pwc-archive/papers-with-abstracts huggingface dataset:
Xception: Deep Learning with Depthwise Separable Convolutions
Authors: François Chollet
Date: 2016-10-07 00:00:00
Proceeding: CVPR 2017
Tasks: Classification, Deep Learning, image-classification, Image Classification, Session-Based Recommendations

We present an interpretation of Inception modules in convolutional neural
networks as being an intermediate step in-between regular convolution and the
depthwise separable convolution operation (a depthwise convolution followed by
a pointwise convolution). In this light, a depthwise separable convolution can
be understood as an Inception module with a maximally large number of towers.
This observation leads us to propose a novel deep convolutional neural network
architecture inspired by Inception, where Inception modules have been replaced
with depthwise separable convolutions. We show that this architecture, dubbed
Xception, slightly outperforms Inception V3 on the ImageNet dataset (which
Inception V3 was designed for), and significantly outperforms Inception V3 on a
larger image classification dataset comprising 350 million images and 17,000
classes. Since the Xception architecture has the same number of parameters as
Inception V3, the performance gains are not due to increased capacity but
rather to a more efficient use of model parameters.
Open Vocabulary Scene Parsing
Authors: Hang Zhao, Xavier Puig, Bolei Zhou, Sanja Fidler, Antonio Torralba
Date: 2017-03-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: General Classification, Scene Parsing

Recognizing arbitrary objects in the wild has been a challenging problem due
to the limitations of existing classification models and datasets. In this
paper, we propose a new task that aims at parsing scenes with a large and open
vocabulary, and several evaluation metrics are explored for this problem. Our
proposed approach to this problem is a joint image pixel and word concept
embeddings framework, where word concepts are connected by semantic relations.
We validate the open vocabulary prediction ability of our framework on ADE20K
dataset which covers a wide variety of scenes and objects. We further explore
the trained joint embedding space to show its interpretability.
DyVEDeep: Dynamic Variable Effort Deep Neural Networks
Authors: Sanjay Ganapathy, Swagath Venkataramani, Balaraman Ravindran, Anand Raghunathan
Date: 2017-04-04 00:00:00

Deep Neural Networks (DNNs) have advanced the state-of-the-art in a variety
of machine learning tasks and are deployed in increasing numbers of products
and services. However, the computational requirements of training and
evaluating large-scale DNNs are growing at a much faster pace than the
capabilities of the underlying hardware platforms that they are executed upon.
In this work, we propose Dynamic Variable Effort Deep Neural Networks
(DyVEDeep) to reduce the computational requirements of DNNs during inference.
Previous efforts propose specialized hardware implementations for DNNs,
statically prune the network, or compress the weights. Complementary to these
approaches, DyVEDeep is a dynamic approach that exploits the heterogeneity in
the inputs to DNNs to improve their compute efficiency with comparable
classification accuracy. DyVEDeep equips DNNs with dynamic effort mechanisms
that, in the course of processing an input, identify how critical a group of
computations are to classify the input. DyVEDeep dynamically focuses its
compute effort only on the critical computa- tions, while skipping or
approximating the rest. We propose 3 effort knobs that operate at different
levels of granularity viz. neuron, feature and layer levels. We build DyVEDeep
versions for 5 popular image recognition benchmarks - one for CIFAR-10 and four
for ImageNet (AlexNet, OverFeat and VGG-16, weight-compressed AlexNet). Across
all benchmarks, DyVEDeep achieves 2.1x-2.6x reduction in the number of scalar
operations, which translates to 1.8x-2.3x performance improvement over a
Caffe-based implementation, with < 0.5% loss in accuracy.
Satellite Image-based Localization via Learned Embeddings
Authors: Dong-Ki Kim, Matthew R. Walter
Date: 2017-04-04 00:00:00
Tasks: Image-Based Localization

