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8,000 | Fixing Weight Decay Regularization in Adam | cs.LG | L$_2$ regularization and weight decay regularization are equivalent for
standard stochastic gradient descent (when rescaled by the learning rate), but
as we demonstrate this is \emph{not} the case for adaptive gradient algorithms,
such as Adam. While common deep learning frameworks of these algorithms
implement L$_2$ r... | computer science |
8,001 | Chipmunk: A Systolically Scalable 0.9 mm${}^2$, 3.08 Gop/s/mW @ 1.2 mW
Accelerator for Near-Sensor Recurrent Neural Network Inference | cs.DC | Recurrent neural networks (RNNs) are state-of-the-art in voice
awareness/understanding and speech recognition. On-device computation of RNNs
on low-power mobile and wearable devices would be key to applications such as
zero-latency voice-based human-machine interfaces. Here we present Chipmunk, a
small (<1 mm${}^2$) ha... | computer science |
8,002 | Exploring Speech Enhancement with Generative Adversarial Networks for
Robust Speech Recognition | cs.SD | We investigate the effectiveness of generative adversarial networks (GANs)
for speech enhancement, in the context of improving noise robustness of
automatic speech recognition (ASR) systems. Prior work demonstrates that GANs
can effectively suppress additive noise in raw waveform speech signals,
improving perceptual qu... | computer science |
8,003 | Classification of postoperative surgical site infections from blood
measurements with missing data using recurrent neural networks | cs.NE | Clinical measurements that can be represented as time series constitute an
important fraction of the electronic health records and are often both
uncertain and incomplete. Recurrent neural networks are a special class of
neural networks that are particularly suitable to process time series data but,
in their original f... | computer science |
8,004 | An Elementary Analysis of the Probability That a Binomial Random
Variable Exceeds Its Expectation | math.PR | We give an elementary proof of the fact that a binomial random variable $X$
with parameters $n$ and $0.29/n \le p < 1$ with probability at least $1/4$
strictly exceeds its expectation. We also show that for $1/n \le p < 1 - 1/n$,
$X$ exceeds its expectation by more than one with probability at least
$0.0370$. Both prob... | computer science |
8,005 | Mitigating Asymmetric Nonlinear Weight Update Effects in Hardware Neural
Network based on Analog Resistive Synapse | cs.LG | Asymmetric nonlinear weight update is considered as one of the major
obstacles for realizing hardware neural networks based on analog resistive
synapses because it significantly compromises the online training capability.
This paper provides new solutions to this critical issue through
co-optimization with the hardware... | computer science |
8,006 | Dendritic error backpropagation in deep cortical microcircuits | cs.LG | Animal behaviour depends on learning to associate sensory stimuli with the
desired motor command. Understanding how the brain orchestrates the necessary
synaptic modifications across different brain areas has remained a longstanding
puzzle. Here, we introduce a multi-area neuronal network model in which
synaptic plasti... | computer science |
8,007 | Bridging the Gap Between Neural Networks and Neuromorphic Hardware with
A Neural Network Compiler | cs.NE | Different from developing neural networks (NNs) for general-purpose
processors, the development for NN chips usually faces with some
hardware-specific restrictions, such as limited precision of network signals
and parameters, constrained computation scale, and limited types of non-linear
functions.
This paper propose... | computer science |
8,008 | An Implementation of Back-Propagation Learning on GF11, a Large SIMD
Parallel Computer | cs.LG | Current connectionist simulations require huge computational resources. We
describe a neural network simulator for the IBM GF11, an experimental SIMD
machine with 566 processors and a peak arithmetic performance of 11 Gigaflops.
