pwc-archive/papers-with-abstracts huggingface dataset:
Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-pixel Quantification
Authors: Lukas Drees, Ribana Roscher, Susanne Wenzel
Date: 2018-02-08 00:00:00

The estimation of land cover fractions from remote sensing images is a
frequently used indicator of the environmental quality. This paper focuses on
the quantification of land cover fractions in an urban area of Berlin, Germany,
using simulated hyperspectral EnMAP data with a spatial resolution of
30m$\times$30m. We use constrained sparse representation, where each pixel with
unknown surface characteristics is expressed by a weighted linear combination
of elementary spectra with known land cover class. We automatically determine
the elementary spectra from image reference data using archetypal analysis by
simplex volume maximization, and combine it with reversible jump Markov chain
Monte Carlo method. In our experiments, the estimation of the automatically
derived elementary spectra is compared to the estimation obtained by a manually
designed spectral library by means of reconstruction error, mean absolute error
of the fraction estimates, sum of fractions, $R^2$, and the number of used
elementary spectra. The experiments show that a collection of archetypes can be
an adequate and efficient alternative to the manually designed spectral library
with respect to the mentioned criteria.
Transductive Adversarial Networks (TAN)
Authors: Sean Rowan
Date: 2018-02-08 00:00:00
Tasks: Domain Adaptation

Transductive Adversarial Networks (TAN) is a novel domain-adaptation machine
learning framework that is designed for learning a conditional probability
distribution on unlabelled input data in a target domain, while also only
having access to: (1) easily obtained labelled data from a related source
domain, which may have a different conditional probability distribution than
the target domain, and (2) a marginalised prior distribution on the labels for
the target domain. TAN leverages a fully adversarial training procedure and a
unique generator/encoder architecture which approximates the transductive
combination of the available source- and target-domain data. A benefit of TAN
is that it allows the distance between the source- and target-domain
label-vector marginal probability distributions to be greater than 0 (i.e.
different tasks across the source and target domains) whereas other
domain-adaptation algorithms require this distance to equal 0 (i.e. a single
task across the source and target domains). TAN can, however, still handle the
latter case and is a more generalised approach to this case. Another benefit of
TAN is that due to being a fully adversarial algorithm, it has the potential to
accurately approximate highly complex distributions. Theoretical analysis
demonstrates the viability of the TAN framework.
Saliency-Enhanced Robust Visual Tracking
Authors: Caglar Aytekin, Francesco Cricri, Emre Aksu
Date: 2018-02-08 00:00:00
Tasks: object-detection, Object Detection, Object Tracking, RGB Salient Object Detection, Salient Object Detection, Visual Object Tracking, Visual Tracking

Discrete correlation filter (DCF) based trackers have shown considerable
success in visual object tracking. These trackers often make use of low to mid
level features such as histogram of gradients (HoG) and mid-layer activations
from convolution neural networks (CNNs). We argue that including semantically
higher level information to the tracked features may provide further robustness
to challenging cases such as viewpoint changes. Deep salient object detection
is one example of such high level features, as it make use of semantic
information to highlight the important regions in the given scene. In this
work, we propose an improvement over DCF based trackers by combining saliency
based and other features based filter responses. This combination is performed
with an adaptive weight on the saliency based filter responses, which is
automatically selected according to the temporal consistency of visual
saliency. We show that our method consistently improves a baseline DCF based
tracker especially in challenging cases and performs superior to the
state-of-the-art. Our improved tracker operates at 9.3 fps, introducing a small
computational burden over the baseline which operates at 11 fps.
Machine learning in APOGEE: Unsupervised spectral classification with $K$-means
Authors: Rafael Garcia-Dias, Carlos Allende Prieto, Jorge Sánchez Almeida, Ignacio Ordovás-Pascual
Date: 2018-01-24 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Clustering, General Classification

The data volume generated by astronomical surveys is growing rapidly.
Traditional analysis techniques in spectroscopy either demand intensive human
interaction or are computationally expensive. In this scenario, machine
learning, and unsupervised clustering algorithms in particular offer
interesting alternatives. The Apache Point Observatory Galactic Evolution
Experiment (APOGEE) offers a vast data set of near-infrared stellar spectra
which is perfect for testing such alternatives. Apply an unsupervised
classification scheme based on $K$-means to the massive APOGEE data set.
Explore whether the data are amenable to classification into discrete classes.
We apply the $K$-means algorithm to 153,847 high resolution spectra
($R\approx22,500$). We discuss the main virtues and weaknesses of the
algorithm, as well as our choice of parameters. We show that a classification
based on normalised spectra captures the variations in stellar atmospheric
parameters, chemical abundances, and rotational velocity, among other factors.
The algorithm is able to separate the bulge and halo populations, and
distinguish dwarfs, sub-giants, RC and RGB stars. However, a discrete
classification in flux space does not result in a neat organisation in the
parameters space. Furthermore, the lack of obvious groups in flux space causes
the results to be fairly sensitive to the initialisation, and disrupts the
efficiency of commonly-used methods to select the optimal number of clusters.
Our classification is publicly available, including extensive online material
associated with the APOGEE Data Release 12 (DR12). Our description of the
APOGEE database can enormously help with the identification of specific types
of targets for various applications. We find a lack of obvious groups in flux
space, and identify limitations of the $K$-means algorithm in dealing with this
kind of data.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
Authors: Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang
Date: 2017-10-09 00:00:00
Tasks: Network Embedding, Representation Learning

