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9,200 | Conversational Recommendation System with Unsupervised Learning | cs.CL | We will demonstrate a conversational products recommendation agent. This
system shows how we combine research in personalized recommendation systems
with research in dialogue systems to build a virtual sales agent. Based on new
deep learning technologies we developed, the virtual agent is capable of
learning how to int... | computer science |
9,201 | A General Framework for Content-enhanced Network Representation Learning | cs.SI | This paper investigates the problem of network embedding, which aims at
learning low-dimensional vector representation of nodes in networks. Most
existing network embedding methods rely solely on the network structure, i.e.,
the linkage relationships between nodes, but ignore the rich content
information associated wit... | computer science |
9,202 | Supervised Term Weighting Metrics for Sentiment Analysis in Short Text | cs.CL | Term weighting metrics assign weights to terms in order to discriminate the
important terms from the less crucial ones. Due to this characteristic, these
metrics have attracted growing attention in text classification and recently in
sentiment analysis. Using the weights given by such metrics could lead to more
accurat... | computer science |
9,203 | Mapping Between fMRI Responses to Movies and their Natural Language
Annotations | cs.CL | Several research groups have shown how to correlate fMRI responses to the
meanings of presented stimuli. This paper presents new methods for doing so
when only a natural language annotation is available as the description of the
stimulus. We study fMRI data gathered from subjects watching an episode of BBCs
Sherlock [1... | computer science |
9,204 | MusicMood: Predicting the mood of music from song lyrics using machine
learning | cs.LG | Sentiment prediction of contemporary music can have a wide-range of
applications in modern society, for instance, selecting music for public
institutions such as hospitals or restaurants to potentially improve the
emotional well-being of personnel, patients, and customers, respectively. In
this project, music recommend... | computer science |
9,205 | Using Artificial Intelligence to Identify State Secrets | cs.CY | Whether officials can be trusted to protect national security information has
become a matter of great public controversy, reigniting a long-standing debate
about the scope and nature of official secrecy. The declassification of
millions of electronic records has made it possible to analyze these issues
with greater ri... | computer science |
9,206 | The Deep Journey from Content to Collaborative Filtering | cs.IR | In Recommender Systems research, algorithms are often characterized as either
Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained
using a dataset of user explicit or implicit preferences while CB algorithms
are typically based on item profiles. These approaches harness very different
data sour... | computer science |
9,207 | Generalized Topic Modeling | cs.LG | Recently there has been significant activity in developing algorithms with
provable guarantees for topic modeling. In standard topic models, a topic (such
as sports, business, or politics) is viewed as a probability distribution $\vec
a_i$ over words, and a document is generated by first selecting a mixture $\vec
w$ ov... | computer science |
9,208 | Robust end-to-end deep audiovisual speech recognition | cs.CL | Speech is one of the most effective ways of communication among humans. Even
though audio is the most common way of transmitting speech, very important
information can be found in other modalities, such as vision. Vision is
particularly useful when the acoustic signal is corrupted. Multi-modal speech
recognition howeve... | computer science |
9,209 | Less is More: Learning Prominent and Diverse Topics for Data
Summarization | cs.LG | Statistical topic models efficiently facilitate the exploration of
large-scale data sets. Many models have been developed and broadly used to
summarize the semantic structure in news, science, social media, and digital
humanities. However, a common and practical objective in data exploration tasks
is not to enumerate a... | computer science |
9,210 | Prior matters: simple and general methods for evaluating and improving
topic quality in topic modeling | cs.CL | Latent Dirichlet Allocation (LDA) models trained without stopword removal
often produce topics with high posterior probabilities on uninformative words,
obscuring the underlying corpus content. Even when canonical stopwords are
manually removed, uninformative words common in that corpus will still dominate
the most pro... | computer science |
9,211 | Leveraging Large Amounts of Weakly Supervised Data for Multi-Language
Sentiment Classification | cs.CL | This paper presents a novel approach for multi-lingual sentiment
classification in short texts. This is a challenging task as the amount of
training data in languages other than English is very limited. Previously
proposed multi-lingual approaches typically require to establish a
correspondence to English for which pow... | computer science |
9,212 | Empirical Evaluation of Parallel Training Algorithms on Acoustic
Modeling | cs.CL | Deep learning models (DLMs) are state-of-the-art techniques in speech
recognition. However, training good DLMs can be time consuming especially for
production-size models and corpora. Although several parallel training
algorithms have been proposed to improve training efficiency, there is no clear
guidance on which one... | computer science |
9,213 | Gate Activation Signal Analysis for Gated Recurrent Neural Networks and
Its Correlation with Phoneme Boundaries | cs.SD | In this paper we analyze the gate activation signals inside the gated
recurrent neural networks, and find the temporal structure of such signals is
highly correlated with the phoneme boundaries. This correlation is further
verified by a set of experiments for phoneme segmentation, in which better
results compared to st... | computer science |
9,214 | Tacotron: Towards End-to-End Speech Synthesis | cs.CL | A text-to-speech synthesis system typically consists of multiple stages, such
as a text analysis frontend, an acoustic model and an audio synthesis module.