We present our parallel implementation of the backpropagation learning
algorithm, technique... | computer science |
8,009 | Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC)
Perspective | cs.LG | Machine learning is playing an increasingly significant role in emerging
mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware
architects have designed customized hardware for machine learning algorithms,
especially neural networks, to improve compute efficiency. However, machine
learning is typica... | computer science |
8,010 | CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs | cs.NE | Deep Neural Networks are becoming increasingly popular in always-on IoT edge
devices performing data analytics right at the source, reducing latency as well
as energy consumption for data communication. This paper presents CMSIS-NN,
efficient kernels developed to maximize the performance and minimize the memory
footpri... | computer science |
8,011 | Mixed Precision Training of Convolutional Neural Networks using Integer
Operations | cs.NE | The state-of-the-art (SOTA) for mixed precision training is dominated by
variants of low precision floating point operations, and in particular, FP16
accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a
lot of research has also happened in the domain of low and mixed-precision
Integer training,... | computer science |
8,012 | Biological Mechanisms for Learning: A Computational Model of Olfactory
Learning in the Manduca sexta Moth, with Applications to Neural Nets | cs.LG | The insect olfactory system, which includes the antennal lobe (AL), mushroom
body (MB), and ancillary structures, is a relatively simple neural system
capable of learning. Its structural features, which are widespread in
biological neural systems, process olfactory stimuli through a cascade of
networks where large dime... | computer science |
8,013 | ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error
Resilience for Energy Efficient Deep Neural Network Accelerators | cs.NE | Hardware accelerators are being increasingly deployed to boost the
performance and energy efficiency of deep neural network (DNN) inference. In
this paper we propose Thundervolt, a new framework that enables aggressive
voltage underscaling of high-performance DNN accelerators without compromising
classification accurac... | computer science |
8,014 | Three levels of neural reuse through the dynamical lens: structural
network, autonomous dynamics and transient dynamics | cs.NE | The brain in conjunction with the body is able to adapt to new environments
and perform multiple behaviors through reuse of neural resources and transfer
of existing behavioral traits. Although mechanisms that underlie this ability
are not well known, they are largely attributed to neuromodulation. In this
work, we dem... | computer science |
8,015 | Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach | cs.IT | We present a novel framework for applying deep neural networks (DNN) to soft
decoding of linear codes at arbitrary block lengths. Unlike other approaches,
our framework allows unconstrained DNN design, enabling the free application of
powerful designs that were developed in other contexts. Our method is robust to
overf... | computer science |
8,016 | Exploring Hidden Dimensions in Parallelizing Convolutional Neural
Networks | cs.LG | The past few years have witnessed growth in the size and computational
requirements for training deep convolutional neural networks. Current
approaches parallelize the training process onto multiple devices by applying a
single parallelization strategy (e.g., data or model parallelism) to all layers
in a network. Altho... | computer science |
8,017 | Convolutional Neural Networks over Control Flow Graphs for Software
Defect Prediction | cs.SE | Existing defects in software components is unavoidable and leads to not only
a waste of time and money but also many serious consequences. To build
predictive models, previous studies focus on manually extracting features or
using tree representations of programs, and exploiting different machine
learning algorithms. H... | computer science |
8,018 | Putting a bug in ML: The moth olfactory network learns to read MNIST | cs.LG | We seek to (i) characterize the learning architectures exploited in
biological neural networks for training on very few samples, and (ii) port
these algorithmic structures to a machine learning context. The Moth Olfactory
Network is among the simplest biological neural systems that can learn, and its
architecture inclu... | computer science |
8,019 | The Mechanics of n-Player Differentiable Games | cs.LG | The cornerstone underpinning deep learning is the guarantee that gradient
descent on an objective converges to local minima. Unfortunately, this
guarantee fails in settings, such as generative adversarial nets, where there
are multiple interacting losses. The behavior of gradient-based methods in
games is not well unde... | computer science |
8,020 | Behavioral Learning of Aircraft Landing Sequencing Using a Society of
Probabilistic Finite State Machines | cs.NE | Air Traffic Control (ATC) is a complex safety critical environment. A tower
controller would be making many decisions in real-time to sequence aircraft.
While some optimization tools exist to help the controller in some airports,
even in these situations, the real sequence of the aircraft adopted by the
controller is s... | computer science |
8,021 | A multi-instance deep neural network classifier: application to Higgs
boson CP measurement | cs.LG | We investigate properties of a classifier applied to the measurements of the
CP state of the Higgs boson in $H\rightarrow\tau\tau$ decays. The problem is
framed as binary classifier applied to individual instances. Then the prior
knowledge that the instances belong to the same class is used to define the
multi-instance... | computer science |
8,022 | Artificial neural network based modelling approach for municipal solid
waste gasification in a fluidized bed reactor | cs.LG | In this paper, multi-layer feed forward neural networks are used to predict
the lower heating value of gas (LHV), lower heating value of gasification
products including tars and entrained char (LHVp) and syngas yield during
gasification of municipal solid waste (MSW) during gasification in a fluidized
bed reactor. Thes... | computer science |
8,023 | Technical Note: Bias and the Quantification of Stability | cs.LG | Research on bias in machine learning algorithms has generally been concerned
with the impact of bias on predictive accuracy. We believe that there are other
factors that should also play a role in the evaluation of bias. One such factor
is the stability of the algorithm; in other words, the repeatability of the
results... | computer science |
8,024 | A Theory of Cross-Validation Error | cs.LG | This paper presents a theory of error in cross-validation testing of
algorithms for predicting real-valued attributes. The theory justifies the
claim that predicting real-valued attributes requires balancing the conflicting
demands of simplicity and accuracy. Furthermore, the theory indicates precisely
how these confli... | computer science |
8,025 | Theoretical Analyses of Cross-Validation Error and Voting in
Instance-Based Learning | cs.LG | This paper begins with a general theory of error in cross-validation testing
of algorithms for supervised learning from examples. It is assumed that the
examples are described by attribute-value pairs, where the values are symbolic.