We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment. Our approach takes as input a sequence of ground-level images acquired by the vehicle as it navigates, and outputs an estimate of the vehicle's pose relative to a georeferenced satellite image. We overcome the significant viewpoint and appearance variations between the images through a neural multi-view model that learns location-discriminative embeddings in which ground-level images are matched with their corresponding satellite view of the scene. We use this learned function as an observation model in a filtering framework to maintain a distribution over the vehicle's pose. We evaluate our method on different benchmark datasets and demonstrate its ability localize ground-level images in environments novel relative to training, despite the challenges of significant viewpoint and appearance variations.
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning
Authors: Yi Zhu, Shawn Newsam
Date: 2016-12-22 00:00:00
Tasks: Action Detection, Action Localization, Action Recognition, Multi-Task Learning, Retrieval, Temporal Action Localization, Temporal Localization, Video Retrieval

This paper studies the joint learning of action recognition and temporal
localization in long, untrimmed videos. We employ a multi-task learning
framework that performs the three highly related steps of action proposal,
action recognition, and action localization refinement in parallel instead of
the standard sequential pipeline that performs the steps in order. We develop a
novel temporal actionness regression module that estimates what proportion of a
clip contains action. We use it for temporal localization but it could have
other applications like video retrieval, surveillance, summarization, etc. We
also introduce random shear augmentation during training to simulate viewpoint
change. We evaluate our framework on three popular video benchmarks. Results
demonstrate that our joint model is efficient in terms of storage and
computation in that we do not need to compute and cache dense trajectory
features, and that it is several times faster than its sequential ConvNets
counterpart. Yet, despite being more efficient, it outperforms state-of-the-art
methods with respect to accuracy.
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting
Authors: Zhen-Hua Feng, Josef Kittler, William Christmas, Patrik Huber, Xiao-Jun Wu
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Data Augmentation, Face Alignment, Facial Landmark Detection, Model Selection, regression

We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic
Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on
unconstrained faces. Our DAC-CSR divides facial landmark detection into three
cascaded sub-tasks: face bounding box refinement, general CSR and
attention-controlled CSR. The first two stages refine initial face bounding
boxes and output intermediate facial landmarks. Then, an online dynamic model
selection method is used to choose appropriate domain-specific CSRs for further
landmark refinement. The key innovation of our DAC-CSR is the fault-tolerant
mechanism, using fuzzy set sample weighting for attention-controlled
domain-specific model training. Moreover, we advocate data augmentation with a
simple but effective 2D profile face generator, and context-aware feature
extraction for better facial feature representation. Experimental results
obtained on challenging datasets demonstrate the merits of our DAC-CSR over the
state-of-the-art.
A note on the expected minimum error probability in equientropic channels
Authors: Sebastian Weichwald, Tatiana Fomina, Bernhard Schölkopf, Moritz Grosse-Wentrup
Date: 2016-05-23 00:00:00

While the channel capacity reflects a theoretical upper bound on the
achievable information transmission rate in the limit of infinitely many bits,
it does not characterise the information transfer of a given encoding routine
with finitely many bits. In this note, we characterise the quality of a code
(i. e. a given encoding routine) by an upper bound on the expected minimum
error probability that can be achieved when using this code. We show that for
equientropic channels this upper bound is minimal for codes with maximal
marginal entropy. As an instructive example we show for the additive white
Gaussian noise (AWGN) channel that random coding---also a capacity achieving
code---indeed maximises the marginal entropy in the limit of infinite messages.
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Authors: Feras Saad, Leonardo Casarsa, Vikash Mansinghka
Date: 2017-04-04 00:00:00
Tasks: Information Retrieval, Probabilistic Programming, Retrieval