Building these components often requires extensive domain expertise and may
contain brittle design choices. In this paper, we present Tacotron, an
end-to-end genera... | computer science |
9,215 | TransNets: Learning to Transform for Recommendation | cs.IR | Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is
available. For example, a recent model, DeepCoNN, uses neural nets to learn one
latent representation for the text of all reviews written by a target user, and
a sec... | computer science |
9,216 | Voice Conversion Using Sequence-to-Sequence Learning of Context
Posterior Probabilities | cs.SD | Voice conversion (VC) using sequence-to-sequence learning of context
posterior probabilities is proposed. Conventional VC using shared context
posterior probabilities predicts target speech parameters from the context
posterior probabilities estimated from the source speech parameters. Although
conventional VC can be b... | computer science |
9,217 | Leveraging Term Banks for Answering Complex Questions: A Case for Sparse
Vectors | cs.IR | While open-domain question answering (QA) systems have proven effective for
answering simple questions, they struggle with more complex questions. Our goal
is to answer more complex questions reliably, without incurring a significant
cost in knowledge resource construction to support the QA. One readily
available knowl... | computer science |
9,218 | A Neural Parametric Singing Synthesizer | cs.SD | We present a new model for singing synthesis based on a modified version of
the WaveNet architecture. Instead of modeling raw waveform, we model features
produced by a parametric vocoder that separates the influence of pitch and
timbre. This allows conveniently modifying pitch to match any target melody,
facilitates tr... | computer science |
9,219 | Sparse Communication for Distributed Gradient Descent | cs.CL | We make distributed stochastic gradient descent faster by exchanging sparse
updates instead of dense updates. Gradient updates are positively skewed as
most updates are near zero, so we map the 99% smallest updates (by absolute
value) to zero then exchange sparse matrices. This method can be combined with
quantization ... | computer science |
9,220 | Joint Modeling of Text and Acoustic-Prosodic Cues for Neural Parsing | cs.CL | In conversational speech, the acoustic signal provides cues that help
listeners disambiguate difficult parses. For automatically parsing a spoken
utterance, we introduce a model that integrates transcribed text and
acoustic-prosodic features using a convolutional neural network over energy and
pitch trajectories couple... | computer science |
9,221 | Neural Ranking Models with Weak Supervision | cs.IR | Despite the impressive improvements achieved by unsupervised deep neural
networks in computer vision and NLP tasks, such improvements have not yet been
observed in ranking for information retrieval. The reason may be the complexity
of the ranking problem, as it is not obvious how to learn from queries and
documents whe... | computer science |
9,222 | On Using Active Learning and Self-Training when Mining Performance
Discussions on Stack Overflow | cs.CL | Abundant data is the key to successful machine learning. However, supervised
learning requires annotated data that are often hard to obtain. In a
classification task with limited resources, Active Learning (AL) promises to
guide annotators to examples that bring the most value for a classifier. AL can
be successfully c... | computer science |
9,223 | Deep Speaker Feature Learning for Text-independent Speaker Verification | cs.SD | Recently deep neural networks (DNNs) have been used to learn speaker
features. However, the quality of the learned features is not sufficiently
good, so a complex back-end model, either neural or probabilistic, has to be
used to address the residual uncertainty when applied to speaker verification,
just as with raw fea... | computer science |
9,224 | Social Media-based Substance Use Prediction | cs.CL | In this paper, we demonstrate how the state-of-the-art machine learning and
text mining techniques can be used to build effective social media-based
substance use detection systems. Since a substance use ground truth is
difficult to obtain on a large scale, to maximize system performance, we
explore different feature l... | computer science |
9,225 | Deep Learning for Environmentally Robust Speech Recognition: An Overview
of Recent Developments | cs.SD | Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition but still remains
an important challenge. Data-driven supervised approaches, especially the ones
based on deep neural networks, have recently emerged as potential alternatives
to trad... | computer science |
9,226 | Task-specific Word Identification from Short Texts Using a Convolutional
Neural Network | cs.CL | Task-specific word identification aims to choose the task-related words that
best describe a short text. Existing approaches require well-defined seed words
or lexical dictionaries (e.g., WordNet), which are often unavailable for many
applications such as social discrimination detection and fake review detection.