Cross-validation requires a set of training examples and a set of testing
examples. The... | computer science |
8,026 | Types of Cost in Inductive Concept Learning | cs.LG | Inductive concept learning is the task of learning to assign cases to a
discrete set of classes. In real-world applications of concept learning, there
are many different types of cost involved. The majority of the machine learning
literature ignores all types of cost (unless accuracy is interpreted as a type
of cost me... | computer science |
8,027 | Exploiting Context When Learning to Classify | cs.LG | This paper addresses the problem of classifying observations when features
are context-sensitive, specifically when the testing set involves a context
that is different from the training set. The paper begins with a precise
definition of the problem, then general strategies are presented for enhancing
the performance o... | computer science |
8,028 | The Management of Context-Sensitive Features: A Review of Strategies | cs.LG | In this paper, we review five heuristic strategies for handling
context-sensitive features in supervised machine learning from examples. We
discuss two methods for recovering lost (implicit) contextual information. We
mention some evidence that hybrid strategies can have a synergetic effect. We
then show how the work o... | computer science |
8,029 | The Identification of Context-Sensitive Features: A Formal Definition of
Context for Concept Learning | cs.LG | A large body of research in machine learning is concerned with supervised
learning from examples. The examples are typically represented as vectors in a
multi-dimensional feature space (also known as attribute-value descriptions). A
teacher partitions a set of training examples into a finite number of classes.
The task... | computer science |
8,030 | Robust Classification with Context-Sensitive Features | cs.LG | This paper addresses the problem of classifying observations when features
are context-sensitive, especially when the testing set involves a context that
is different from the training set. The paper begins with a precise definition
of the problem, then general strategies are presented for enhancing the
performance of ... | computer science |
8,031 | Manifold Learning with Geodesic Minimal Spanning Trees | cs.CV | In the manifold learning problem one seeks to discover a smooth low
dimensional surface, i.e., a manifold embedded in a higher dimensional linear
vector space, based on a set of measured sample points on the surface. In this
paper we consider the closely related problem of estimating the manifold's
intrinsic dimension ... | computer science |
8,032 | A Numerical Example on the Principles of Stochastic Discrimination | cs.CV | Studies on ensemble methods for classification suffer from the difficulty of
modeling the complementary strengths of the components. Kleinberg's theory of
stochastic discrimination (SD) addresses this rigorously via mathematical
notions of enrichment, uniformity, and projectability of an ensemble. We
explain these conc... | computer science |
8,033 | Self-Organised Factorial Encoding of a Toroidal Manifold | cs.LG | It is shown analytically how a neural network can be used optimally to encode
input data that is derived from a toroidal manifold. The case of a 2-layer
network is considered, where the output is assumed to be a set of discrete
neural firing events. The network objective function measures the average
Euclidean error th... | computer science |
8,034 | A kernel method for canonical correlation analysis | cs.LG | Canonical correlation analysis is a technique to extract common features from
a pair of multivariate data. In complex situations, however, it does not
extract useful features because of its linearity. On the other hand, kernel
method used in support vector machine is an efficient approach to improve such
a linear metho... | computer science |
8,035 | Bayesian Nonlinear Principal Component Analysis Using Random Fields | cs.CV | We propose a novel model for nonlinear dimension reduction motivated by the
probabilistic formulation of principal component analysis. Nonlinearity is
achieved by specifying different transformation matrices at different locations
of the latent space and smoothing the transformation using a Markov random
field type pri... | computer science |
8,036 | Learning Graph Matching | cs.CV | As a fundamental problem in pattern recognition, graph matching has