Since the invention of word2vec, the skip-gram model has significantly
advanced the research of network embedding, such as the recent emergence of the
DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of
the aforementioned models with negative sampling can be unified into the matrix
factorization framework with closed forms. Our analysis and proofs reveal that:
(1) DeepWalk empirically produces a low-rank transformation of a network's
normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk
when the size of vertices' context is set to one; (3) As an extension of LINE,
PTE can be viewed as the joint factorization of multiple networks' Laplacians;
(4) node2vec is factorizing a matrix related to the stationary distribution and
transition probability tensor of a 2nd-order random walk. We further provide
the theoretical connections between skip-gram based network embedding
algorithms and the theory of graph Laplacian. Finally, we present the NetMF
method as well as its approximation algorithm for computing network embedding.
Our method offers significant improvements over DeepWalk and LINE for
conventional network mining tasks. This work lays the theoretical foundation
for skip-gram based network embedding methods, leading to a better
understanding of latent network representation learning.
FixaTons: A collection of Human Fixations Datasets and Metrics for Scanpath Similarity
Authors: Dario Zanca, Valeria Serchi, Pietro Piu, Francesca Rosini, Alessandra Rufa
Date: 2018-02-07 00:00:00
Tasks: Descriptive, Saliency Prediction

In the last three decades, human visual attention has been a topic of great
interest in various disciplines. In computer vision, many models have been
proposed to predict the distribution of human fixations on a visual stimulus.
Recently, thanks to the creation of large collections of data, machine learning
algorithms have obtained state-of-the-art performance on the task of saliency
map estimation. On the other hand, computational models of scanpath are much
less studied. Works are often only descriptive or task specific. This is due to
the fact that the scanpath is harder to model because it must include the
description of a dynamic. General purpose computational models are present in
the literature, but are then evaluated in tasks of saliency prediction, losing
therefore information about the dynamics and the behaviour. In addition, two
technical reasons have limited the research. The first reason is the lack of
robust and uniformly used set of metrics to compare the similarity between
scanpath. The second reason is the lack of sufficiently large and varied
scanpath datasets. In this report we want to help in both directions. We
present FixaTons, a large collection of datasets human scanpaths (temporally
ordered sequences of fixations) and saliency maps. It comes along with a
software library for easy data usage, statistics calculation and implementation
of metrics for scanpath and saliency prediction evaluation.
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks
Authors: Thilo Strauss, Markus Hanselmann, Andrej Junginger, Holger Ulmer
Date: 2017-09-11 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Learning, Self-Driving Cars

Deep learning has become the state of the art approach in many machine
learning problems such as classification. It has recently been shown that deep
learning is highly vulnerable to adversarial perturbations. Taking the camera
systems of self-driving cars as an example, small adversarial perturbations can
cause the system to make errors in important tasks, such as classifying traffic
signs or detecting pedestrians. Hence, in order to use deep learning without
safety concerns a proper defense strategy is required. We propose to use
ensemble methods as a defense strategy against adversarial perturbations. We
find that an attack leading one model to misclassify does not imply the same
for other networks performing the same task. This makes ensemble methods an
attractive defense strategy against adversarial attacks. We empirically show
for the MNIST and the CIFAR-10 data sets that ensemble methods not only improve
the accuracy of neural networks on test data but also increase their robustness
against adversarial perturbations.
Scalable Relaxations of Sparse Packing Constraints: Optimal Biocontrol in Predator-Prey Network
Authors: Johan Bjorck, Yiwei Bai, Xiaojian Wu, Yexiang Xue, Mark C. Whitmore, Carla Gomes
Date: 2017-11-18 00:00:00
Tasks: Combinatorial Optimization, Management

