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600 | Backward and Forward Language Modeling for Constrained Sentence
Generation | cs.CL | Recent language models, especially those based on recurrent neural networks
(RNNs), make it possible to generate natural language from a learned
probability. Language generation has wide applications including machine
translation, summarization, question answering, conversation systems, etc.
Existing methods typically ... | computer science |
601 | Online Keyword Spotting with a Character-Level Recurrent Neural Network | cs.CL | In this paper, we propose a context-aware keyword spotting model employing a
character-level recurrent neural network (RNN) for spoken term detection in
continuous speech. The RNN is end-to-end trained with connectionist temporal
classification (CTC) to generate the probabilities of character and
word-boundary labels. ... | computer science |
602 | Domain Specific Author Attribution Based on Feedforward Neural Network
Language Models | cs.CL | Authorship attribution refers to the task of automatically determining the
author based on a given sample of text. It is a problem with a long history and
has a wide range of application. Building author profiles using language models
is one of the most successful methods to automate this task. New language
modeling me... | computer science |
603 | Segmental Recurrent Neural Networks for End-to-end Speech Recognition | cs.CL | We study the segmental recurrent neural network for end-to-end acoustic
modelling. This model connects the segmental conditional random field (CRF)
with a recurrent neural network (RNN) used for feature extraction. Compared to
most previous CRF-based acoustic models, it does not rely on an external system
to provide fe... | computer science |
604 | How Transferable are Neural Networks in NLP Applications? | cs.CL | Transfer learning is aimed to make use of valuable knowledge in a source
domain to help model performance in a target domain. It is particularly
important to neural networks, which are very likely to be overfitting. In some
fields like image processing, many studies have shown the effectiveness of
neural network-based ... | computer science |
605 | Recurrent Neural Network Encoder with Attention for Community Question
Answering | cs.CL | We apply a general recurrent neural network (RNN) encoder framework to
community question answering (cQA) tasks. Our approach does not rely on any
linguistic processing, and can be applied to different languages or domains.
Further improvements are observed when we extend the RNN encoders with a neural
attention mechan... | computer science |
606 | Recursive Neural Language Architecture for Tag Prediction | cs.IR | We consider the problem of learning distributed representations for tags from
their associated content for the task of tag recommendation. Considering
tagging information is usually very sparse, effective learning from content and
tag association is very crucial and challenging task. Recently, various neural
representa... | computer science |
607 | On the Compression of Recurrent Neural Networks with an Application to
LVCSR acoustic modeling for Embedded Speech Recognition | cs.CL | We study the problem of compressing recurrent neural networks (RNNs). In
particular, we focus on the compression of RNN acoustic models, which are
motivated by the goal of building compact and accurate speech recognition
systems which can be run efficiently on mobile devices. In this work, we
present a technique for ge... | computer science |
608 | Pointing the Unknown Words | cs.CL | The problem of rare and unknown words is an important issue that can
potentially influence the performance of many NLP systems, including both the
traditional count-based and the deep learning models. We propose a novel way to
deal with the rare and unseen words for the neural network models using
attention. Our model ... | computer science |
609 | Learning Multiscale Features Directly From Waveforms | cs.CL | Deep learning has dramatically improved the performance of speech recognition
systems through learning hierarchies of features optimized for the task at
hand. However, true end-to-end learning, where features are learned directly
from waveforms, has only recently reached the performance of hand-tailored
representations... | computer science |
610 | Joint Learning of Sentence Embeddings for Relevance and Entailment | cs.CL | We consider the problem of Recognizing Textual Entailment within an
Information Retrieval context, where we must simultaneously determine the
relevancy as well as degree of entailment for individual pieces of evidence to
determine a yes/no answer to a binary natural language question.