applications in a variety of fields, from computer vision to computational
biology. In graph matching, patterns are modeled as graphs and pattern
recognition amounts to finding a correspondence between the nodes of different
graphs. Many formulations o... | computer science |
8,037 | Robust Near-Isometric Matching via Structured Learning of Graphical
Models | cs.CV | Models for near-rigid shape matching are typically based on distance-related
features, in order to infer matches that are consistent with the isometric
assumption. However, real shapes from image datasets, even when expected to be
related by "almost isometric" transformations, are actually subject not only to
noise but... | computer science |
8,038 | A Theoretical Analysis of Joint Manifolds | cs.LG | The emergence of low-cost sensor architectures for diverse modalities has
made it possible to deploy sensor arrays that capture a single event from a
large number of vantage points and using multiple modalities. In many
scenarios, these sensors acquire very high-dimensional data such as audio
signals, images, and video... | computer science |
8,039 | On the Dual Formulation of Boosting Algorithms | cs.LG | We study boosting algorithms from a new perspective. We show that the
Lagrange dual problems of AdaBoost, LogitBoost and soft-margin LPBoost with
generalized hinge loss are all entropy maximization problems. By looking at the
dual problems of these boosting algorithms, we show that the success of
boosting algorithms ca... | computer science |
8,040 | Boosting through Optimization of Margin Distributions | cs.LG | Boosting has attracted much research attention in the past decade. The
success of boosting algorithms may be interpreted in terms of the margin
theory. Recently it has been shown that generalization error of classifiers can
be obtained by explicitly taking the margin distribution of the training data
into account. Most... | computer science |
8,041 | Information Distance in Multiples | cs.CV | Information distance is a parameter-free similarity measure based on
compression, used in pattern recognition, data mining, phylogeny, clustering,
and classification. The notion of information distance is extended from pairs
to multiples (finite lists). We study maximal overlap, metricity, universality,
minimal overlap... | computer science |
8,042 | Isometric Multi-Manifolds Learning | cs.LG | Isometric feature mapping (Isomap) is a promising manifold learning method.
However, Isomap fails to work on data which distribute on clusters in a single
manifold or manifolds. Many works have been done on extending Isomap to
multi-manifolds learning. In this paper, we first proposed a new
multi-manifolds learning alg... | computer science |
8,043 | Biogeography based Satellite Image Classification | cs.CV | Biogeography is the study of the geographical distribution of biological
organisms. The mindset of the engineer is that we can learn from nature.
Biogeography Based Optimization is a burgeoning nature inspired technique to
find the optimal solution of the problem. Satellite image classification is an
important task bec... | computer science |
8,044 | Gesture Recognition with a Focus on Important Actions by Using a Path
Searching Method in Weighted Graph | cs.CV | This paper proposes a method of gesture recognition with a focus on important
actions for distinguishing similar gestures. The method generates a partial
action sequence by using optical flow images, expresses the sequence in the
eigenspace, and checks the feature vector sequence by applying an optimum
path-searching m... | computer science |
8,045 | Synthesis of supervised classification algorithm using intelligent and
statistical tools | cs.CV | A fundamental task in detecting foreground objects in both static and dynamic
scenes is to take the best choice of color system representation and the
efficient technique for background modeling. We propose in this paper a
non-parametric algorithm dedicated to segment and to detect objects in color
images issued from a... | computer science |
8,046 | Fusion of Multiple Matchers using SVM for Offline Signature
Identification | cs.CV | This paper uses Support Vector Machines (SVM) to fuse multiple classifiers
for an offline signature system. From the signature images, global and local
features are extracted and the signatures are verified with the help of
Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers.