Howev... | computer science |
9,227 | Joint Text Embedding for Personalized Content-based Recommendation | cs.IR | Learning a good representation of text is key to many recommendation
applications. Examples include news recommendation where texts to be
recommended are constantly published everyday. However, most existing
recommendation techniques, such as matrix factorization based methods, mainly
rely on interaction histories to l... | computer science |
9,228 | Deep learning for extracting protein-protein interactions from
biomedical literature | cs.CL | State-of-the-art methods for protein-protein interaction (PPI) extraction are
primarily feature-based or kernel-based by leveraging lexical and syntactic
information. But how to incorporate such knowledge in the recent deep learning
methods remains an open question. In this paper, we propose a multichannel
dependency-b... | computer science |
9,229 | Characterizing Types of Convolution in Deep Convolutional Recurrent
Neural Networks for Robust Speech Emotion Recognition | cs.LG | Deep convolutional neural networks are being actively investigated in a wide
range of speech and audio processing applications including speech recognition,
audio event detection and computational paralinguistics, owing to their ability
to reduce factors of variations, for learning from speech. However, studies
have su... | computer science |
9,230 | Encoding of phonology in a recurrent neural model of grounded speech | cs.CL | We study the representation and encoding of phonemes in a recurrent neural
network model of grounded speech. We use a model which processes images and
their spoken descriptions, and projects the visual and auditory representations
into the same semantic space. We perform a number of analyses on how
information about in... | computer science |
9,231 | Jointly Learning Word Embeddings and Latent Topics | cs.CL | Word embedding models such as Skip-gram learn a vector-space representation
for each word, based on the local word collocation patterns that are observed
in a text corpus. Latent topic models, on the other hand, take a more global
view, looking at the word distributions across the corpus to assign a topic to
each word ... | computer science |
9,232 | Semi-supervised Text Categorization Using Recursive K-means Clustering | cs.LG | In this paper, we present a semi-supervised learning algorithm for
classification of text documents. A method of labeling unlabeled text documents
is presented. The presented method is based on the principle of divide and
conquer strategy. It uses recursive K-means algorithm for partitioning both
labeled and unlabeled ... | computer science |
9,233 | Automated Audio Captioning with Recurrent Neural Networks | cs.SD | We present the first approach to automated audio captioning. We employ an
encoder-decoder scheme with an alignment model in between. The input to the
encoder is a sequence of log mel-band energies calculated from an audio file,
while the output is a sequence of words, i.e. a caption. The encoder is a
multi-layered, bi-... | computer science |
9,234 | Like trainer, like bot? Inheritance of bias in algorithmic content
moderation | cs.CY | The internet has become a central medium through which `networked publics'
express their opinions and engage in debate. Offensive comments and personal
attacks can inhibit participation in these spaces. Automated content moderation
aims to overcome this problem using machine learning classifiers trained on
large corpor... | computer science |
9,235 | Multitask Learning for Fine-Grained Twitter Sentiment Analysis | cs.IR | Traditional sentiment analysis approaches tackle problems like ternary
(3-category) and fine-grained (5-category) classification by learning the tasks
separately. We argue that such classification tasks are correlated and we
propose a multitask approach based on a recurrent neural network that benefits
by jointly learn... | computer science |
9,236 | Quasar: Datasets for Question Answering by Search and Reading | cs.CL | We present two new large-scale datasets aimed at evaluating systems designed
to comprehend a natural language query and extract its answer from a large
corpus of text. The Quasar-S dataset consists of 37000 cloze-style
(fill-in-the-gap) queries constructed from definitions of software entity tags
on the popular website... | computer science |
9,237 | Listening while Speaking: Speech Chain by Deep Learning | cs.CL | Despite the close relationship between speech perception and production,
research in automatic speech recognition (ASR) and text-to-speech synthesis
(TTS) has progressed more or less independently without exerting much mutual
influence on each other. In human communication, on the other hand, a
closed-loop speech chain... | computer science |
9,238 | Single-Channel Multi-talker Speech Recognition with Permutation
Invariant Training | cs.SD | Although great progresses have been made in automatic speech recognition
(ASR), significant performance degradation is still observed when recognizing
multi-talker mixed speech. In this paper, we propose and evaluate several
architectures to address this problem under the assumption that only a single
channel of mixed ... | computer science |
9,239 | VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop | cs.LG | We present a new neural text to speech (TTS) method that is able to transform
text to speech in voices that are sampled in the wild. Unlike other systems,
our solution is able to deal with unconstrained voice samples and without
requiring aligned phonemes or linguistic features. The network architecture is
simpler than... | computer science |
9,240 | Stock Prediction: a method based on extraction of news features and
recurrent neural networks | cs.CL | This paper proposed a method for stock prediction. In terms of feature
extraction, we extract the features of stock-related news besides stock prices.
