pwc-archive/papers-with-abstracts huggingface dataset:
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification
Authors: Sounak Dey, Anjan Dutta, J. Ignacio Toledo, Suman K. Ghosh, Josep Llados, Umapada Pal
Date: 2017-07-07 00:00:00
Tasks: Handwriting Verification

Offline signature verification is one of the most challenging tasks in
biometrics and document forensics. Unlike other verification problems, it needs
to model minute but critical details between genuine and forged signatures,
because a skilled falsification might often resembles the real signature with
small deformation. This verification task is even harder in writer independent
scenarios which is undeniably fiscal for realistic cases. In this paper, we
model an offline writer independent signature verification task with a
convolutional Siamese network. Siamese networks are twin networks with shared
weights, which can be trained to learn a feature space where similar
observations are placed in proximity. This is achieved by exposing the network
to a pair of similar and dissimilar observations and minimizing the Euclidean
distance between similar pairs while simultaneously maximizing it between
dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the
capability of our network to model forgery in different languages (scripts) and
handwriting styles. Moreover, our designed Siamese network, named SigNet,
exceeds the state-of-the-art results on most of the benchmark signature
datasets, which paves the way for further research in this direction.
Decontamination of Mutual Contamination Models
Authors: Julian Katz-Samuels, Gilles Blanchard, Clayton Scott
Date: 2017-09-30 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Many machine learning problems can be characterized by mutual contamination
models. In these problems, one observes several random samples from different
convex combinations of a set of unknown base distributions and the goal is to
infer these base distributions. This paper considers the general setting where
the base distributions are defined on arbitrary probability spaces. We examine
three popular machine learning problems that arise in this general setting:
multiclass classification with label noise, demixing of mixed membership
models, and classification with partial labels. In each case, we give
sufficient conditions for identifiability and present algorithms for the
infinite and finite sample settings, with associated performance guarantees.
Two-sample Statistics Based on Anisotropic Kernels
Authors: Xiuyuan Cheng, Alexander Cloninger, Ronald R. Coifman
Date: 2017-09-14 00:00:00
Tasks: Diffusion  MRI, Vocal Bursts Valence Prediction

The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD)
statistic for measuring the distance between two distributions given
finitely-many multivariate samples. When the distributions are locally
low-dimensional, the proposed test can be made more powerful to distinguish
certain alternatives by incorporating local covariance matrices and
constructing an anisotropic kernel. The kernel matrix is asymmetric; it
computes the affinity between $n$ data points and a set of $n_R$ reference
points, where $n_R$ can be drastically smaller than $n$. While the proposed
statistic can be viewed as a special class of Reproducing Kernel Hilbert Space
MMD, the consistency of the test is proved, under mild assumptions of the
kernel, as long as $\|p-q\| \sqrt{n} \to \infty $, and a finite-sample lower
bound of the testing power is obtained. Applications to flow cytometry and
diffusion MRI datasets are demonstrated, which motivate the proposed approach
to compare distributions.
Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches
Authors: Yonatan Vaizman, Katherine Ellis, Gert Lanckriet
Date: 2016-09-20 00:00:00

The ability to automatically recognize a person's behavioral context can
contribute to health monitoring, aging care and many other domains. Validating
context recognition in-the-wild is crucial to promote practical applications
that work in real-life settings. We collected over 300k minutes of sensor data
with context labels from 60 subjects. Unlike previous studies, our subjects
used their own personal phone, in any way that was convenient to them, and
engaged in their routine in their natural environments. Unscripted behavior and
unconstrained phone usage resulted in situations that are harder to recognize.
We demonstrate how fusion of multi-modal sensors is important for resolving
such cases. We present a baseline system, and encourage researchers to use our
public dataset to compare methods and improve context recognition in-the-wild.
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems
Authors: Weinan E, Bing Yu
Date: 2017-09-30 00:00:00
Tasks: Deep Learning

We propose a deep learning based method, the Deep Ritz Method, for
numerically solving variational problems, particularly the ones that arise from
partial differential equations. The Deep Ritz method is naturally nonlinear,
naturally adaptive and has the potential to work in rather high dimensions. The
framework is quite simple and fits well with the stochastic gradient descent
method used in deep learning. We illustrate the method on several problems
including some eigenvalue problems.
Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction
Authors: Diana Nicoleta Popa, James Henderson
Date: 2017-09-30 00:00:00
Tasks: Natural Language Inference, Semantic Textual Similarity, Word Embeddings