SVM is used to fu... | computer science |
8,047 | Intrinsic dimension estimation of data by principal component analysis | cs.CV | Estimating intrinsic dimensionality of data is a classic problem in pattern
recognition and statistics. Principal Component Analysis (PCA) is a powerful
tool in discovering dimensionality of data sets with a linear structure; it,
however, becomes ineffective when data have a nonlinear structure. In this
paper, we propo... | computer science |
8,048 | Handwritten Bangla Basic and Compound character recognition using MLP
and SVM classifier | cs.CV | A novel approach for recognition of handwritten compound Bangla characters,
along with the Basic characters of Bangla alphabet, is presented here. Compared
to English like Roman script, one of the major stumbling blocks in Optical
Character Recognition (OCR) of handwritten Bangla script is the large number of
complex s... | computer science |
8,049 | Supervised Classification Performance of Multispectral Images | cs.LG | Nowadays government and private agencies use remote sensing imagery for a
wide range of applications from military applications to farm development. The
images may be a panchromatic, multispectral, hyperspectral or even
ultraspectral of terra bytes. Remote sensing image classification is one
amongst the most significan... | computer science |
8,050 | Facial Expression Representation and Recognition Using 2DHLDA, Gabor
Wavelets, and Ensemble Learning | cs.CV | In this paper, a novel method for representation and recognition of the
facial expressions in two-dimensional image sequences is presented. We apply a
variation of two-dimensional heteroscedastic linear discriminant analysis
(2DHLDA) algorithm, as an efficient dimensionality reduction technique, to
Gabor representation... | computer science |
8,051 | Recognizing Combinations of Facial Action Units with Different Intensity
Using a Mixture of Hidden Markov Models and Neural Network | cs.CV | Facial Action Coding System consists of 44 action units (AUs) and more than
7000 combinations. Hidden Markov models (HMMs) classifier has been used
successfully to recognize facial action units (AUs) and expressions due to its
ability to deal with AU dynamics. However, a separate HMM is necessary for each
single AU and... | computer science |
8,052 | Multilinear Biased Discriminant Analysis: A Novel Method for Facial
Action Unit Representation | cs.CV | In this paper a novel efficient method for representation of facial action
units by encoding an image sequence as a fourth-order tensor is presented. The
multilinear tensor-based extension of the biased discriminant analysis (BDA)
algorithm, called multilinear biased discriminant analysis (MBDA), is first
proposed. The... | computer science |
8,053 | Extended Two-Dimensional PCA for Efficient Face Representation and
Recognition | cs.CV | In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is
proposed which is an extension to the original 2DPCA. We state that the
covariance matrix of 2DPCA is equivalent to the average of the main diagonal of
the covariance matrix of PCA. This implies that 2DPCA eliminates some
covariance informatio... | computer science |
8,054 | A new embedding quality assessment method for manifold learning | cs.CV | Manifold learning is a hot research topic in the field of computer science. A
crucial issue with current manifold learning methods is that they lack a
natural quantitative measure to assess the quality of learned embeddings, which
greatly limits their applications to real-world problems. In this paper, a new
embedding ... | computer science |
8,055 | Learning image transformations without training examples | cs.LG | The use of image transformations is essential for efficient modeling and
learning of visual data. But the class of relevant transformations is large:
affine transformations, projective transformations, elastic deformations, ...
the list goes on. Therefore, learning these transformations, rather than hand
coding them, i... | computer science |
8,056 | Dictionary Learning for Deblurring and Digital Zoom | cs.LG | This paper proposes a novel approach to image deblurring and digital zooming
using sparse local models of image appearance. These models, where small image
patches are represented as linear combinations of a few elements drawn from
some large set (dictionary) of candidates, have proven well adapted to several
image res... | computer science |
8,057 | Robust Image Analysis by L1-Norm Semi-supervised Learning | cs.CV | This paper presents a novel L1-norm semi-supervised learning algorithm for
robust image analysis by giving new L1-norm formulation of Laplacian
regularization which is the key step of graph-based semi-supervised learning.
Since our L1-norm Laplacian regularization is defined directly over the
eigenvectors of the normal... | computer science |
8,058 | Autonomous Cleaning of Corrupted Scanned Documents - A Generative
Modeling Approach | cs.CV | We study the task of cleaning scanned text documents that are strongly
corrupted by dirt such as manual line strokes, spilled ink etc. We aim at
autonomously removing dirt from a single letter-size page based only on the
information the page contains. Our approach, therefore, has to learn character
representations with... | computer science |
8,059 | Learning Random Kernel Approximations for Object Recognition | cs.CV | Approximations based on random Fourier features have recently emerged as an
efficient and formally consistent methodology to design large-scale kernel
machines. By expressing the kernel as a Fourier expansion, features are
generated based on a finite set of random basis projections, sampled from the
Fourier transform o... | computer science |
8,060 | A New Fuzzy Stacked Generalization Technique and Analysis of its
Performance | cs.LG | In this study, a new Stacked Generalization technique called Fuzzy Stacked
Generalization (FSG) is proposed to minimize the difference between N -sample
and large-sample classification error of the Nearest Neighbor classifier. The
proposed FSG employs a new hierarchical distance learning strategy to minimize
the error ... | computer science |
8,061 | Analysis Operator Learning and Its Application to Image Reconstruction | cs.LG | Exploiting a priori known structural information lies at the core of many
image reconstruction methods that can be stated as inverse problems. The
synthesis model, which assumes that images can be decomposed into a linear
combination of very few atoms of some dictionary, is now a well established
tool for the design of... | computer science |
8,062 | Brain tumor MRI image classification with feature selection and
extraction using linear discriminant analysis | cs.CV | Feature extraction is a method of capturing visual content of an image. The
feature extraction is the process to represent raw image in its reduced form to
facilitate decision making such as pattern classification. We have tried to
address the problem of classification MRI brain images by creating a robust and
more acc... | computer science |
8,063 | A Comparative Study of Efficient Initialization Methods for the K-Means
Clustering Algorithm | cs.LG | K-means is undoubtedly the most widely used partitional clustering algorithm.