We compare several variants of n... | computer science |
611 | Deep API Learning | cs.SE | Developers often wonder how to implement a certain functionality (e.g., how
to parse XML files) using APIs. Obtaining an API usage sequence based on an
API-related natural language query is very helpful in this regard. Given a
query, existing approaches utilize information retrieval models to search for
matching API se... | computer science |
612 | Does Multimodality Help Human and Machine for Translation and Image
Captioning? | cs.CL | This paper presents the systems developed by LIUM and CVC for the WMT16
Multimodal Machine Translation challenge. We explored various comparative
methods, namely phrase-based systems and attentional recurrent neural networks
models trained using monomodal or multimodal data. We also performed a human
evaluation in orde... | computer science |
613 | Very Deep Convolutional Networks for Text Classification | cs.CL | The dominant approach for many NLP tasks are recurrent neural networks, in
particular LSTMs, and convolutional neural networks. However, these
architectures are rather shallow in comparison to the deep convolutional
networks which have pushed the state-of-the-art in computer vision. We present
a new architecture (VDCNN... | computer science |
614 | Improving Recurrent Neural Networks For Sequence Labelling | cs.CL | In this paper we study different types of Recurrent Neural Networks (RNN) for
sequence labeling tasks. We propose two new variants of RNNs integrating
improvements for sequence labeling, and we compare them to the more traditional
Elman and Jordan RNNs. We compare all models, either traditional or new, on
four distinct... | computer science |
615 | Sentence Similarity Measures for Fine-Grained Estimation of Topical
Relevance in Learner Essays | cs.CL | We investigate the task of assessing sentence-level prompt relevance in
learner essays. Various systems using word overlap, neural embeddings and
neural compositional models are evaluated on two datasets of learner writing.
We propose a new method for sentence-level similarity calculation, which learns
to adjust the we... | computer science |
616 | Deep CNNs along the Time Axis with Intermap Pooling for Robustness to
Spectral Variations | cs.CL | Convolutional neural networks (CNNs) with convolutional and pooling
operations along the frequency axis have been proposed to attain invariance to
frequency shifts of features. However, this is inappropriate with regard to the
fact that acoustic features vary in frequency. In this paper, we contend that
convolution alo... | computer science |
617 | Automatic Text Scoring Using Neural Networks | cs.CL | Automated Text Scoring (ATS) provides a cost-effective and consistent
alternative to human marking. However, in order to achieve good performance,
the predictive features of the system need to be manually engineered by human
experts. We introduce a model that forms word representations by learning the
extent to which s... | computer science |
618 | A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic
Modeling in Speech Recognition | cs.NE | We present a comprehensive study of deep bidirectional long short-term memory
(LSTM) recurrent neural network (RNN) based acoustic models for automatic
speech recognition (ASR). We study the effect of size and depth and train
models of up to 8 layers. We investigate the training aspect and study
different variants of o... | computer science |
619 | Sequence-Level Knowledge Distillation | cs.CL | Neural machine translation (NMT) offers a novel alternative formulation of
translation that is potentially simpler than statistical approaches. However to
reach competitive performance, NMT models need to be exceedingly large. In this
paper we consider applying knowledge distillation approaches (Bucila et al.,
2006; Hi... | computer science |
620 | Learning Semantically Coherent and Reusable Kernels in Convolution
Neural Nets for Sentence Classification | cs.CL | The state-of-the-art CNN models give good performance on sentence
classification tasks. The purpose of this work is to empirically study
desirable properties such as semantic coherence, attention mechanism and
reusability of CNNs in these tasks. Semantically coherent kernels are
preferable as they are a lot more interp... | computer science |
621 | RETURNN: The RWTH Extensible Training framework for Universal Recurrent
Neural Networks | cs.LG | In this work we release our extensible and easily configurable neural network
training software. It provides a rich set of functional layers with a
particular focus on efficient training of recurrent neural network topologies
on multiple GPUs. The source of the software package is public and freely
available for academ... | computer science |
622 | Character-Level Language Modeling with Hierarchical Recurrent Neural
Networks | cs.LG | Recurrent neural network (RNN) based character-level language models (CLMs)
are extremely useful for modeling out-of-vocabulary words by nature. However,
their performance is generally much worse than the word-level language models
(WLMs), since CLMs need to consider longer history of tokens to properly
predict the nex... | computer science |
623 | Multi-task Recurrent Model for True Multilingual Speech Recognition | cs.CL | Research on multilingual speech recognition remains attractive yet
challenging. Recent studies focus on learning shared structures under the
multi-task paradigm, in particular a feature sharing structure. This approach
has been found effective to improve performance on each individual language.