Vector-space models, from word embeddings to neural network parsers, have
many advantages for NLP. But how to generalise from fixed-length word vectors
to a vector space for arbitrary linguistic structures is still unclear. In this
paper we propose bag-of-vector embeddings of arbitrary linguistic graphs. A
bag-of-vector space is the minimal nonparametric extension of a vector space,
allowing the representation to grow with the size of the graph, but not tying
the representation to any specific tree or graph structure. We propose
efficient training and inference algorithms based on tensor factorisation for
embedding arbitrary graphs in a bag-of-vector space. We demonstrate the
usefulness of this representation by training bag-of-vector embeddings of
dependency graphs and evaluating them on unsupervised semantic induction for
the Semantic Textual Similarity and Natural Language Inference tasks.
Robust Tracking and Behavioral Modeling of Movements of Biological Collectives from Ordinary Video Recordings
Authors: Hiroki Sayama, Farnaz Zamani Esfahlani, Ali Jazayeri, J. Scott Turner
Date: 2017-07-23 00:00:00

We propose a novel computational method to extract information about
interactions among individuals with different behavioral states in a biological
collective from ordinary video recordings. Assuming that individuals are acting
as finite state machines, our method first detects discrete behavioral states
of those individuals and then constructs a model of their state transitions,
taking into account the positions and states of other individuals in the
vicinity. We have tested the proposed method through applications to two
real-world biological collectives: termites in an experimental setting and
human pedestrians in a university campus. For each application, a robust
tracking system was developed in-house, utilizing interactive human
intervention (for termite tracking) or online agent-based simulation (for
pedestrian tracking). In both cases, significant interactions were detected
between nearby individuals with different states, demonstrating the
effectiveness of the proposed method.
An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
Authors: Marcin Junczys-Dowmunt, Roman Grundkiewicz
Date: 2017-06-13 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Automatic Post-Editing, Hard Attention, Machine Translation, Translation

In this work, we explore multiple neural architectures adapted for the task
of automatic post-editing of machine translation output. We focus on neural
end-to-end models that combine both inputs $mt$ (raw MT output) and $src$
(source language input) in a single neural architecture, modeling $\{mt, src\}
\rightarrow pe$ directly. Apart from that, we investigate the influence of
hard-attention models which seem to be well-suited for monolingual tasks, as
well as combinations of both ideas. We report results on data sets provided
during the WMT-2016 shared task on automatic post-editing and can demonstrate
that dual-attention models that incorporate all available data in the APE
scenario in a single model improve on the best shared task system and on all
other published results after the shared task. Dual-attention models that are
combined with hard attention remain competitive despite applying fewer changes
to the input.
Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images
Authors: Sergiy Zhuk, Tigran Tchrakian, Albert Akhriev, Siyuan Lu, Hendrik Hamann
Date: 2017-09-30 00:00:00
Tasks: Motion Forecasting, Optical Flow Estimation

This paper describes a new algorithm for solar energy forecasting from a
sequence of Cloud Optical Depth (COD) images. The algorithm is based on the
following simple observation: the dynamics of clouds represented by COD images
resembles the motion (transport) of a density in a fluid flow. This suggests
that, to forecast the motion of COD images, it is sufficient to forecast the
flow. The latter, in turn, can be accomplished by fitting a parametric model of
the fluid flow to the COD images observed in the past. Namely, the learning
phase of the algorithm is composed of the following steps: (i) given a sequence
of COD images, the snapshots of the optical flow are estimated from two
consecutive COD images; (ii) these snapshots are then assimilated into a
Navier-Stokes Equation (NSE), i.e. an initial velocity field for NSE is
selected so that the corresponding NSE' solution is as close as possible to the
optical flow snapshots. The prediction phase consists of utilizing a linear
transport equation, which describes the propagation of COD images in the fluid
flow predicted by NSE, to estimate the future motion of the COD images. The
algorithm has been tested on COD images provided by two geostationary
operational environmental satellites from NOAA serving the west-hemisphere.
Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis
Authors: S. Joseph, H. Ujir, I. Hipiny
Date: 2017-09-30 00:00:00
Tasks: Classification, Edge Detection, General Classification, image-classification, Image Classification

Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope automation, digital image acquisition, edge detection and colour analysis (histogram). We collected 60 digital images from 20 standard thin sections using a digital camera mounted on a conventional microscope. Each image is partitioned into a finite number of cells that form a grid structure. Edge and colour profile of pixels inside each cell determine its classification. The individual cells then determine the thin section image classification via a majority voting scheme. Our method yielded successful results as high as 90% to 100% precision.
Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze
Authors: I. Hipiny, H. Ujir, J. L. Minoi, S. F. Samson Juan, M. A. Khairuddin, M. S. Sunar
Date: 2017-09-30 00:00:00
Tasks: Activity Recognition, Retrieval, Video Retrieval