Databases are widespread, yet extracting relevant data can be difficult.
Without substantial domain knowledge, multivariate search queries often return
sparse or uninformative results. This paper introduces an approach for
searching structured data based on probabilistic programming and nonparametric
Bayes. Users specify queries in a probabilistic language that combines standard
SQL database search operators with an information theoretic ranking function
called predictive relevance. Predictive relevance can be calculated by a fast
sparse matrix algorithm based on posterior samples from CrossCat, a
nonparametric Bayesian model for high-dimensional, heterogeneously-typed data
tables. The result is a flexible search technique that applies to a broad class
of information retrieval problems, which we integrate into BayesDB, a
probabilistic programming platform for probabilistic data analysis. This paper
demonstrates applications to databases of US colleges, global macroeconomic
indicators of public health, and classic cars. We found that human evaluators
often prefer the results from probabilistic search to results from a standard
baseline.
Using Echo State Networks for Cryptography
Authors: Rajkumar Ramamurthy, Christian Bauckhage, Krisztian Buza, Stefan Wrobel
Date: 2017-04-04 00:00:00

Echo state networks are simple recurrent neural networks that are easy to
implement and train. Despite their simplicity, they show a form of memory and
can predict or regenerate sequences of data. We make use of this property to
realize a novel neural cryptography scheme. The key idea is to assume that
Alice and Bob share a copy of an echo state network. If Alice trains her copy
to memorize a message, she can communicate the trained part of the network to
Bob who plugs it into his copy to regenerate the message. Considering a
byte-level representation of in- and output, the technique applies to arbitrary
types of data (texts, images, audio files, etc.) and practical experiments
reveal it to satisfy the fundamental cryptographic properties of diffusion and
confusion.
On The Stability of Video Detection and Tracking
Authors: Hong Zhang, Naiyan Wang
Date: 2016-11-20 00:00:00

In this paper, we study an important yet less explored aspect in video
detection and tracking -- stability. Surprisingly, there is no prior work that
tried to study it. As a result, we start our work by proposing a novel
evaluation metric for video detection which considers both stability and
accuracy. For accuracy, we extend the existing accuracy metric mean Average
Precision (mAP). For stability, we decompose it into three terms: fragment
error, center position error, scale and ratio error. Each error represents one
aspect of stability. Furthermore, we demonstrate that the stability metric has
low correlation with accuracy metric. Thus, it indeed captures a different
perspective of quality. Lastly, based on this metric, we evaluate several
existing methods for video detection and show how they affect accuracy and
stability. We believe our work can provide guidance and solid baselines for
future researches in the related areas.
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods
Authors: Yan Shuo Tan, Roman Vershynin
Date: 2017-04-04 00:00:00

The problem of Non-Gaussian Component Analysis (NGCA) is about finding a
maximal low-dimensional subspace $E$ in $\mathbb{R}^n$ so that data points
projected onto $E$ follow a non-gaussian distribution. Although this is an
appropriate model for some real world data analysis problems, there has been
little progress on this problem over the last decade.
  In this paper, we attempt to address this state of affairs in two ways.
First, we give a new characterization of standard gaussian distributions in
high-dimensions, which lead to effective tests for non-gaussianness. Second, we
propose a simple algorithm, \emph{Reweighted PCA}, as a method for solving the
NGCA problem. We prove that for a general unknown non-gaussian distribution,
this algorithm recovers at least one direction in $E$, with sample and time
complexity depending polynomially on the dimension of the ambient space. We
conjecture that the algorithm actually recovers the entire $E$.
On the Use of Deep Learning for Blind Image Quality Assessment
Authors: Simone Bianco, Luigi Celona, Paolo Napoletano, Raimondo Schettini
Date: 2016-02-17 00:00:00
Tasks: Blind Image Quality Assessment, Image Description, Image Quality Assessment