However, this approach i... | computer science |
624 | Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep
Recurrent models | cs.CL | Sentiment Analysis (SA) is an action research area in the digital age. With
rapid and constant growth of online social media sites and services, and the
increasing amount of textual data such as - statuses, comments, reviews etc.
available in them, application of automatic SA is on the rise. However, most of
the resear... | computer science |
625 | Attending to Characters in Neural Sequence Labeling Models | cs.CL | Sequence labeling architectures use word embeddings for capturing similarity,
but suffer when handling previously unseen or rare words. We investigate
character-level extensions to such models and propose a novel architecture for
combining alternative word representations. By using an attention mechanism,
the model is ... | computer science |
626 | Visualizing and Understanding Curriculum Learning for Long Short-Term
Memory Networks | cs.CL | Curriculum Learning emphasizes the order of training instances in a
computational learning setup. The core hypothesis is that simpler instances
should be learned early as building blocks to learn more complex ones. Despite
its usefulness, it is still unknown how exactly the internal representation of
models are affecte... | computer science |
627 | Dense Prediction on Sequences with Time-Dilated Convolutions for Speech
Recognition | cs.CL | In computer vision pixelwise dense prediction is the task of predicting a
label for each pixel in the image. Convolutional neural networks achieve good
performance on this task, while being computationally efficient. In this paper
we carry these ideas over to the problem of assigning a sequence of labels to a
set of sp... | computer science |
628 | End-to-End ASR-free Keyword Search from Speech | cs.CL | End-to-end (E2E) systems have achieved competitive results compared to
conventional hybrid hidden Markov model (HMM)-deep neural network based
automatic speech recognition (ASR) systems. Such E2E systems are attractive due
to the lack of dependence on alignments between input acoustic and output
grapheme or HMM state s... | computer science |
629 | Training Language Models Using Target-Propagation | cs.CL | While Truncated Back-Propagation through Time (BPTT) is the most popular
approach to training Recurrent Neural Networks (RNNs), it suffers from being
inherently sequential (making parallelization difficult) and from truncating
gradient flow between distant time-steps. We investigate whether Target
Propagation (TPROP) s... | computer science |
630 | Deep Voice: Real-time Neural Text-to-Speech | cs.CL | We present Deep Voice, a production-quality text-to-speech system constructed
entirely from deep neural networks. Deep Voice lays the groundwork for truly
end-to-end neural speech synthesis. The system comprises five major building
blocks: a segmentation model for locating phoneme boundaries, a
grapheme-to-phoneme conv... | computer science |
631 | Improved Variational Autoencoders for Text Modeling using Dilated
Convolutions | cs.NE | Recent work on generative modeling of text has found that variational
auto-encoders (VAE) incorporating LSTM decoders perform worse than simpler LSTM
language models (Bowman et al., 2015). This negative result is so far poorly
understood, but has been attributed to the propensity of LSTM decoders to
ignore conditioning... | computer science |
632 | Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence
Labelling | cs.CL | Most existing sequence labelling models rely on a fixed decomposition of a
target sequence into a sequence of basic units. These methods suffer from two
major drawbacks: 1) the set of basic units is fixed, such as the set of words,
characters or phonemes in speech recognition, and 2) the decomposition of
target sequenc... | computer science |
633 | Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model) | cs.CL | We examine Memory Networks for the task of question answering (QA), under
common real world scenario where training examples are scarce and under weakly
supervised scenario, that is only extrinsic labels are available for training.
We propose extensions for the Dynamic Memory Network (DMN), specifically within
the atte... | computer science |
634 | Simplified End-to-End MMI Training and Voting for ASR | cs.LG | A simplified speech recognition system that uses the maximum mutual
information (MMI) criterion is considered. End-to-end training using gradient
descent is suggested, similarly to the training of connectionist temporal
classification (CTC). We use an MMI criterion with a simple language model in
the training stage, an... | computer science |
635 | Learning to Generate Reviews and Discovering Sentiment | cs.LG | We explore the properties of byte-level recurrent language models. When given
sufficient amounts of capacity, training data, and compute time, the
representations learned by these models include disentangled features
corresponding to high-level concepts. Specifically, we find a single unit which
performs sentiment anal... | computer science |
636 | Semi-supervised Multitask Learning for Sequence Labeling | cs.CL | We propose a sequence labeling framework with a secondary training objective,
learning to predict surrounding words for every word in the dataset. This
language modeling objective incentivises the system to learn general-purpose
patterns of semantic and syntactic composition, which are also useful for
improving accurac... | computer science |
637 | Going Wider: Recurrent Neural Network With Parallel Cells | cs.CL | Recurrent Neural Network (RNN) has been widely applied for sequence modeling.