Unfortunately, due to its gradient descent nature, this algorithm is highly
sensitive to the initial placement of the cluster centers. Numerous
initialization methods have been proposed to address this problem. In this
paper, we first present... | computer science |
8,064 | A Complete System for Candidate Polyps Detection in Virtual Colonoscopy | cs.CV | Computer tomographic colonography, combined with computer-aided detection, is
a promising emerging technique for colonic polyp analysis. We present a
complete pipeline for polyp detection, starting with a simple colon
segmentation technique that enhances polyps, followed by an adaptive-scale
candidate polyp delineation... | computer science |
8,065 | Learning Graphical Model Parameters with Approximate Marginal Inference | cs.LG | Likelihood based-learning of graphical models faces challenges of
computational-complexity and robustness to model mis-specification. This paper
studies methods that fit parameters directly to maximize a measure of the
accuracy of predicted marginals, taking into account both model and inference
approximations at train... | computer science |
8,066 | Auto-pooling: Learning to Improve Invariance of Image Features from
Image Sequences | cs.CV | Learning invariant representations from images is one of the hardest
challenges facing computer vision. Spatial pooling is widely used to create
invariance to spatial shifting, but it is restricted to convolutional models.
In this paper, we propose a novel pooling method that can learn soft clustering
of features from ... | computer science |
8,067 | Learnable Pooling Regions for Image Classification | cs.CV | Biologically inspired, from the early HMAX model to Spatial Pyramid Matching,
pooling has played an important role in visual recognition pipelines. Spatial
pooling, by grouping of local codes, equips these methods with a certain degree
of robustness to translation and deformation yet preserving important spatial
inform... | computer science |
8,068 | Information Theoretic Learning with Infinitely Divisible Kernels | cs.LG | In this paper, we develop a framework for information theoretic learning
based on infinitely divisible matrices. We formulate an entropy-like functional
on positive definite matrices based on Renyi's axiomatic definition of entropy
and examine some key properties of this functional that lead to the concept of
infinite ... | computer science |
8,069 | Big Neural Networks Waste Capacity | cs.LG | This article exposes the failure of some big neural networks to leverage
added capacity to reduce underfitting. Past research suggest diminishing
returns when increasing the size of neural networks. Our experiments on
ImageNet LSVRC-2010 show that this may be due to the fact there are highly
diminishing returns for cap... | computer science |
8,070 | Regularized Discriminant Embedding for Visual Descriptor Learning | cs.CV | Images can vary according to changes in viewpoint, resolution, noise, and
illumination. In this paper, we aim to learn representations for an image,
which are robust to wide changes in such environmental conditions, using
training pairs of matching and non-matching local image patches that are
collected under various e... | computer science |
8,071 | Zero-Shot Learning Through Cross-Modal Transfer | cs.CV | This work introduces a model that can recognize objects in images even if no
training data is available for the objects. The only necessary knowledge about
the unseen categories comes from unsupervised large text corpora. In our
zero-shot framework distributional information in language can be seen as
spanning a semant... | computer science |
8,072 | Discriminative Recurrent Sparse Auto-Encoders | cs.LG | We present the discriminative recurrent sparse auto-encoder model, comprising
a recurrent encoder of rectified linear units, unrolled for a fixed number of
iterations, and connected to two linear decoders that reconstruct the input and
predict its supervised classification. Training via
backpropagation-through-time ini... | computer science |
8,073 | Why Size Matters: Feature Coding as Nystrom Sampling | cs.LG | Recently, the computer vision and machine learning community has been in
favor of feature extraction pipelines that rely on a coding step followed by a
linear classifier, due to their overall simplicity, well understood properties
of linear classifiers, and their computational efficiency. In this paper we
propose a nov... | computer science |
8,074 | A Fast Semidefinite Approach to Solving Binary Quadratic Problems | cs.CV | Many computer vision problems can be formulated as binary quadratic programs
(BQPs). Two classic relaxation methods are widely used for solving BQPs,
namely, spectral methods and semidefinite programming (SDP), each with their
own advantages and disadvantages. Spectral relaxation is simple and easy to
implement, but it... | computer science |
8,075 | Kernel Reconstruction ICA for Sparse Representation | cs.CV | Independent Component Analysis (ICA) is an effective unsupervised tool to
learn statistically independent representation. However, ICA is not only
sensitive to whitening but also difficult to learn an over-complete basis.
