pwc-archive/papers-with-abstracts huggingface dataset:
Extracting urban impervious surface from GF-1 imagery using one-class classifiers
Authors: Yao Yao, Jialv He, Jinbao Zhang, Yatao Zhang
Date: 2017-05-13 00:00:00
Tasks: General Classification, Management

Impervious surface area is a direct consequence of the urbanization, which
also plays an important role in urban planning and environmental management.
With the rapidly technical development of remote sensing, monitoring urban
impervious surface via high spatial resolution (HSR) images has attracted
unprecedented attention recently. Traditional multi-classes models are
inefficient for impervious surface extraction because it requires labeling all
needed and unneeded classes that occur in the image exhaustively. Therefore, we
need to find a reliable one-class model to classify one specific land cover
type without labeling other classes. In this study, we investigate several
one-class classifiers, such as Presence and Background Learning (PBL), Positive
Unlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervious
surface area using high spatial resolution imagery of GF-1, China's new
generation of high spatial remote sensing satellite, and evaluate the
classification accuracy based on artificial interpretation results. Compared to
traditional multi-classes classifiers (ANN and SVM), the experimental results
indicate that PBL and PUL provide higher classification accuracy, which is
similar to the accuracy provided by ANN model. Meanwhile, PBL and PUL
outperforms OCSVM, BSVM, MAXENT and SVM models. Hence, the one-class
classifiers only need a small set of specific samples to train models without
losing predictive accuracy, which is supposed to gain more attention on urban
impervious surface extraction or other one specific land cover type.
Learning Semantic Correspondences in Technical Documentation
Authors: Kyle Richardson, Jonas Kuhn
Date: 2017-05-13 00:00:00
Proceeding: ACL 2017 7
Tasks: Semantic Parsing

We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representations in the target documentation, such as formal representation of
functions or code templates. Our approach exploits the parallel nature of such
documentation, or the tight coupling between high-level text and the low-level
representations we aim to learn. Data is collected by mining technical
documents for such parallel text-representation pairs, which we use to train a
simple semantic parsing model. We report new baseline results on sixteen novel
datasets, including the standard library documentation for nine popular
programming languages across seven natural languages, and a small collection of
Unix utility manuals.
Subspace clustering using a symmetric low-rank representation
Authors: Jie Chen, Hua Mao, Yongsheng Sang, Zhang Yi
Date: 2014-03-07 00:00:00
Tasks: Clustering

In this paper, we propose a low-rank representation with symmetric constraint
(LRRSC) method for robust subspace clustering. Given a collection of data
points approximately drawn from multiple subspaces, the proposed technique can
simultaneously recover the dimension and members of each subspace. LRRSC
extends the original low-rank representation algorithm by integrating a
symmetric constraint into the low-rankness property of high-dimensional data
representation. The symmetric low-rank representation, which preserves the
subspace structures of high-dimensional data, guarantees weight consistency for
each pair of data points so that highly correlated data points of subspaces are
represented together. Moreover, it can be efficiently calculated by solving a
convex optimization problem. We provide a rigorous proof for minimizing the
nuclear-norm regularized least square problem with a symmetric constraint. The
affinity matrix for spectral clustering can be obtained by further exploiting
the angular information of the principal directions of the symmetric low-rank
representation. This is a critical step towards evaluating the memberships
between data points. Experimental results on benchmark databases demonstrate
the effectiveness and robustness of LRRSC compared with several
state-of-the-art subspace clustering algorithms.
Inductive supervised quantum learning
Authors: Alex Monràs, Gael Sentís, Peter Wittek
Date: 2016-05-24 00:00:00
Tasks: Inductive Learning, Learning Theory

In supervised learning, an inductive learning algorithm extracts general
rules from observed training instances, then the rules are applied to test
instances. We show that this splitting of training and application arises
naturally, in the classical setting, from a simple independence requirement
with a physical interpretation of being non-signalling. Thus, two seemingly
different definitions of inductive learning happen to coincide. This follows
from the properties of classical information that break down in the quantum
setup. We prove a quantum de Finetti theorem for quantum channels, which shows
that in the quantum case, the equivalence holds in the asymptotic setting, that
is, for large number of test instances. This reveals a natural analogy between
classical learning protocols and their quantum counterparts, justifying a
similar treatment, and allowing to inquire about standard elements in
computational learning theory, such as structural risk minimization and sample
complexity.
Absolute Pose Estimation from Line Correspondences using Direct Linear Transformation
Authors: Bronislav Přibyl, Pavel Zemčík, Martin Čadík
Date: 2016-08-24 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation

This work is concerned with camera pose estimation from correspondences of
3D/2D lines, i. e. with the Perspective-n-Line (PnL) problem. We focus on large
line sets, which can be efficiently solved by methods using linear formulation
of PnL. We propose a novel method "DLT-Combined-Lines" based on the Direct
Linear Transformation (DLT) algorithm, which benefits from a new combination of
two existing DLT methods for pose estimation. The method represents 2D
structure by lines, and 3D structure by both points and lines. The redundant 3D
information reduces the minimum required line correspondences to 5. A
cornerstone of the method is a combined projection matri xestimated by the DLT
algorithm. It contains multiple estimates of camera rotation and translation,
which can be recovered after enforcing constraints of the matrix. Multiplicity
of the estimates is exploited to improve the accuracy of the proposed method.
For large line sets (10 and more), the method is comparable to the
state-of-theart in accuracy of orientation estimation. It achieves
state-of-the-art accuracy in estimation of camera position and it yields the
smallest reprojection error under strong image noise. The method achieves top-3
results on real world data. The proposed method is also highly computationally
effective, estimating the pose of 1000 lines in 12 ms on a desktop computer.
Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals
Authors: David Rügamer, Sarah Brockhaus, Kornelia Gentsch, Klaus Scherer, Sonja Greven
Date: 2016-09-20 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Electromyography (EMG), regression

The link between different psychophysiological measures during emotion
episodes is not well understood. To analyse the functional relationship between
electroencephalography (EEG) and facial electromyography (EMG), we apply
historical function-on-function regression models to EEG and EMG data that were
simultaneously recorded from 24 participants while they were playing a
computerised gambling task. Given the complexity of the data structure for this
application, we extend simple functional historical models to models including
random historical effects, factor-specific historical effects, and
factor-specific random historical effects. Estimation is conducted by a
component-wise gradient boosting algorithm, which scales well to large data
sets and complex models.
Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making
Authors: Andrew Critch
Date: 2017-01-05 00:00:00
Tasks: Decision Making, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Existing multi-objective reinforcement learning (MORL) algorithms do not
account for objectives that arise from players with differing beliefs.
Concretely, consider two players with different beliefs and utility functions
who may cooperate to build a machine that takes actions on their behalf. A
representation is needed for how much the machine's policy will prioritize each
player's interests over time. Assuming the players have reached common
knowledge of their situation, this paper derives a recursion that any Pareto
optimal policy must satisfy. Two qualitative observations can be made from the
recursion: the machine must (1) use each player's own beliefs in evaluating how
well an action will serve that player's utility function, and (2) shift the
relative priority it assigns to each player's expected utilities over time, by
a factor proportional to how well that player's beliefs predict the machine's
inputs. Observation (2) represents a substantial divergence from na\"{i}ve
linear utility aggregation (as in Harsanyi's utilitarian theorem, and existing
MORL algorithms), which is shown here to be inadequate for Pareto optimal
sequential decision-making on behalf of players with different beliefs.
Motion-Compensated Temporal Filtering for Critically-Sampled Wavelet-Encoded Images
Authors: Vildan Atalay Aydin, Hassan Foroosh
Date: 2017-05-13 00:00:00
Tasks: Motion Estimation

We propose a novel motion estimation/compensation (ME/MC) method for
wavelet-based (in-band) motion compensated temporal filtering (MCTF), with
application to low-bitrate video coding. Unlike the conventional in-band MCTF
algorithms, which require redundancy to overcome the shift-variance problem of
critically sampled (complete) discrete wavelet transforms (DWT), we perform
ME/MC steps directly on DWT coefficients by avoiding the need of
shift-invariance. We omit upsampling, the inverse-DWT (IDWT), and the
calculation of redundant DWT coefficients, while achieving arbitrary subpixel
accuracy without interpolation, and high video quality even at very
low-bitrates, by deriving the exact relationships between DWT subbands of input
image sequences. Experimental results demonstrate the accuracy of the proposed
method, confirming that our model for ME/MC effectively improves video coding
quality.
Asymmetric Tri-training for Unsupervised Domain Adaptation
Authors: Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada
Date: 2017-02-27 00:00:00
Proceeding: ICML 2017 8
Tasks: Domain Adaptation, Sentiment Analysis, Unsupervised Domain Adaptation

