat.ML | Energy statistics was proposed by Sz\'{e}kely in the 80's inspired by the
Newtonian gravitational potential from classical mechanics, and it provides a
hypothesis test for equality of distributions. It was further generalized from
Euclidean spaces to metric spaces of strong negative type, and more recently, a
connectio... | computer science |
3,890 | Deep Private-Feature Extraction | stat.ML | We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model
which is trained and evaluated based on information theoretic constraints.
Using the selective exchange of information between a user's device and a
service provider, DPFE enables the user to prevent certain sensitive
information from being sha... | computer science |
3,891 | Creativity and Delusions: A Neurocomputational Approach | cs.NE | Thinking is one of the most interesting mental processes. Its complexity is
sometimes simplified and its different manifestations are classified into
normal and abnormal, like the delusional and disorganized thought or the
creative one. The boundaries between these facets of thinking are fuzzy causing
difficulties in m... | computer science |
3,892 | On the predictability of Rainfall in Kerala- An application of ABF
Neural Network | cs.NE | Rainfall in Kerala State, the southern part of Indian Peninsula in particular
is caused by the two monsoons and the two cyclones every year. In general,
climate and rainfall are highly nonlinear phenomena in nature giving rise to
what is known as the `butterfly effect'. We however attempt to train an ABF
neural network... | computer science |
3,893 | Gene Expression Programming: a New Adaptive Algorithm for Solving
Problems | cs.AI | Gene expression programming, a genotype/phenotype genetic algorithm (linear
and ramified), is presented here for the first time as a new technique for the
creation of computer programs. Gene expression programming uses character
linear chromosomes composed of genes structurally organized in a head and a
tail. The chrom... | computer science |
3,894 | Steady State Resource Allocation Analysis of the Stochastic Diffusion
Search | cs.AI | This article presents the long-term behaviour analysis of Stochastic
Diffusion Search (SDS), a distributed agent-based system for best-fit pattern
matching. SDS operates by allocating simple agents into different regions of
the search space. Agents independently pose hypotheses about the presence of
the pattern in the ... | computer science |
3,895 | Extremal Optimization: an Evolutionary Local-Search Algorithm | cs.NE | A recently introduced general-purpose heuristic for finding high-quality
solutions for many hard optimization problems is reviewed. The method is
inspired by recent progress in understanding far-from-equilibrium phenomena in
terms of {\em self-organized criticality,} a concept introduced to describe
emergent complexity... | computer science |
3,896 | On Nonspecific Evidence | cs.AI | When simultaneously reasoning with evidences about several different events
it is necessary to separate the evidence according to event. These events
should then be handled independently. However, when propositions of evidences
are weakly specified in the sense that it may not be certain to which event
they are referri... | computer science |
3,897 | Finding a Posterior Domain Probability Distribution by Specifying
Nonspecific Evidence | cs.AI | This article is an extension of the results of two earlier articles. In [J.
Schubert, On nonspecific evidence, Int. J. Intell. Syst. 8 (1993) 711-725] we
established within Dempster-Shafer theory a criterion function called the
metaconflict function. With this criterion we can partition into subsets a set
of several pi... | computer science |
3,898 | Cluster-based Specification Techniques in Dempster-Shafer Theory | cs.AI | When reasoning with uncertainty there are many situations where evidences are
not only uncertain but their propositions may also be weakly specified in the
sense that it may not be certain to which event a proposition is referring. It
is then crucial not to combine such evidences in the mistaken belief that they
are re... | computer science |
3,899 | Cluster-based Specification Techniques in Dempster-Shafer Theory for an
Evidential Intelligence Analysis of MultipleTarget Tracks (Thesis Abstract) | cs.AI | In Intelligence Analysis it is of vital importance to manage uncertainty.
Intelligence data is almost always uncertain and incomplete, making it
necessary to reason and taking decisions under uncertainty. One way to manage
the uncertainty in Intelligence Analysis is Dempster-Shafer Theory. This thesis
contains five res... | computer science |
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