Cascades represent rapid changes in networks. A cascading phenomenon of
ecological and economic impact is the spread of invasive species in geographic
landscapes. The most promising management strategy is often biocontrol, which
entails introducing a natural predator able to control the invading population,
a setting that can be treated as two interacting cascades of predator and prey
populations. We formulate and study a nonlinear problem of optimal biocontrol:
optimally seeding the predator cascade over time to minimize the harmful prey
population. Recurring budgets, which typically face conservation organizations,
naturally leads to sparse constraints which make the problem amenable to
approximation algorithms. Available methods based on continuous relaxations
scale poorly, to remedy this we develop a novel and scalable randomized
algorithm based on a width relaxation, applicable to a broad class of
combinatorial optimization problems. We evaluate our contributions in the
context of biocontrol for the insect pest Hemlock Wolly Adelgid (HWA) in
eastern North America. Our algorithm outperforms competing methods in terms of
scalability and solution quality, and finds near optimal strategies for the
control of the HWA for fine-grained networks -- an important problem in
computational sustainability.
Learning Inductive Biases with Simple Neural Networks
Authors: Reuben Feinman, Brenden M. Lake
Date: 2018-02-08 00:00:00
Tasks: Inductive Bias, Object Recognition

People use rich prior knowledge about the world in order to efficiently learn
new concepts. These priors - also known as "inductive biases" - pertain to the
space of internal models considered by a learner, and they help the learner
make inferences that go beyond the observed data. A recent study found that
deep neural networks optimized for object recognition develop the shape bias
(Ritter et al., 2017), an inductive bias possessed by children that plays an
important role in early word learning. However, these networks use
unrealistically large quantities of training data, and the conditions required
for these biases to develop are not well understood. Moreover, it is unclear
how the learning dynamics of these networks relate to developmental processes
in childhood. We investigate the development and influence of the shape bias in
neural networks using controlled datasets of abstract patterns and synthetic
images, allowing us to systematically vary the quantity and form of the
experience provided to the learning algorithms. We find that simple neural
networks develop a shape bias after seeing as few as 3 examples of 4 object
categories. The development of these biases predicts the onset of vocabulary
acceleration in our networks, consistent with the developmental process in
children.
From Hashing to CNNs: Training BinaryWeight Networks via Hashing
Authors: Qinghao Hu, Peisong Wang, Jian Cheng
Date: 2018-02-08 00:00:00

Deep convolutional neural networks (CNNs) have shown appealing performance on
various computer vision tasks in recent years. This motivates people to deploy
CNNs to realworld applications. However, most of state-of-art CNNs require
large memory and computational resources, which hinders the deployment on
mobile devices. Recent studies show that low-bit weight representation can
reduce much storage and memory demand, and also can achieve efficient network
inference. To achieve this goal, we propose a novel approach named BWNH to
train Binary Weight Networks via Hashing. In this paper, we first reveal the
strong connection between inner-product preserving hashing and binary weight
networks, and show that training binary weight networks can be intrinsically
regarded as a hashing problem. Based on this perspective, we propose an
alternating optimization method to learn the hash codes instead of directly
learning binary weights. Extensive experiments on CIFAR10, CIFAR100 and
ImageNet demonstrate that our proposed BWNH outperforms current state-of-art by
a large margin.
Topologically Controlled Lossy Compression
Authors: Maxime Soler, Melanie Plainchault, Bruno Conche, Julien Tierny
Date: 2018-02-08 00:00:00
Tasks: Quantization, Topological Data Analysis

This paper presents a new algorithm for the lossy compression of scalar data
defined on 2D or 3D regular grids, with topological control. Certain techniques
allow users to control the pointwise error induced by the compression. However,
in many scenarios it is desirable to control in a similar way the preservation
of higher-level notions, such as topological features , in order to provide
guarantees on the outcome of post-hoc data analyses. This paper presents the
first compression technique for scalar data which supports a strictly
controlled loss of topological features. It provides users with specific
guarantees both on the preservation of the important features and on the size
of the smaller features destroyed during compression. In particular, we present
a simple compression strategy based on a topologically adaptive quantization of
the range. Our algorithm provides strong guarantees on the bottleneck distance
between persistence diagrams of the input and decompressed data, specifically
those associated with extrema. A simple extension of our strategy additionally
enables a control on the pointwise error. We also show how to combine our
approach with state-of-the-art compressors, to further improve the geometrical
reconstruction. Extensive experiments, for comparable compression rates,
demonstrate the superiority of our algorithm in terms of the preservation of
topological features. We show the utility of our approach by illustrating the
compatibility between the output of post-hoc topological data analysis
pipelines, executed on the input and decompressed data, for simulated or
acquired data sets. We also provide a lightweight VTK-based C++ implementation
of our approach for reproduction purposes.
Recurrent Scale Approximation for Object Detection in CNN
Authors: Yu Liu, Hongyang Li, Junjie Yan, Fangyin Wei, Xiaogang Wang, Xiaoou Tang
Date: 2017-07-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Face Detection, Object, object-detection, Object Detection