In this work we investigate the use of deep learning for distortion-generic
blind image quality assessment. We report on different design choices, ranging
from the use of features extracted from pre-trained Convolutional Neural
Networks (CNNs) as a generic image description, to the use of features
extracted from a CNN fine-tuned for the image quality task. Our best proposal,
named DeepBIQ, estimates the image quality by average pooling the scores
predicted on multiple sub-regions of the original image. The score of each
sub-region is computed using a Support Vector Regression (SVR) machine taking
as input features extracted using a CNN fine-tuned for category-based image
quality assessment. Experimental results on the LIVE In the Wild Image Quality
Challenge Database and on the LIVE Image Quality Assessment Database show that
DeepBIQ outperforms the state-of-the-art methods compared, having a Linear
Correlation Coefficient (LCC) with human subjective scores of almost 0.91 and
0.98 respectively. Furthermore, in most of the cases, the quality score
predictions of DeepBIQ are closer to the average observer than those of a
generic human observer.
A Brain-like Cognitive Process with Shared Methods
Authors: Kieran Greer
Date: 2015-07-17 00:00:00
Tasks: Clustering

This paper describes a new entropy-style of equation that may be useful in a
general sense, but can be applied to a cognitive model with related processes.
The model is based on the human brain, with automatic and distributed pattern
activity. Methods for carrying out the different processes are suggested. The
main purpose of this paper is to reaffirm earlier research on different
knowledge-based and experience-based clustering techniques. The overall
architecture has stayed essentially the same and so it is the localised
processes or smaller details that have been updated. For example, a counting
mechanism is used slightly differently, to measure a level of 'cohesion'
instead of a 'correct' classification, over pattern instances. The introduction
of features has further enhanced the architecture and the new entropy-style
equation is proposed. While an earlier paper defined three levels of functional
requirement, this paper re-defines the levels in a more human vernacular, with
higher-level goals described in terms of action-result pairs.
Feature Selection for Regression Problems Based on the Morisita Estimator of Intrinsic Dimension
Authors: Jean Golay, Michael Leuenberger, Mikhail Kanevski
Date: 2016-01-31 00:00:00
Tasks: feature selection, Management, regression

Data acquisition, storage and management have been improved, while the key
factors of many phenomena are not well known. Consequently, irrelevant and
redundant features artificially increase the size of datasets, which
complicates learning tasks, such as regression. To address this problem,
feature selection methods have been proposed. This paper introduces a new
supervised filter based on the Morisita estimator of intrinsic dimension. It
can identify relevant features and distinguish between redundant and irrelevant
information. Besides, it offers a clear graphical representation of the
results, and it can be easily implemented in different programming languages.
Comprehensive numerical experiments are conducted using simulated datasets
characterized by different levels of complexity, sample size and noise. The
suggested algorithm is also successfully tested on a selection of real world
applications and compared with RReliefF using extreme learning machine. In
addition, a new measure of feature relevance is presented and discussed.
Multi-Label Learning with Global and Local Label Correlation
Authors: Yue Zhu, James T. Kwok, Zhi-Hua Zhou
Date: 2017-04-04 00:00:00
Tasks: Multi-Label Learning

It is well-known that exploiting label correlations is important to
multi-label learning. Existing approaches either assume that the label
correlations are global and shared by all instances; or that the label
correlations are local and shared only by a data subset. In fact, in the
real-world applications, both cases may occur that some label correlations are
globally applicable and some are shared only in a local group of instances.
Moreover, it is also a usual case that only partial labels are observed, which
makes the exploitation of the label correlations much more difficult. That is,
it is hard to estimate the label correlations when many labels are absent. In
this paper, we propose a new multi-label approach GLOCAL dealing with both the
full-label and the missing-label cases, exploiting global and local label
correlations simultaneously, through learning a latent label representation and
optimizing label manifolds. The extensive experimental studies validate the
effectiveness of our approach on both full-label and missing-label data.
Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling
Authors: Ridho Rahmadi, Perry Groot, Marieke HC van Rijn, Jan AJG van den Brand, Marianne Heins, Hans Knoop, Tom Heskes
Date: 2016-05-22 00:00:00