Deep-layered models trained on a large number of labeled samples boost the
accuracy of many tasks. It is important to apply such models to different
domains because collecting many labeled samples in various domains is
expensive. In unsupervised domain adaptation, one needs to train a classifier
that works well on a target domain when provided with labeled source samples
and unlabeled target samples. Although many methods aim to match the
distributions of source and target samples, simply matching the distribution
cannot ensure accuracy on the target domain. To learn discriminative
representations for the target domain, we assume that artificially labeling
target samples can result in a good representation. Tri-training leverages
three classifiers equally to give pseudo-labels to unlabeled samples, but the
method does not assume labeling samples generated from a different domain.In
this paper, we propose an asymmetric tri-training method for unsupervised
domain adaptation, where we assign pseudo-labels to unlabeled samples and train
neural networks as if they are true labels. In our work, we use three networks
asymmetrically. By asymmetric, we mean that two networks are used to label
unlabeled target samples and one network is trained by the samples to obtain
target-discriminative representations. We evaluate our method on digit
recognition and sentiment analysis datasets. Our proposed method achieves
state-of-the-art performance on the benchmark digit recognition datasets of
domain adaptation.
GRASS: Generative Recursive Autoencoders for Shape Structures
Authors: Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, Leonidas Guibas
Date: 2017-05-05 00:00:00
Tasks: Decoder

We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.
Unrolled Generative Adversarial Networks
Authors: Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
Date: 2016-11-07 00:00:00
Tasks: Diversity

We introduce a method to stabilize Generative Adversarial Networks (GANs) by
defining the generator objective with respect to an unrolled optimization of
the discriminator. This allows training to be adjusted between using the
optimal discriminator in the generator's objective, which is ideal but
infeasible in practice, and using the current value of the discriminator, which
is often unstable and leads to poor solutions. We show how this technique
solves the common problem of mode collapse, stabilizes training of GANs with
complex recurrent generators, and increases diversity and coverage of the data
distribution by the generator.
Topologically Robust 3D Shape Matching via Gradual Deflation and Inflation
Authors: Asli Genctav, Yusuf Sahillioglu, Sibel Tari
Date: 2017-04-30 00:00:00
Tasks: Diversity

Despite being vastly ignored in the literature, coping with topological noise
is an issue of increasing importance, especially as a consequence of the
increasing number and diversity of 3D polygonal models that are captured by
devices of different qualities or synthesized by algorithms of different
stabilities. One approach for matching 3D shapes under topological noise is to
replace the topology-sensitive geodesic distance with distances that are less
sensitive to topological changes. We propose an alternative approach utilising
gradual deflation (or inflation) of the shape volume, of which purpose is to
bring the pair of shapes to be matched to a \emph{comparable} topology before
the search for correspondences. Illustrative experiments using different
datasets demonstrate that as the level of topological noise increases, our
approach outperforms the other methods in the literature.
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks
Authors: Syed Shakib Sarwar, Priyadarshini Panda, Kaushik Roy
Date: 2017-05-12 00:00:00
Tasks: Face Detection, Object Recognition

Convolutional Neural Networks (CNN) are being increasingly used in computer
vision for a wide range of classification and recognition problems. However,
training these large networks demands high computational time and energy
requirements; hence, their energy-efficient implementation is of great
interest. In this work, we reduce the training complexity of CNNs by replacing
certain weight kernels of a CNN with Gabor filters. The convolutional layers
use the Gabor filters as fixed weight kernels, which extracts intrinsic
features, with regular trainable weight kernels. This combination creates a
balanced system that gives better training performance in terms of energy and
time, compared to the standalone CNN (without any Gabor kernels), in exchange
for tolerable accuracy degradation. We show that the accuracy degradation can
be mitigated by partially training the Gabor kernels, for a small fraction of
the total training cycles. We evaluated the proposed approach on 4 benchmark
applications. Simple tasks like face detection and character recognition (MNIST
and TiCH), were implemented using LeNet architecture. While a more complex task
of object recognition (CIFAR10) was implemented on a state of the art deep CNN
(Network in Network) architecture. The proposed approach yields 1.31-1.53x
improvement in training energy in comparison to conventional CNN
implementation. We also obtain improvement up to 1.4x in training time, up to
2.23x in storage requirements, and up to 2.2x in memory access energy. The
accuracy degradation suffered by the approximate implementations is within 0-3%
of the baseline.
On the Complexity of Semantic Integration of OWL Ontologies
Authors: Yevgeny Kazakov, Denis Ponomaryov
Date: 2017-05-12 00:00:00

We propose a new mechanism for integration of OWL ontologies using semantic
import relations. In contrast to the standard OWL importing, we do not require
all axioms of the imported ontologies to be taken into account for reasoning
tasks, but only their logical implications over a chosen signature. This
property comes natural in many ontology integration scenarios, especially when
the number of ontologies is large. In this paper, we study the complexity of
reasoning over ontologies with semantic import relations and establish a range
of tight complexity bounds for various fragments of OWL.
Progression of Decomposed Local-Effect Action Theories
Authors: Denis Ponomaryov, Mikhail Soutchanski
Date: 2017-05-12 00:00:00