In RNN, the hidden states at current step are full connected to those at
previous step, thus the influence from less related features at previous step
may potentially decrease model's learning ability. We propose a simple
technique called par... | computer science |
638 | Phonetic Temporal Neural Model for Language Identification | cs.CL | Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We ... | computer science |
639 | Relevance-based Word Embedding | cs.IR | Learning a high-dimensional dense representation for vocabulary terms, also
known as a word embedding, has recently attracted much attention in natural
language processing and information retrieval tasks. The embedding vectors are
typically learned based on term proximity in a large corpus. This means that
the objectiv... | computer science |
640 | Deriving Neural Architectures from Sequence and Graph Kernels | cs.NE | The design of neural architectures for structured objects is typically guided
by experimental insights rather than a formal process. In this work, we appeal
to kernels over combinatorial structures, such as sequences and graphs, to
derive appropriate neural operations. We introduce a class of deep recurrent
neural oper... | computer science |
641 | On Multilingual Training of Neural Dependency Parsers | cs.CL | We show that a recently proposed neural dependency parser can be improved by
joint training on multiple languages from the same family. The parser is
implemented as a deep neural network whose only input is orthographic
representations of words. In order to successfully parse, the network has to
discover how linguistic... | computer science |
642 | Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks | cs.CL | Ladder networks are a notable new concept in the field of semi-supervised
learning by showing state-of-the-art results in image recognition tasks while
being compatible with many existing neural architectures. We present the
recurrent ladder network, a novel modification of the ladder network, for
semi-supervised learn... | computer science |
643 | Adversarially Regularized Autoencoders | cs.LG | While autoencoders are a key technique in representation learning for
continuous structures, such as images or wave forms, developing general-purpose
autoencoders for discrete structures, such as text sequence or discretized
images, has proven to be more challenging. In particular, discrete inputs make
it more difficul... | computer science |
644 | Auxiliary Objectives for Neural Error Detection Models | cs.CL | We investigate the utility of different auxiliary objectives and training
strategies within a neural sequence labeling approach to error detection in
learner writing. Auxiliary costs provide the model with additional linguistic
information, allowing it to learn general-purpose compositional features that
can then be ex... | computer science |
645 | An Error-Oriented Approach to Word Embedding Pre-Training | cs.CL | We propose a novel word embedding pre-training approach that exploits writing
errors in learners' scripts. We compare our method to previous models that tune
the embeddings based on script scores and the discrimination between correct
and corrupt word contexts in addition to the generic commonly-used embeddings
pre-tra... | computer science |
646 | A Continuous Relaxation of Beam Search for End-to-end Training of Neural
Sequence Models | cs.LG | Beam search is a desirable choice of test-time decoding algorithm for neural
sequence models because it potentially avoids search errors made by simpler
greedy methods. However, typical cross entropy training procedures for these
models do not directly consider the behaviour of the final decoding method. As
a result, f... | computer science |
647 | Regularizing and Optimizing LSTM Language Models | cs.CL | Recurrent neural networks (RNNs), such as long short-term memory networks
(LSTMs), serve as a fundamental building block for many sequence learning
tasks, including machine translation, language modeling, and question
answering. In this paper, we consider the specific problem of word-level
language modeling and investi... | computer science |
648 | Supervised Speech Separation Based on Deep Learning: An Overview | cs.CL | Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background ... | computer science |
649 | Grasping the Finer Point: A Supervised Similarity Network for Metaphor
Detection | cs.CL | The ubiquity of metaphor in our everyday communication makes it an important
problem for natural language understanding. Yet, the majority of metaphor
processing systems to date rely on hand-engineered features and there is still
no consensus in the field as to which features are optimal for this task. In
this paper, w... | computer science |
650 | Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words | cs.CL | Distributed word embeddings have shown superior performances in numerous
Natural Language Processing (NLP) tasks. However, their performances vary
significantly across different tasks, implying that the word embeddings learnt
by those methods capture complementary aspects of lexical semantics. Therefore,
we believe tha... | computer science |
651 | KeyVec: Key-semantics Preserving Document Representations | cs.CL | Previous studies have demonstrated the empirical success of word embeddings
in various applications. In this paper, we investigate the problem of learning
distributed representations for text documents which many machine learning
algorithms take as input for a number of NLP tasks.