Consequently, ICA with soft Reconstruction cost(RICA) was presented to learn
sparse representation... | computer science |
8,076 | Bayesian crack detection in ultra high resolution multimodal images of
paintings | cs.CV | The preservation of our cultural heritage is of paramount importance. Thanks
to recent developments in digital acquisition techniques, powerful image
analysis algorithms are developed which can be useful non-invasive tools to
assist in the restoration and preservation of art. In this paper we propose a
semi-supervised ... | computer science |
8,077 | Deterministic Initialization of the K-Means Algorithm Using Hierarchical
Clustering | cs.LG | K-means is undoubtedly the most widely used partitional clustering algorithm.
Unfortunately, due to its gradient descent nature, this algorithm is highly
sensitive to the initial placement of the cluster centers. Numerous
initialization methods have been proposed to address this problem. Many of
these methods, however,... | computer science |
8,078 | Discriminative Parameter Estimation for Random Walks Segmentation:
Technical Report | cs.CV | The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use
probabilistic segmentation methods. By combining contrast terms with prior
terms, it provides accurate segmentations of medical images in a fully
automated manner. However, one of the main drawbacks of using the RW algorithm
is that its parame... | computer science |
8,079 | Faster and better: a machine learning approach to corner detection | cs.CV | The repeatability and efficiency of a corner detector determines how likely
it is to be useful in a real-world application. The repeatability is importand
because the same scene viewed from different positions should yield features
which correspond to the same real-world 3D locations [Schmid et al 2000]. The
efficiency... | computer science |
8,080 | Median K-flats for hybrid linear modeling with many outliers | cs.CV | We describe the Median K-Flats (MKF) algorithm, a simple online method for
hybrid linear modeling, i.e., for approximating data by a mixture of flats.
This algorithm simultaneously partitions the data into clusters while finding
their corresponding best approximating l1 d-flats, so that the cumulative l1
error is minim... | computer science |
8,081 | Extension of Path Probability Method to Approximate Inference over Time | cs.LG | There has been a tremendous growth in publicly available digital video
footage over the past decade. This has necessitated the development of new
techniques in computer vision geared towards efficient analysis, storage and
retrieval of such data. Many mid-level computer vision tasks such as
segmentation, object detecti... | computer science |
8,082 | Modelling Distributed Shape Priors by Gibbs Random Fields of Second
Order | cs.CV | We analyse the potential of Gibbs Random Fields for shape prior modelling. We
show that the expressive power of second order GRFs is already sufficient to
express simple shapes and spatial relations between them simultaneously. This
allows to model and recognise complex shapes as spatial compositions of simpler
parts. | computer science |
8,083 | Weakly Supervised Learning of Foreground-Background Segmentation using
Masked RBMs | cs.LG | We propose an extension of the Restricted Boltzmann Machine (RBM) that allows
the joint shape and appearance of foreground objects in cluttered images to be
modeled independently of the background. We present a learning scheme that
learns this representation directly from cluttered images with only very weak
supervisio... | computer science |
8,084 | Spatial-Aware Dictionary Learning for Hyperspectral Image Classification | cs.CV | This paper presents a structured dictionary-based model for hyperspectral
data that incorporates both spectral and contextual characteristics of a
spectral sample, with the goal of hyperspectral image classification. The idea
is to partition the pixels of a hyperspectral image into a number of spatial
neighborhoods cal... | computer science |
8,085 | Discriminative Parameter Estimation for Random Walks Segmentation | cs.CV | The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use
probabilistic segmentation methods. By combining contrast terms with prior
terms, it provides accurate segmentations of medical images in a fully
automated manner. However, one of the main drawbacks of using the RW algorithm
is that its parame... | computer science |
8,086 | A kernel for time series based on global alignments | cs.CV | We propose in this paper a new family of kernels to handle times series,
notably speech data, within the framework of kernel methods which includes
popular algorithms such as the Support Vector Machine. These kernels elaborate
on the well known Dynamic Time Warping (DTW) family of distances by considering
the same set ... | computer science |
8,087 | Feature Selection By KDDA For SVM-Based MultiView Face Recognition | cs.CV | Applications such as face recognition that deal with high-dimensional data
need a mapping technique that introduces representation of low-dimensional
features with enhanced discriminatory power and a proper classifier, able to
classify those complex features. Most of traditional Linear Discriminant