We first select some seed words based on experience which are the symbols of
good news and bad news. Then we propose an optimization method and calculate
the positive po... | computer science |
9,241 | End-to-End Neural Segmental Models for Speech Recognition | cs.CL | Segmental models are an alternative to frame-based models for sequence
prediction, where hypothesized path weights are based on entire segment scores
rather than a single frame at a time. Neural segmental models are segmental
models that use neural network-based weight functions. Neural segmental models
have achieved c... | computer science |
9,242 | Leveraging Sparse and Dense Feature Combinations for Sentiment
Classification | cs.CL | Neural networks are one of the most popular approaches for many natural
language processing tasks such as sentiment analysis. They often outperform
traditional machine learning models and achieve the state-of-art results on
most tasks. However, many existing deep learning models are complex, difficult
to train and prov... | computer science |
9,243 | Extractive Summarization using Deep Learning | cs.CL | This paper proposes a text summarization approach for factual reports using a
deep learning model. This approach consists of three phases: feature
extraction, feature enhancement, and summary generation, which work together to
assimilate core information and generate a coherent, understandable summary. We
are exploring... | computer science |
9,244 | Unsupervised Terminological Ontology Learning based on Hierarchical
Topic Modeling | cs.CL | In this paper, we present hierarchical relationbased latent Dirichlet
allocation (hrLDA), a data-driven hierarchical topic model for extracting
terminological ontologies from a large number of heterogeneous documents. In
contrast to traditional topic models, hrLDA relies on noun phrases instead of
unigrams, considers s... | computer science |
9,245 | MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre
Broadcast Challenge | cs.CL | In order to successfully annotate the Arabic speech con- tent found in
open-domain media broadcasts, it is essential to be able to process a diverse
set of Arabic dialects. For the 2017 Multi-Genre Broadcast challenge (MGB-3)
there were two possible tasks: Arabic speech recognition, and Arabic Dialect
Identification (A... | computer science |
9,246 | Understanding the Logical and Semantic Structure of Large Documents | cs.CL | Current language understanding approaches focus on small documents, such as
newswire articles, blog posts, product reviews and discussion forum entries.