We propose a neural network model, K... | computer science |
652 | Exploring Asymmetric Encoder-Decoder Structure for Context-based
Sentence Representation Learning | cs.NE | Context information plays an important role in human language understanding,
and it is also useful for machines to learn vector representations of language.
In this paper, we explore an asymmetric encoder-decoder structure for
unsupervised context-based sentence representation learning. As a result, we
build an encoder... | computer science |
653 | CNN Is All You Need | cs.CL | The Convolution Neural Network (CNN) has demonstrated the unique advantage in
audio, image and text learning; recently it has also challenged Recurrent
Neural Networks (RNNs) with long short-term memory cells (LSTM) in
sequence-to-sequence learning, since the computations involved in CNN are
easily parallelizable where... | computer science |
654 | Combining Representation Learning with Logic for Language Processing | cs.NE | The current state-of-the-art in many natural language processing and
automated knowledge base completion tasks is held by representation learning
methods which learn distributed vector representations of symbols via
gradient-based optimization. They require little or no hand-crafted features,
thus avoiding the need for... | computer science |
655 | A Note on Topology Preservation in Classification, and the Construction
of a Universal Neuron Grid | cs.NE | It will be shown that according to theorems of K. Menger, every neuron grid
if identified with a curve is able to preserve the adopted qualitative
structure of a data space. Furthermore, if this identification is made, the
neuron grid structure can always be mapped to a subset of a universal neuron
grid which is constr... | computer science |
656 | Linear-Nonlinear-Poisson Neuron Networks Perform Bayesian Inference On
Boltzmann Machines | cs.AI | One conjecture in both deep learning and classical connectionist viewpoint is
that the biological brain implements certain kinds of deep networks as its
back-end. However, to our knowledge, a detailed correspondence has not yet been
set up, which is important if we want to bridge between neuroscience and
machine learni... | computer science |
657 | Mapping Temporal Variables into the NeuCube for Improved Pattern
Recognition, Predictive Modelling and Understanding of Stream Data | cs.NE | This paper proposes a new method for an optimized mapping of temporal
variables, describing a temporal stream data, into the recently proposed
NeuCube spiking neural network architecture. This optimized mapping extends the
use of the NeuCube, which was initially designed for spatiotemporal brain data,
to work on arbitr... | computer science |
658 | An Evolutionary Algorithm to Learn SPARQL Queries for
Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia | cs.AI | Efficient usage of the knowledge provided by the Linked Data community is
often hindered by the need for domain experts to formulate the right SPARQL
queries to answer questions. For new questions they have to decide which
datasets are suitable and in which terminology and modelling style to phrase
the SPARQL query.
... | computer science |
659 | A Geometric Framework for Convolutional Neural Networks | stat.ML | In this paper, a geometric framework for neural networks is proposed. This
framework uses the inner product space structure underlying the parameter set
to perform gradient descent not in a component-based form, but in a
coordinate-free manner. Convolutional neural networks are described in this
framework in a compact ... | computer science |
660 | A Novel Representation of Neural Networks | stat.ML | Deep Neural Networks (DNNs) have become very popular for prediction in many
areas. Their strength is in representation with a high number of parameters
that are commonly learned via gradient descent or similar optimization methods.
However, the representation is non-standardized, and the gradient calculation
methods ar... | computer science |
661 | Converting Cascade-Correlation Neural Nets into Probabilistic Generative
Models | cs.AI | Humans are not only adept in recognizing what class an input instance belongs
to (i.e., classification task), but perhaps more remarkably, they can imagine
(i.e., generate) plausible instances of a desired class with ease, when
prompted. Inspired by this, we propose a framework which allows transforming
Cascade-Correla... | computer science |
662 | On the Performance of Network Parallel Training in Artificial Neural
Networks | cs.AI | Artificial Neural Networks (ANNs) have received increasing attention in
recent years with applications that span a wide range of disciplines including
vital domains such as medicine, network security and autonomous transportation.