Unsupervised segmentation of action segments in egocentric videos is a desirable feature in tasks such as activity recognition and content-based video retrieval. Reducing the search space into a finite set of action segments facilitates a faster and less noisy matching. However, there exist a substantial gap in machine understanding of natural temporal cuts during a continuous human activity. This work reports on a novel gaze-based approach for segmenting action segments in videos captured using an egocentric camera. Gaze is used to locate the region-of-interest inside a frame. By tracking two simple motion-based parameters inside successive regions-of-interest, we discover a finite set of temporal cuts. We present several results using combinations (of the two parameters) on a dataset, i.e., BRISGAZE-ACTIONS. The dataset contains egocentric videos depicting several daily-living activities. The quality of the temporal cuts is further improved by implementing two entropy measures.
A Many-Objective Evolutionary Algorithm with Angle-Based Selection and Shift-Based Density Estimation
Authors: Zhi-Zhong Liu, Yong Wang, Pei-Qiu Huang
Date: 2017-09-30 00:00:00
Tasks: Density Estimation, Evolutionary Algorithms

Evolutionary many-objective optimization has been gaining increasing
attention from the evolutionary computation research community. Much effort has
been devoted to addressing this issue by improving the scalability of
multiobjective evolutionary algorithms, such as Pareto-based,
decomposition-based, and indicator-based approaches. Different from current
work, we propose a novel algorithm in this paper called AnD, which consists of
an angle-based selection strategy and a shift-based density estimation
strategy. These two strategies are employed in the environmental selection to
delete the poor individuals one by one. Specifically, the former is devised to
find a pair of individuals with the minimum vector angle, which means that
these two individuals share the most similar search direction. The latter,
which takes both the diversity and convergence into account, is adopted to
compare these two individuals and to delete the worse one. AnD has a simple
structure, few parameters, and no complicated operators. The performance of AnD
is compared with that of seven state-of-the-art many-objective evolutionary
algorithms on a variety of benchmark test problems with up to 15 objectives.
The experimental results suggest that AnD can achieve highly competitive
performance. In addition, we also verify that AnD can be readily extended to
solve constrained many-objective optimization problems.
PCANet-II: When PCANet Meets the Second Order Pooling
Authors: Lei Tian, Xiaopeng Hong, Guoying Zhao, Chunxiao Fan, Yue Ming, Matti Pietikäinen
Date: 2017-09-30 00:00:00

PCANet, as one noticeable shallow network, employs the histogram
representation for feature pooling. However, there are three main problems
about this kind of pooling method. First, the histogram-based pooling method
binarizes the feature maps and leads to inevitable discriminative information
loss. Second, it is difficult to effectively combine other visual cues into a
compact representation, because the simple concatenation of various visual cues
leads to feature representation inefficiency. Third, the dimensionality of
histogram-based output grows exponentially with the number of feature maps
used. In order to overcome these problems, we propose a novel shallow network
model, named as PCANet-II. Compared with the histogram-based output, the second
order pooling not only provides more discriminative information by preserving
both the magnitude and sign of convolutional responses, but also dramatically
reduces the size of output features. Thus we combine the second order
statistical pooling method with the shallow network, i.e., PCANet. Moreover, it
is easy to combine other discriminative and robust cues by using the second
order pooling. So we introduce the binary feature difference encoding scheme
into our PCANet-II to further improve robustness. Experiments demonstrate the
effectiveness and robustness of our proposed PCANet-II method.
Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning
Authors: Ta-Chung Chi, Po-Chun Chen, Shang-Yu Su, Yun-Nung Chen
Date: 2017-09-30 00:00:00
Proceeding: IJCNLP 2017 11

Language understanding (LU) and dialogue policy learning are two essential
components in conversational systems. Human-human dialogues are not
well-controlled and often random and unpredictable due to their own goals and
speaking habits. This paper proposes a role-based contextual model to consider
different speaker roles independently based on the various speaking patterns in
the multi-turn dialogues. The experiments on the benchmark dataset show that
the proposed role-based model successfully learns role-specific behavioral
patterns for contextual encoding and then significantly improves language
understanding and dialogue policy learning tasks.
Learning to Compose Domain-Specific Transformations for Data Augmentation
Authors: Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré
Date: 2017-09-06 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Data Augmentation, Image Augmentation, Relation Extraction, Text Augmentation