Analysis suffer from... | computer science |
8,088 | Face Detection Using Adaboosted SVM-Based Component Classifier | cs.CV | Recently, Adaboost has been widely used to improve the accuracy of any given
learning algorithm. In this paper we focus on designing an algorithm to employ
combination of Adaboost with Support Vector Machine as weak component
classifiers to be used in Face Detection Task. To obtain a set of effective
SVM-weaklearner Cl... | computer science |
8,089 | Adaboost with "Keypoint Presence Features" for Real-Time Vehicle Visual
Detection | cs.CV | We present promising results for real-time vehicle visual detection, obtained
with adaBoost using new original ?keypoints presence features?. These
weak-classifiers produce a boolean response based on presence or absence in the
tested image of a ?keypoint? (~ a SURF interest point) with a descriptor
sufficiently simila... | computer science |
8,090 | Introducing New AdaBoost Features for Real-Time Vehicle Detection | cs.CV | This paper shows how to improve the real-time object detection in complex
robotics applications, by exploring new visual features as AdaBoost weak
classifiers. These new features are symmetric Haar filters (enforcing global
horizontal and vertical symmetry) and N-connexity control points. Experimental
evaluation on a c... | computer science |
8,091 | Visual object categorization with new keypoint-based adaBoost features | cs.CV | We present promising results for visual object categorization, obtained with
adaBoost using new original ?keypoints-based features?. These weak-classifiers
produce a boolean response based on presence or absence in the tested image of
a ?keypoint? (a kind of SURF interest point) with a descriptor sufficiently
similar (... | computer science |
8,092 | Local and global approaches of affinity propagation clustering for large
scale data | cs.LG | Recently a new clustering algorithm called 'affinity propagation' (AP) has
been proposed, which efficiently clustered sparsely related data by passing
messages between data points. However, we want to cluster large scale data
where the similarities are not sparse in many cases. This paper presents two
variants of AP fo... | computer science |
8,093 | Positive Semidefinite Metric Learning with Boosting | cs.CV | The learning of appropriate distance metrics is a critical problem in image
classification and retrieval. In this work, we propose a boosting-based
technique, termed \BoostMetric, for learning a Mahalanobis distance metric. One
of the primary difficulties in learning such a metric is to ensure that the
Mahalanobis matr... | computer science |
8,094 | An Unsupervised Algorithm For Learning Lie Group Transformations | cs.CV | We present several theoretical contributions which allow Lie groups to be fit
to high dimensional datasets. Transformation operators are represented in their
eigen-basis, reducing the computational complexity of parameter estimation to
that of training a linear transformation model. A transformation specific
"blurring"... | computer science |
8,095 | An Explicit Nonlinear Mapping for Manifold Learning | cs.CV | Manifold learning is a hot research topic in the field of computer science
and has many applications in the real world. A main drawback of manifold
learning methods is, however, that there is no explicit mappings from the input
data manifold to the output embedding. This prohibits the application of
manifold learning m... | computer science |
8,096 | SVM-based Multiview Face Recognition by Generalization of Discriminant
Analysis | cs.CV | Identity verification of authentic persons by their multiview faces is a real
valued problem in machine vision. Multiview faces are having difficulties due
to non-linear representation in the feature space. This paper illustrates the
usability of the generalization of LDA in the form of canonical covariate for
face rec... | computer science |
8,097 | Fast Inference in Sparse Coding Algorithms with Applications to Object
Recognition | cs.CV | Adaptive sparse coding methods learn a possibly overcomplete set of basis
functions, such that natural image patches can be reconstructed by linearly
combining a small subset of these bases. The applicability of these methods to
visual object recognition tasks has been limited because of the prohibitive
cost of the opt... | computer science |
8,098 | Local Component Analysis for Nonparametric Bayes Classifier | cs.CV | The decision boundaries of Bayes classifier are optimal because they lead to
maximum probability of correct decision. It means if we knew the prior
probabilities and the class-conditional densities, we could design a classifier
which gives the lowest probability of error. However, in classification based
on nonparametr... | computer science |
8,099 | Support vector machines/relevance vector machine for remote sensing
classification: A review | cs.CV | Kernel-based machine learning algorithms are based on mapping data from the
original input feature space to a kernel feature space of higher dimensionality
to solve a linear problem in that space. Over the last decade, kernel based
classification and regression approaches such as support vector machines have
widely bee... | computer science |
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