Understanding and extracting information from large documents like legal
briefs, proposals, technical manuals and research articles is still a
challenging task. We des... | computer science |
9,247 | Information Theoretic Analysis of DNN-HMM Acoustic Modeling | cs.SD | We propose an information theoretic framework for quantitative assessment of
acoustic modeling for hidden Markov model (HMM) based automatic speech
recognition (ASR). Acoustic modeling yields the probabilities of HMM sub-word
states for a short temporal window of speech acoustic features. We cast ASR as
a communication... | computer science |
9,248 | Sequence Prediction with Neural Segmental Models | cs.CL | Segments that span contiguous parts of inputs, such as phonemes in speech,
named entities in sentences, actions in videos, occur frequently in sequence
prediction problems. Segmental models, a class of models that explicitly
hypothesizes segments, have allowed the exploration of rich segment features
for sequence predi... | computer science |
9,249 | Attention-based Wav2Text with Feature Transfer Learning | cs.CL | Conventional automatic speech recognition (ASR) typically performs
multi-level pattern recognition tasks that map the acoustic speech waveform
into a hierarchy of speech units. But, it is widely known that information loss
in the earlier stage can propagate through the later stages. After the
resurgence of deep learnin... | computer science |
9,250 | Topic Modeling based on Keywords and Context | cs.CL | Current topic models often suffer from discovering topics not matching human
intuition, unnatural switching of topics within documents and high
computational demands. We address these concerns by proposing a topic model and
an inference algorithm based on automatically identifying characteristic
keywords for topics. Ke... | computer science |
9,251 | Text2Action: Generative Adversarial Synthesis from Language to Action | cs.LG | In this paper, we propose a generative model which learns the relationship
between language and human action in order to generate a human action sequence
given a sentence describing human behavior. The proposed generative model is a
generative adversarial network (GAN), which is based on the sequence to
sequence (SEQ2S... | computer science |
9,252 | Fishing for Clickbaits in Social Images and Texts with
Linguistically-Infused Neural Network Models | cs.LG | This paper presents the results and conclusions of our participation in the
Clickbait Challenge 2017 on automatic clickbait detection in social media. We
first describe linguistically-infused neural network models and identify
informative representations to predict the level of clickbaiting present in
Twitter posts. Ou... | computer science |
9,253 | Deep Triphone Embedding Improves Phoneme Recognition | cs.SD | In this paper, we present a novel Deep Triphone Embedding (DTE)
representation derived from Deep Neural Network (DNN) to encapsulate the
discriminative information present in the adjoining speech frames. DTEs are
generated using a four hidden layer DNN with 3000 nodes in each hidden layer at
the first-stage. This DNN i... | computer science |
9,254 | Sequence-to-Sequence ASR Optimization via Reinforcement Learning | cs.CL | Despite the success of sequence-to-sequence approaches in automatic speech
recognition (ASR) systems, the models still suffer from several problems,
mainly due to the mismatch between the training and inference conditions. In
the sequence-to-sequence architecture, the model is trained to predict the
grapheme of the cur... | computer science |
9,255 | Quality-Efficiency Trade-offs in Machine Learning for Text Processing | cs.IR | Data mining, machine learning, and natural language processing are powerful
techniques that can be used together to extract information from large texts.
Depending on the task or problem at hand, there are many different approaches
that can be used. The methods available are continuously being optimized, but
not all th... | computer science |
9,256 | Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted
Boltzmann Machine | cs.CL | Recently by the development of the Internet and the Web, different types of
social media such as web blogs become an immense source of text data. Through
the processing of these data, it is possible to discover practical information
about different topics, individuals opinions and a thorough understanding of
the societ... | computer science |
9,257 | Efficient Representation for Natural Language Processing via Kernelized
Hashcodes | cs.CL | Kernel methods have been used widely in a number of tasks, but have had
limited success in Natural Language Processing (NLP) due to high cost of
computing kernel similarities between discrete natural language structures. A
recently proposed technique, Kernelized Locality Sensitive Hashing (KLSH), can
significantly redu... | computer science |
9,258 | Prior-aware Dual Decomposition: Document-specific Topic Inference for
Spectral Topic Models | cs.CL | Spectral topic modeling algorithms operate on matrices/tensors of word
co-occurrence statistics to learn topic-specific word distributions. This
approach removes the dependence on the original documents and produces
substantial gains in efficiency and provable topic inference, but at a cost:
the model can no longer pro... | computer science |
9,259 | Multiple-Instance, Cascaded Classification for Keyword Spotting in
Narrow-Band Audio | cs.LG | We propose using cascaded classifiers for a keyword spotting (KWS) task on
narrow-band (NB), 8kHz audio acquired in non-IID environments --- a more
challenging task than most state-of-the-art KWS systems face. We present a
model that incorporates Deep Neural Networks (DNNs), cascading,
multiple-feature representations,... | computer science |
9,260 | Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis | cs.CL | We consider the task of fine-grained sentiment analysis from the perspective
of multiple instance learning (MIL). Our neural model is trained on document
sentiment labels, and learns to predict the sentiment of text segments, i.e.