Data augmentation is a ubiquitous technique for increasing the size of
labeled training sets by leveraging task-specific data transformations that
preserve class labels. While it is often easy for domain experts to specify
individual transformations, constructing and tuning the more sophisticated
compositions typically needed to achieve state-of-the-art results is a
time-consuming manual task in practice. We propose a method for automating this
process by learning a generative sequence model over user-specified
transformation functions using a generative adversarial approach. Our method
can make use of arbitrary, non-deterministic transformation functions, is
robust to misspecified user input, and is trained on unlabeled data. The
learned transformation model can then be used to perform data augmentation for
any end discriminative model. In our experiments, we show the efficacy of our
approach on both image and text datasets, achieving improvements of 4.0
accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task,
and 3.4 accuracy points when using domain-specific transformation operations on
a medical imaging dataset as compared to standard heuristic augmentation
approaches.
Deep manifold-to-manifold transforming network for action recognition
Authors: Tong Zhang, Wenming Zheng, Zhen Cui, Chaolong Li
Date: 2017-05-30 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Symmetric positive definite (SPD) matrices (e.g., covariances, graph
Laplacians, etc.) are widely used to model the relationship of spatial or
temporal domain. Nevertheless, SPD matrices are theoretically embedded on
Riemannian manifolds. In this paper, we propose an end-to-end deep
manifold-to-manifold transforming network (DMT-Net) which can make SPD matrices
flow from one Riemannian manifold to another more discriminative one. To learn
discriminative SPD features characterizing both spatial and temporal
dependencies, we specifically develop three novel layers on manifolds: (i) the
local SPD convolutional layer, (ii) the non-linear SPD activation layer, and
(iii) the Riemannian-preserved recursive layer. The SPD property is preserved
through all layers without any requirement of singular value decomposition
(SVD), which is often used in the existing methods with expensive computation
cost. Furthermore, a diagonalizing SPD layer is designed to efficiently
calculate the final metric for the classification task. To evaluate our
proposed method, we conduct extensive experiments on the task of action
recognition, where input signals are popularly modeled as SPD matrices. The
experimental results demonstrate that our DMT-Net is much more competitive over
state-of-the-art.
Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings
Authors: A. K. M. Sabbir, Antonio Jimeno Yepes, Ramakanth Kavuluru
Date: 2016-10-26 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation Extraction, Representation Learning, Word Sense Disambiguation

Biomedical word sense disambiguation (WSD) is an important intermediate task
in many natural language processing applications such as named entity
recognition, syntactic parsing, and relation extraction. In this paper, we
employ knowledge-based approaches that also exploit recent advances in neural
word/concept embeddings to improve over the state-of-the-art in biomedical WSD
using the MSH WSD dataset as the test set. Our methods involve weak supervision
- we do not use any hand-labeled examples for WSD to build our prediction
models; however, we employ an existing well known named entity recognition and
concept mapping program, MetaMap, to obtain our concept vectors. Over the MSH
WSD dataset, our linear time (in terms of numbers of senses and words in the
test instance) method achieves an accuracy of 92.24% which is an absolute 3%
improvement over the best known results obtained via unsupervised or
knowledge-based means. A more expensive approach that we developed relies on a
nearest neighbor framework and achieves an accuracy of 94.34%. Employing dense
vector representations learned from unlabeled free text has been shown to
benefit many language processing tasks recently and our efforts show that
biomedical WSD is no exception to this trend. For a complex and rapidly
evolving domain such as biomedicine, building labeled datasets for larger sets
of ambiguous terms may be impractical. Here, we show that weak supervision that
leverages recent advances in representation learning can rival supervised
approaches in biomedical WSD. However, external knowledge bases (here sense
inventories) play a key role in the improvements achieved.
3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data
Authors: Li Sun, Zhi Yan, Sergi Molina Mellado, Marc Hanheide, Tom Duckett
Date: 2017-09-30 00:00:00
Tasks: Decoder, Human Detection, Pedestrian Trajectory Prediction, Trajectory Prediction

This paper presents a novel 3DOF pedestrian trajectory prediction approach
for autonomous mobile service robots. While most previously reported methods
are based on learning of 2D positions in monocular camera images, our approach
uses range-finder sensors to learn and predict 3DOF pose trajectories (i.e. 2D
position plus 1D rotation within the world coordinate system). Our approach,
T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using
long-term data from real-world robot deployments and aims to learn
context-dependent (environment- and time-specific) human activities. Our
approach incorporates long-term temporal information (i.e. date and time) with
short-term pose observations as input. A sequence-to-sequence LSTM
encoder-decoder is trained, which encodes observations into LSTM and then
decodes as predictions. For deployment, it can perform on-the-fly prediction in
real-time. Instead of using manually annotated data, we rely on a robust human
detection, tracking and SLAM system, providing us with examples in a global
coordinate system. We validate the approach using more than 15K pedestrian
trajectories recorded in a care home environment over a period of three months.
The experiment shows that the proposed T-Pose-LSTM model advances the
state-of-the-art 2D-based method for human trajectory prediction in long-term
mobile robot deployments.