A typical problem in causal modeling is the instability of model structure
learning, i.e., small changes in finite data can result in completely different
optimal models. The present work introduces a novel causal modeling algorithm
for longitudinal data, that is robust for finite samples based on recent
advances in stability selection using subsampling and selection algorithms. Our
approach uses exploratory search but allows incorporation of prior knowledge,
e.g., the absence of a particular causal relationship between two specific
variables. We represent causal relationships using structural equation models.
Models are scored along two objectives: the model fit and the model complexity.
Since both objectives are often conflicting we apply a multi-objective
evolutionary algorithm to search for Pareto optimal models. To handle the
instability of small finite data samples, we repeatedly subsample the data and
select those substructures (from the optimal models) that are both stable and
parsimonious. These substructures can be visualized through a causal graph. Our
more exploratory approach achieves at least comparable performance as, but
often a significant improvement over state-of-the-art alternative approaches on
a simulated data set with a known ground truth. We also present the results of
our method on three real-world longitudinal data sets on chronic fatigue
syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained
with our approach are generally in line with results from more
hypothesis-driven analyses in earlier studies and suggest some novel
relationships that deserve further research.
Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network
Authors: Artem Sevastopolsky
Date: 2017-04-04 00:00:00
Tasks: Segmentation

Glaucoma is the second leading cause of blindness all over the world, with
approximately 60 million cases reported worldwide in 2010. If undiagnosed in
time, glaucoma causes irreversible damage to the optic nerve leading to
blindness. The optic nerve head examination, which involves measurement of
cup-to-disc ratio, is considered one of the most valuable methods of structural
diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation
of optic disc and optic cup on eye fundus images and can be performed by modern
computer vision algorithms. This work presents universal approach for automatic
optic disc and cup segmentation, which is based on deep learning, namely,
modification of U-Net convolutional neural network. Our experiments include
comparison with the best known methods on publicly available databases
DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation,
our method achieves quality comparable to current state-of-the-art methods,
outperforming them in terms of the prediction time.
From Modal to Multimodal Ambiguities: a Classification Approach
Authors: Maria Chiara Caschera, Fernando Ferri, Patrizia Grifoni
Date: 2017-04-04 00:00:00
Tasks: Classification, General Classification, multimodal interaction

This paper deals with classifying ambiguities for Multimodal Languages. It
evolves the classifications and the methods of the literature on ambiguities
for Natural Language and Visual Language, empirically defining an original
classification of ambiguities for multimodal interaction using a linguistic
perspective. This classification distinguishes between Semantic and Syntactic
multimodal ambiguities and their subclasses, which are intercepted using a
rule-based method implemented in a software module. The experimental results
have achieved an accuracy of the obtained classification compared to the
expected one, which are defined by the human judgment, of 94.6% for the
semantic ambiguities classes, and 92.1% for the syntactic ambiguities classes.
Geracao Automatica de Paineis de Controle para Analise de Mobilidade Urbana Utilizando Redes Complexas
Authors: Victor Dantas, Henrique Santos, Carlos Caminha, Vasco Furtado
Date: 2017-04-04 00:00:00

In this paper we describe an automatic generator to support the data
scientist to construct, in a user-friendly way, dashboards from data
represented as networks. The generator called SBINet (Semantic for Business
Intelligence from Networks) has a semantic layer that, through ontologies,
describes the data that represents a network as well as the possible metrics to
be calculated in the network. Thus, with SBINet, the stages of the dashboard
constructing process that uses complex network metrics are facilitated and can
be done by users who do not necessarily know about complex networks.
Universal probability-free prediction
Authors: Vladimir Vovk, Dusko Pavlovic
Date: 2016-03-14 00:00:00
Tasks: Conformal Prediction, Prediction

We construct universal prediction systems in the spirit of Popper's
falsifiability and Kolmogorov complexity and randomness. These prediction
systems do not depend on any statistical assumptions (but under the IID
assumption they dominate, to within the usual accuracy, conformal prediction).
Our constructions give rise to a theory of algorithmic complexity and
randomness of time containing analogues of several notions and results of the
classical theory of Kolmogorov complexity and randomness.