sentences or elementary discourse units (EDUs), without segment-level
supervision. We int... | computer science |
9,261 | Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks | cs.LG | Recent deep learning (DL) models have moved beyond static network
architectures to dynamic ones, handling data where the network structure
changes every example, such as sequences of variable lengths, trees, and
graphs. Existing dataflow-based programming models for DL---both static and
dynamic declaration---either can... | computer science |
9,262 | Document Generation with Hierarchical Latent Tree Models | cs.CL | In most probabilistic topic models, a document is viewed as a collection of
tokens and each token is a variable whose values are all the words in a
vocabulary. One exception is hierarchical latent tree models (HLTMs), where a
document is viewed as a binary vector over the vocabulary and each word is
regarded as a binar... | computer science |
9,263 | Generating and Estimating Nonverbal Alphabets for Situated and
Multimodal Communications | cs.HC | In this paper, we discuss the formalized approach for generating and
estimating symbols (and alphabets), which can be communicated by the wide range
of non-verbal means based on specific user requirements (medium, priorities,
type of information that needs to be conveyed). The short characterization of
basic terms and ... | computer science |
9,264 | Differentially Private Distributed Learning for Language Modeling Tasks | cs.CL | One of the big challenges in machine learning applications is that training
data can be different from the real-world data faced by the algorithm. In
language modeling, users' language (e.g. in private messaging) could change in
a year and be completely different from what we observe in publicly available
data. At the ... | computer science |
9,265 | Leveraging Native Language Speech for Accent Identification using Deep
Siamese Networks | cs.CL | The problem of automatic accent identification is important for several
applications like speaker profiling and recognition as well as for improving
speech recognition systems. The accented nature of speech can be primarily
attributed to the influence of the speaker's native language on the given
speech recording. In t... | computer science |
9,266 | Lifelong Learning for Sentiment Classification | cs.CL | This paper proposes a novel lifelong learning (LL) approach to sentiment
classification. LL mimics the human continuous learning process, i.e.,
retaining the knowledge learned from past tasks and use it to help future
learning. In this paper, we first discuss LL in general and then LL for
sentiment classification in pa... | computer science |
9,267 | Black-box Generation of Adversarial Text Sequences to Evade Deep
Learning Classifiers | cs.CL | Although various techniques have been proposed to generate adversarial
samples for white-box attacks on text, little attention has been paid to a
black-box attack, which is a more realistic scenario. In this paper, we present
a novel algorithm, DeepWordBug, to effectively generate small text
perturbations in a black-bo... | computer science |
9,268 | DCDistance: A Supervised Text Document Feature extraction based on class
labels | cs.IR | Text Mining is a field that aims at extracting information from textual data.
One of the challenges of such field of study comes from the pre-processing
stage in which a vector (and structured) representation should be extracted
from unstructured data. The common extraction creates large and sparse vectors
representing... | computer science |
9,269 | Adversarial Texts with Gradient Methods | cs.CL | Adversarial samples for images have been extensively studied in the
literature. Among many of the attacking methods, gradient-based methods are
both effective and easy to compute. In this work, we propose a framework to
adapt the gradient attacking methods on images to text domain. The main
difficulties for generating ... | computer science |
9,270 | Improving Review Representations with User Attention and Product
Attention for Sentiment Classification | cs.CL | Neural network methods have achieved great success in reviews sentiment
classification. Recently, some works achieved improvement by incorporating user
and product information to generate a review representation. However, in
reviews, we observe that some words or sentences show strong user's preference,
and some others... | computer science |
9,271 | Texygen: A Benchmarking Platform for Text Generation Models | cs.CL | We introduce Texygen, a benchmarking platform to support research on
open-domain text generation models. Texygen has not only implemented a majority
of text generation models, but also covered a set of metrics that evaluate the
diversity, the quality and the consistency of the generated texts. The Texygen
platform coul... | computer science |
9,272 | Speech Emotion Recognition with Data Augmentation and Layer-wise
Learning Rate Adjustment | cs.SD | In this work, we design a neural network for recognizing emotions in speech,
using the standard IEMOCAP dataset. Following the latest advances in audio
analysis, we use an architecture involving both convolutional layers, for
extracting high-level features from raw spectrograms, and recurrent ones for
aggregating long-... | computer science |
9,273 | Neural Voice Cloning with a Few Samples | cs.CL | Voice cloning is a highly desired feature for personalized speech interfaces.