However, neural network architectures are becoming increasingly complex and
with an incre... | computer science |
663 | Programmable Agents | cs.AI | We build deep RL agents that execute declarative programs expressed in formal
language. The agents learn to ground the terms in this language in their
environment, and can generalize their behavior at test time to execute new
programs that refer to objects that were not referenced during training. The
agents develop di... | computer science |
664 | Explainable Artificial Intelligence: Understanding, Visualizing and
Interpreting Deep Learning Models | cs.AI | With the availability of large databases and recent improvements in deep
learning methodology, the performance of AI systems is reaching or even
exceeding the human level on an increasing number of complex tasks. Impressive
examples of this development can be found in domains such as image
classification, sentiment ana... | computer science |
665 | Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? | cs.AI | Deep reinforcement learning has achieved many recent successes, but our
understanding of its strengths and limitations is hampered by the lack of rich
environments in which we can fully characterize optimal behavior, and
correspondingly diagnose individual actions against such a characterization.
Here we consider a fam... | computer science |
666 | Emergence of grid-like representations by training recurrent neural
networks to perform spatial localization | cs.AI | Decades of research on the neural code underlying spatial navigation have
revealed a diverse set of neural response properties. The Entorhinal Cortex
(EC) of the mammalian brain contains a rich set of spatial correlates,
including grid cells which encode space using tessellating patterns. However,
the mechanisms and fu... | computer science |
667 | Dimensionality Reduction and Reconstruction using Mirroring Neural
Networks and Object Recognition based on Reduced Dimension Characteristic
Vector | cs.CV | In this paper, we present a Mirroring Neural Network architecture to perform
non-linear dimensionality reduction and Object Recognition using a reduced
lowdimensional characteristic vector. In addition to dimensionality reduction,
the network also reconstructs (mirrors) the original high-dimensional input
vector from t... | computer science |
668 | Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach | cs.CV | Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI)
data based on a standard General Linear Model (GLM)and spectral clustering was
recently proposed as a means to alleviate the issues associated with spatial
normalization in fMRI. However, for all its appeal, a GLM-based parcellation
approach int... | computer science |
669 | Iris Codes Classification Using Discriminant and Witness Directions | cs.NE | The main topic discussed in this paper is how to use intelligence for
biometric decision defuzzification. A neural training model is proposed and
tested here as a possible solution for dealing with natural fuzzification that
appears between the intra- and inter-class distribution of scores computed
during iris recognit... | computer science |
670 | Algorithms for Image Analysis and Combination of Pattern Classifiers
with Application to Medical Diagnosis | cs.CV | Medical Informatics and the application of modern signal processing in the
assistance of the diagnostic process in medical imaging is one of the more
recent and active research areas today. This thesis addresses a variety of
issues related to the general problem of medical image analysis, specifically
in mammography, a... | computer science |
671 | Deep Neural Networks are Easily Fooled: High Confidence Predictions for
Unrecognizable Images | cs.CV | Deep neural networks (DNNs) have recently been achieving state-of-the-art
performance on a variety of pattern-recognition tasks, most notably visual
classification problems. Given that DNNs are now able to classify objects in
images with near-human-level performance, questions naturally arise as to what
differences rem... | computer science |
672 | Homogeneous Spiking Neuromorphic System for Real-World Pattern
Recognition | cs.NE | A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus elimi... | computer science |
673 | Crowd Behavior Analysis: A Review where Physics meets Biology | cs.CV | Although the traits emerged in a mass gathering are often non-deliberative,
the act of mass impulse may lead to irre- vocable crowd disasters. The two-fold
increase of carnage in crowd since the past two decades has spurred significant
advances in the field of computer vision, towards effective and proactive crowd
surv... | computer science |
674 | Can Pretrained Neural Networks Detect Anatomy? | cs.CV | Convolutional neural networks demonstrated outstanding empirical results in
computer vision and speech recognition tasks where labeled training data is
abundant. In medical imaging, there is a huge variety of possible imaging
modalities and contrasts, where annotated data is usually very scarce. We
present two approach... | computer science |
675 | Metaheuristic Algorithms for Convolution Neural Network | cs.CV | A typical modern optimization technique is usually either heuristic or
metaheuristic. This technique has managed to solve some optimization problems
in the research area of science, engineering, and industry. However,
implementation strategy of metaheuristic for accuracy improvement on
convolution neural networks (CNN)... | computer science |
676 | Hadamard Product for Low-rank Bilinear Pooling | cs.CV | Bilinear models provide rich representations compared with linear models.