Since convolutional neural network (CNN) lacks an inherent mechanism to
handle large scale variations, we always need to compute feature maps multiple
times for multi-scale object detection, which has the bottleneck of
computational cost in practice. To address this, we devise a recurrent scale
approximation (RSA) to compute feature map once only, and only through this map
can we approximate the rest maps on other levels. At the core of RSA is the
recursive rolling out mechanism: given an initial map at a particular scale, it
generates the prediction at a smaller scale that is half the size of input. To
further increase efficiency and accuracy, we (a): design a scale-forecast
network to globally predict potential scales in the image since there is no
need to compute maps on all levels of the pyramid. (b): propose a landmark
retracing network (LRN) to trace back locations of the regressed landmarks and
generate a confidence score for each landmark; LRN can effectively alleviate
false positives caused by the accumulated error in RSA. The whole system can be
trained end-to-end in a unified CNN framework. Experiments demonstrate that our
proposed algorithm is superior against state-of-the-art methods on face
detection benchmarks and achieves comparable results for generic proposal
generation. The source code of RSA is available at
github.com/sciencefans/RSA-for-object-detection.
Deep Image Super Resolution via Natural Image Priors
Authors: Hojjat S. Mousavi, Tiantong Guo, Vishal Monga
Date: 2018-02-08 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Single image super-resolution (SR) via deep learning has recently gained
significant attention in the literature. Convolutional neural networks (CNNs)
are typically learned to represent the mapping between low-resolution (LR) and
high-resolution (HR) images/patches with the help of training examples. Most
existing deep networks for SR produce high quality results when training data
is abundant. However, their performance degrades sharply when training is
limited. We propose to regularize deep structures with prior knowledge about
the images so that they can capture more structural information from the same
limited data. In particular, we incorporate in a tractable fashion within the
CNN framework, natural image priors which have shown to have much recent
success in imaging and vision inverse problems. Experimental results show that
the proposed deep network with natural image priors is particularly effective
in training starved regimes.
Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction
Authors: Da Xiao, Jo-Yu Liao, Xingyuan Yuan
Date: 2018-02-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Reinforcement Learning

To overcome the limitations of Neural Programmer-Interpreters (NPI) in its
universality and learnability, we propose the incorporation of combinator
abstraction into neural programing and a new NPI architecture to support this
abstraction, which we call Combinatory Neural Programmer-Interpreter (CNPI).
Combinator abstraction dramatically reduces the number and complexity of
programs that need to be interpreted by the core controller of CNPI, while
still allowing the CNPI to represent and interpret arbitrary complex programs
by the collaboration of the core with the other components. We propose a small
set of four combinators to capture the most pervasive programming patterns. Due
to the finiteness and simplicity of this combinator set and the offloading of
some burden of interpretation from the core, we are able construct a CNPI that
is universal with respect to the set of all combinatorizable programs, which is
adequate for solving most algorithmic tasks. Moreover, besides supervised
training on execution traces, CNPI can be trained by policy gradient
reinforcement learning with appropriately designed curricula.
On consistency of optimal pricing algorithms in repeated posted-price auctions with strategic buyer
Authors: Alexey Drutsa
Date: 2017-07-17 00:00:00

We study revenue optimization learning algorithms for repeated posted-price
auctions where a seller interacts with a single strategic buyer that holds a
fixed private valuation for a good and seeks to maximize his cumulative
discounted surplus. For this setting, first, we propose a novel algorithm that
never decreases offered prices and has a tight strategic regret bound in
$\Theta(\log\log T)$ under some mild assumptions on the buyer surplus
discounting. This result closes the open research question on the existence of
a no-regret horizon-independent weakly consistent pricing. The proposed
algorithm is inspired by our observation that a double decrease of offered
prices in a weakly consistent algorithm is enough to cause a linear regret.
This motivates us to construct a novel transformation that maps a
right-consistent algorithm to a weakly consistent one that never decreases
offered prices.
  Second, we outperform the previously known strategic regret upper bound of
the algorithm PRRFES, where the improvement is achieved by means of a finer
constant factor $C$ of the principal term $C\log\log T$ in this upper bound.
Finally, we generalize results on strategic regret previously known for
geometric discounting of the buyer's surplus to discounting of other types,
namely: the optimality of the pricing PRRFES to the case of geometrically
concave decreasing discounting; and linear lower bound on the strategic regret
of a wide range of horizon-independent weakly consistent algorithms to the case
of arbitrary discounts.