Neural network based speech synthesis has been shown to generate high quality
speech for a large number of speakers. In this paper, we introduce a neural
voice cloning system that takes a few audio samples as input. We study two
approaches: s... | computer science |
9,274 | Deep factorization for speech signal | eess.AS | Various informative factors mixed in speech signals, leading to great
difficulty when decoding any of the factors. An intuitive idea is to factorize
each speech frame into individual informative factors, though it turns out to
be highly difficult. Recently, we found that speaker traits, which were assumed
to be long-te... | computer science |
9,275 | Cross-domain Recommendation via Deep Domain Adaptation | cs.LG | The behavior of users in certain services could be a clue that can be used to
infer their preferences and may be used to make recommendations for other
services they have never used. However, the cross-domain relationships between
items and user consumption patterns are not simple, especially when there are
few or no c... | computer science |
9,276 | Hierarchical Learning of Cross-Language Mappings through Distributed
Vector Representations for Code | cs.LG | Translating a program written in one programming language to another can be
useful for software development tasks that need functionality implementations
in different languages. Although past studies have considered this problem,
they may be either specific to the language grammars, or specific to certain
kinds of code... | computer science |
9,277 | Corpus Statistics in Text Classification of Online Data | cs.CL | Transformation of Machine Learning (ML) from a boutique science to a
generally accepted technology has increased importance of reproduction and
transportability of ML studies. In the current work, we investigate how corpus
characteristics of textual data sets correspond to text classification results.
We work with two ... | computer science |
9,278 | On Design and Implementation of the Distributed Modular Audio
Recognition Framework: Requirements and Specification Design Document | cs.CV | We present the requirements and design specification of the open-source
Distributed Modular Audio Recognition Framework (DMARF), a distributed
extension of MARF. The distributed version aggregates a number of distributed
technologies (e.g. Java RMI, CORBA, Web Services) in a pluggable and modular
model along with the p... | computer science |
9,279 | Design Automation for Binarized Neural Networks: A Quantum Leap
Opportunity? | cs.OH | Design automation in general, and in particular logic synthesis, can play a
key role in enabling the design of application-specific Binarized Neural
Networks (BNN). This paper presents the hardware design and synthesis of a
purely combinational BNN for ultra-low power near-sensor processing. We
leverage the major oppor... | computer science |
9,280 | Cross-Entropy method: convergence issues for extended implementation | math.OC | The cross-entropy method (CE) developed by R. Rubinstein is an elegant
practical principle for simulating rare events. The method approximates the
probability of the rare event by means of a family of probabilistic models. The
method has been extended to optimization, by considering an optimal event as a
rare event. CE... | computer science |
9,281 | Optoelectronic Reservoir Computing | cs.ET | Reservoir computing is a recently introduced, highly efficient bio-inspired
approach for processing time dependent data. The basic scheme of reservoir
computing consists of a non linear recurrent dynamical system coupled to a
single input layer and a single output layer. Within these constraints many
implementations ar... | computer science |
9,282 | A Cascade Neural Network Architecture investigating Surface Plasmon
Polaritons propagation for thin metals in OpenMP | cs.NE | Surface plasmon polaritons (SPPs) confined along metal-dielectric interface
have attracted a relevant interest in the area of ultracompact photonic
circuits, photovoltaic devices and other applications due to their strong field
confinement and enhancement. This paper investigates a novel cascade neural
network (NN) arc... | computer science |
9,283 | Riemannian metrics for neural networks I: feedforward networks | cs.NE | We describe four algorithms for neural network training, each adapted to
different scalability constraints. These algorithms are mathematically
principled and invariant under a number of transformations in data and network
representation, from which performance is thus independent. These algorithms
are obtained from th... | computer science |
9,284 | Statistical mechanics of unsupervised feature learning in a restricted
Boltzmann machine with binary synapses | cs.LG | Revealing hidden features in unlabeled data is called unsupervised feature
learning, which plays an important role in pretraining a deep neural network.