They have been applied in various visual tasks, such as object recognition,
segmentation, and visual question-answering, to get state-of-the-art
performances taking advantage of the expanded representations. However,
bilinear representations tend... | computer science |
677 | Incremental Network Quantization: Towards Lossless CNNs with
Low-Precision Weights | cs.CV | This paper presents incremental network quantization (INQ), a novel method,
targeting to efficiently convert any pre-trained full-precision convolutional
neural network (CNN) model into a low-precision version whose weights are
constrained to be either powers of two or zero. Unlike existing methods which
are struggled ... | computer science |
678 | LesionSeg: Semantic segmentation of skin lesions using Deep
Convolutional Neural Network | cs.CV | We present a method for skin lesion segmentation for the ISIC 2017 Skin
Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional
Network architecture which is trained end to end, from scratch, on a limited
dataset. Our semantic segmentation architecture utilizes several recent
innovations in partic... | computer science |
679 | Convolutional Spike Timing Dependent Plasticity based Feature Learning
in Spiking Neural Networks | cs.NE | Brain-inspired learning models attempt to mimic the cortical architecture and
computations performed in the neurons and synapses constituting the human brain
to achieve its efficiency in cognitive tasks. In this work, we present
convolutional spike timing dependent plasticity based feature learning with
biologically pl... | computer science |
680 | Adversarial Transformation Networks: Learning to Generate Adversarial
Examples | cs.NE | Multiple different approaches of generating adversarial examples have been
proposed to attack deep neural networks. These approaches involve either
directly computing gradients with respect to the image pixels, or directly
solving an optimization on the image pixels. In this work, we present a
fundamentally new method ... | computer science |
681 | Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced
Attentive Response Approach for Explaining and Visualizing Deep
Learning-Driven Stock Market Prediction | cs.AI | Deep learning has been shown to outperform traditional machine learning
algorithms across a wide range of problem domains. However, current deep
learning algorithms have been criticized as uninterpretable "black-boxes" which
cannot explain their decision making processes. This is a major shortcoming
that prevents the w... | computer science |
682 | Fast YOLO: A Fast You Only Look Once System for Real-time Embedded
Object Detection in Video | cs.CV | Object detection is considered one of the most challenging problems in this
field of computer vision, as it involves the combination of object
classification and object localization within a scene. Recently, deep neural
networks (DNNs) have been demonstrated to achieve superior object detection
performance compared to ... | computer science |
683 | NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm | cs.NE | Neural networks (NNs) have begun to have a pervasive impact on various
applications of machine learning. However, the problem of finding an optimal NN
architecture for large applications has remained open for several decades.
Conventional approaches search for the optimal NN architecture through
extensive trial-and-err... | computer science |
684 | Analysis of supervised and semi-supervised GrowCut applied to
segmentation of masses in mammography images | cs.CV | Breast cancer is already one of the most common form of cancer worldwide.
Mammography image analysis is still the most effective diagnostic method to
promote the early detection of breast cancer. Accurately segmenting tumors in
digital mammography images is important to improve diagnosis capabilities of
health speciali... | computer science |
685 | Empirical Explorations in Training Networks with Discrete Activations | cs.NE | We present extensive experiments training and testing hidden units in deep
networks that emit only a predefined, static, number of discretized values.
These units provide benefits in real-world deployment in systems in which
memory and/or computation may be limited. Additionally, they are particularly
well suited for u... | computer science |
686 | Regularized Evolution for Image Classifier Architecture Search | cs.NE | The effort devoted to hand-crafting image classifiers has motivated the use
of architecture search to discover them automatically. Reinforcement learning
and evolution have both shown promise for this purpose. This study employs a
regularized version of a popular asynchronous evolutionary algorithm. We
rigorously compa... | computer science |
687 | Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network
for Real-time Embedded Object Detection | cs.CV | Object detection is a major challenge in computer vision, involving both
object classification and object localization within a scene. While deep neural
networks have been shown in recent years to yield very powerful techniques for
tackling the challenge of object detection, one of the biggest challenges with
enabling ... | computer science |
688 | Inferencing Based on Unsupervised Learning of Disentangled
Representations | cs.CV | Combining Generative Adversarial Networks (GANs) with encoders that learn to
encode data points has shown promising results in learning data representations
in an unsupervised way. We propose a framework that combines an encoder and a
generator to learn disentangled representations which encode meaningful
information a... | computer science |
689 | The Parameter-Less Self-Organizing Map algorithm | cs.NE | The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network
algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a
learning rate and annealing schemes for learning rate and neighbourhood size.