In many tasks related to reasoning about consequences of a logical theory, it
is desirable to decompose the theory into a number of weakly-related or
independent components. However, a theory may represent knowledge that is
subject to change, as a result of executing actions that have effects on some
of the initial properties mentioned in the theory. Having once computed a
decomposition of a theory, it is advantageous to know whether a decomposition
has to be computed again in the newly-changed theory (obtained from taking into
account changes resulting from execution of an action). In the paper, we
address this problem in the scope of the situation calculus, where a change of
an initial theory is related to the notion of progression. Progression provides
a form of forward reasoning; it relies on forgetting values of those
properties, which are subject to change, and computing new values for them. We
consider decomposability and inseparability, two component properties known
from the literature, and contribute by 1) studying the conditions when these
properties are preserved and 2) when they are lost wrt progression and the
related operation of forgetting. To show the latter, we demonstrate the
boundaries using a number of negative examples. To show the former, we identify
cases when these properties are preserved under forgetting and progression of
initial theories in local-effect basic action theories of the situation
calculus. Our paper contributes to bridging two different communities in
Knowledge Representation, namely research on modularity and research on
reasoning about actions.
Deep Learning Microscopy
Authors: Yair Rivenson, Zoltan Gorocs, Harun Gunaydin, Yibo Zhang, Hongda Wang, Aydogan Ozcan
Date: 2017-05-12 00:00:00
Tasks: Deep Learning

We demonstrate that a deep neural network can significantly improve optical
microscopy, enhancing its spatial resolution over a large field-of-view and
depth-of-field. After its training, the only input to this network is an image
acquired using a regular optical microscope, without any changes to its design.
We blindly tested this deep learning approach using various tissue samples that
are imaged with low-resolution and wide-field systems, where the network
rapidly outputs an image with remarkably better resolution, matching the
performance of higher numerical aperture lenses, also significantly surpassing
their limited field-of-view and depth-of-field. These results are
transformative for various fields that use microscopy tools, including e.g.,
life sciences, where optical microscopy is considered as one of the most widely
used and deployed techniques. Beyond such applications, our presented approach
is broadly applicable to other imaging modalities, also spanning different
parts of the electromagnetic spectrum, and can be used to design computational
imagers that get better and better as they continue to image specimen and
establish new transformations among different modes of imaging.
A Formal Characterization of the Local Search Topology of the Gap Heuristic
Authors: Richard Anthony Valenzano, Danniel Sihui Yang
Date: 2017-05-12 00:00:00
Tasks: Heuristic Search

The pancake puzzle is a classic optimization problem that has become a
standard benchmark for heuristic search algorithms. In this paper, we provide
full proofs regarding the local search topology of the gap heuristic for the
pancake puzzle. First, we show that in any non-goal state in which there is no
move that will decrease the number of gaps, there is a move that will keep the
number of gaps constant. We then classify any state in which the number of gaps
cannot be decreased in a single action into two groups: those requiring 2
actions to decrease the number of gaps, and those which require 3 actions to
decrease the number of gaps.
Monaural Audio Speaker Separation with Source Contrastive Estimation
Authors: Cory Stephenson, Patrick Callier, Abhinav Ganesh, Karl Ni
Date: 2017-05-12 00:00:00
Tasks: Clustering, Descriptive, Speaker Separation

We propose an algorithm to separate simultaneously speaking persons from each
other, the "cocktail party problem", using a single microphone. Our approach
involves a deep recurrent neural networks regression to a vector space that is
descriptive of independent speakers. Such a vector space can embed empirically
determined speaker characteristics and is optimized by distinguishing between
speaker masks. We call this technique source-contrastive estimation. The
methodology is inspired by negative sampling, which has seen success in natural
language processing, where an embedding is learned by correlating and
de-correlating a given input vector with output weights. Although the matrix
determined by the output weights is dependent on a set of known speakers, we
only use the input vectors during inference. Doing so will ensure that source
separation is explicitly speaker-independent. Our approach is similar to recent
deep neural network clustering and permutation-invariant training research; we
use weighted spectral features and masks to augment individual speaker
frequencies while filtering out other speakers. We avoid, however, the severe
computational burden of other approaches with our technique. Furthermore, by
training a vector space rather than combinations of different speakers or
differences thereof, we avoid the so-called permutation problem during
training. Our algorithm offers an intuitive, computationally efficient response
to the cocktail party problem, and most importantly boasts better empirical
performance than other current techniques.