Here we provide a statistical mechanics analysis of the unsupervised learning
in a restricted Boltzmann machine with binary synapses. A message passing
equation to inf... | computer science |
9,285 | Hierarchical Models as Marginals of Hierarchical Models | math.PR | We investigate the representation of hierarchical models in terms of
marginals of other hierarchical models with smaller interactions. We focus on
binary variables and marginals of pairwise interaction models whose hidden
variables are conditionally independent given the visible variables. In this
case the problem is e... | computer science |
9,286 | Role of zero synapses in unsupervised feature learning | cs.LG | Synapses in real neural circuits can take discrete values, including zero
(silent or potential) synapses. The computational role of zero synapses in
unsupervised feature learning of unlabeled noisy data is still unclear, thus it
is important to understand how the sparseness of synaptic activity is shaped
during learnin... | computer science |
9,287 | Forecasting day-ahead electricity prices in Europe: the importance of
considering market integration | cs.CE | Motivated by the increasing integration among electricity markets, in this
paper we propose two different methods to incorporate market integration in
electricity price forecasting and to improve the predictive performance. First,
we propose a deep neural network that considers features from connected markets
to improv... | computer science |
9,288 | Sample-level CNN Architectures for Music Auto-tagging Using Raw
Waveforms | cs.SD | Recent work has shown that the end-to-end approach using convolutional neural
network (CNN) is effective in various types of machine learning tasks. For
audio signals, the approach takes raw waveforms as input using an 1-D
convolution layer. In this paper, we improve the 1-D CNN architecture for music
auto-tagging by a... | computer science |
9,289 | TasNet: time-domain audio separation network for real-time,
single-channel speech separation | cs.SD | Robust speech processing in multi-talker environments requires effective
speech separation. Recent deep learning systems have made significant progress
toward solving this problem, yet it remains challenging particularly in
real-time, short latency applications. Most methods attempt to construct a mask
for each source ... | computer science |
9,290 | Deep Learning for Real-time Gravitational Wave Detection and Parameter
Estimation: Results with Advanced LIGO Data | cs.LG | The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent field of
science,... | computer science |
9,291 | Deep Learning for Real-time Gravitational Wave Detection and Parameter
Estimation with LIGO Data | cs.LG | The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent science, we
propo... | computer science |
9,292 | Denoising Gravitational Waves using Deep Learning with Recurrent
Denoising Autoencoders | cs.LG | Gravitational wave astronomy is a rapidly growing field of modern
astrophysics, with observations being made frequently by the LIGO detectors.
Gravitational wave signals are often extremely weak and the data from the
detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary
noise, often containing t... | computer science |
9,293 | On the organization of grid and place cells: Neural de-noising via
subspace learning | cs.IT | Place cells in the hippocampus are active when an animal visits a certain
locations (referred to as place fields) within an environment and remain silent
otherwise. Grid cells in the medial entorhinal cortex (MEC) respond at multiple
locations, with firing fields that exhibit a hexagonally symmetric periodic
pattern. T... | computer science |
9,294 | Mean Field Residual Networks: On the Edge of Chaos | cs.NE | We study randomly initialized residual networks using mean field theory and
the theory of difference equations. Classical feedforward neural networks, such
as those with tanh activations, exhibit exponential behavior on the average
when propagating inputs forward or gradients backward. The exponential forward
dynamics ... | computer science |
9,295 | Concept Stability for Constructing Taxonomies of Web-site Users | cs.CY | Owners of a web-site are often interested in analysis of groups of users of
their site. Information on these groups can help optimizing the structure and
contents of the site. In this paper we use an approach based on formal concepts
for constructing taxonomies of user groups. For decreasing the huge amount of
concepts... | computer science |
9,296 | Rasch-based high-dimensionality data reduction and class prediction with
applications to microarray gene expression data | cs.AI | Class prediction is an important application of microarray gene expression
data analysis. The high-dimensionality of microarray data, where number of
genes (variables) is very large compared to the number of samples (obser-
vations), makes the application of many prediction techniques (e.g., logistic
regression, discri... | computer science |
9,297 | Nonparametric Bayesian sparse factor models with application to gene
expression modeling | stat.AP | A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where
observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$,
of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian
Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity
and to ... | computer science |
9,298 | Instant Replay: Investigating statistical Analysis in Sports | stat.AP | Technology has had an unquestionable impact on the way people watch sports.
Along with this technological evolution has come a higher standard to ensure a
good viewing experience for the casual sports fan. It can be argued that the
pervasion of statistical analysis in sports serves to satiate the fan's desire
for detai... | computer science |
9,299 | Structured Sparsity via Alternating Direction Methods | math.OC | We consider a class of sparse learning problems in high dimensional feature
space regularized by a structured sparsity-inducing norm which incorporates
prior knowledge of the group structure of the features. Such problems often
pose a considerable challenge to optimization algorithms due to the
non-smoothness and non-s... | computer science |
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