We discuss the relative performance of the PLSOM and the SOM and demonstrate
some tasks in w... | computer science |
690 | Simplified firefly algorithm for 2D image key-points search | cs.NE | In order to identify an object, human eyes firstly search the field of view
for points or areas which have particular properties. These properties are used
to recognise an image or an object. Then this process could be taken as a model
to develop computer algorithms for images identification. This paper proposes
the id... | computer science |
691 | Deep-Plant: Plant Identification with convolutional neural networks | cs.CV | This paper studies convolutional neural networks (CNN) to learn unsupervised
feature representations for 44 different plant species, collected at the Royal
Botanic Gardens, Kew, England. To gain intuition on the chosen features from
the CNN model (opposed to a 'black box' solution), a visualisation technique
based on t... | computer science |
692 | Adapting Deep Network Features to Capture Psychological Representations | cs.CV | Deep neural networks have become increasingly successful at solving classic
perception problems such as object recognition, semantic segmentation, and
scene understanding, often reaching or surpassing human-level accuracy. This
success is due in part to the ability of DNNs to learn useful representations
of high-dimens... | computer science |
693 | Large-Scale Evolution of Image Classifiers | cs.NE | Neural networks have proven effective at solving difficult problems but
designing their architectures can be challenging, even for image classification
problems alone. Our goal is to minimize human participation, so we employ
evolutionary algorithms to discover such networks automatically. Despite
significant computati... | computer science |
694 | A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification
and Domain Adaptation | cs.CV | Recently, DNN model compression based on network architecture design, e.g.,
SqueezeNet, attracted a lot attention. No accuracy drop on image classification
is observed on these extremely compact networks, compared to well-known models.
An emerging question, however, is whether these model compression techniques
hurt DN... | computer science |
695 | Identifying Spatial Relations in Images using Convolutional Neural
Networks | cs.AI | Traditional approaches to building a large scale knowledge graph have usually
relied on extracting information (entities, their properties, and relations
between them) from unstructured text (e.g. Dbpedia). Recent advances in
Convolutional Neural Networks (CNN) allow us to shift our focus to learning
entities and relat... | computer science |
696 | Hierarchical Attentive Recurrent Tracking | cs.CV | Class-agnostic object tracking is particularly difficult in cluttered
environments as target specific discriminative models cannot be learned a
priori. Inspired by how the human visual cortex employs spatial attention and
separate "where" and "what" processing pathways to actively suppress irrelevant
visual features, t... | computer science |
697 | PSIque: Next Sequence Prediction of Satellite Images using a
Convolutional Sequence-to-Sequence Network | cs.CV | Predicting unseen weather phenomena is an important issue for disaster
management. In this paper, we suggest a model for a convolutional
sequence-to-sequence autoencoder for predicting undiscovered weather situations
from previous satellite images. We also propose a symmetric skip connection
between encoder and decoder... | computer science |
698 | Evaluation of Alzheimer's Disease by Analysis of MR Images using
Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the
ADC Maps | eess.IV | Alzheimer's disease is the most common cause of dementia, yet hard to
diagnose precisely without invasive techniques, particularly at the onset of
the disease. This work approaches image analysis and classification of
synthetic multispectral images composed by diffusion-weighted magnetic
resonance (MR) cerebral images ... | computer science |
699 | Neural tuning size is a key factor underlying holistic face processing | cs.AI | Faces are a class of visual stimuli with unique significance, for a variety
of reasons. They are ubiquitous throughout the course of a person's life, and
face recognition is crucial for daily social interaction. Faces are also unlike
any other stimulus class in terms of certain physical stimulus characteristics.
Furthe... | computer science |
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