pwc-archive/papers-with-abstracts huggingface dataset:
Linear Time Clustering for High Dimensional Mixtures of Gaussian Clouds
Authors: Dan Kushnir, Shirin Jalali, Iraj Saniee
Date: 2017-12-19 00:00:00
Tasks: Clustering, Computational Efficiency, Vocal Bursts Intensity Prediction

Clustering mixtures of Gaussian distributions is a fundamental and
challenging problem that is ubiquitous in various high-dimensional data
processing tasks. While state-of-the-art work on learning Gaussian mixture
models has focused primarily on improving separation bounds and their
generalization to arbitrary classes of mixture models, less emphasis has been
paid to practical computational efficiency of the proposed solutions. In this
paper, we propose a novel and highly efficient clustering algorithm for $n$
points drawn from a mixture of two arbitrary Gaussian distributions in
$\mathbb{R}^p$. The algorithm involves performing random 1-dimensional
projections until a direction is found that yields a user-specified clustering
error $e$. For a 1-dimensional separation parameter $\gamma$ satisfying
$\gamma=Q^{-1}(e)$, the expected number of such projections is shown to be
bounded by $o(\ln p)$, when $\gamma$ satisfies $\gamma\leq
c\sqrt{\ln{\ln{p}}}$, with $c$ as the separability parameter of the two
Gaussians in $\mathbb{R}^p$. Consequently, the expected overall running time of
the algorithm is linear in $n$ and quasi-linear in $p$ at $o(\ln{p})O(np)$, and
the sample complexity is independent of $p$. This result stands in contrast to
prior works which provide polynomial, with at-best quadratic, running time in
$p$ and $n$. We show that our bound on the expected number of 1-dimensional
projections extends to the case of three or more Gaussian components, and we
present a generalization of our results to mixture distributions beyond the
Gaussian model.
AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video
Authors: Nancy Xin Ru Wang, Ali Farhadi, Rajesh Rao, Bingni Brunton
Date: 2017-09-13 00:00:00
Tasks: Future prediction, Multimodal Deep Learning

Developing useful interfaces between brains and machines is a grand challenge
of neuroengineering. An effective interface has the capacity to not only
interpret neural signals, but predict the intentions of the human to perform an
action in the near future; prediction is made even more challenging outside
well-controlled laboratory experiments. This paper describes our approach to
detect and to predict natural human arm movements in the future, a key
challenge in brain computer interfacing that has never before been attempted.
We introduce the novel Annotated Joints in Long-term ECoG (AJILE) dataset;
AJILE includes automatically annotated poses of 7 upper body joints for four
human subjects over 670 total hours (more than 72 million frames), along with
the corresponding simultaneously acquired intracranial neural recordings. The
size and scope of AJILE greatly exceeds all previous datasets with movements
and electrocorticography (ECoG), making it possible to take a deep learning
approach to movement prediction. We propose a multimodal model that combines
deep convolutional neural networks (CNN) with long short-term memory (LSTM)
blocks, leveraging both ECoG and video modalities. We demonstrate that our
models are able to detect movements and predict future movements up to 800 msec
before movement initiation. Further, our multimodal movement prediction models
exhibit resilience to simulated ablation of input neural signals. We believe a
multimodal approach to natural neural decoding that takes context into account
is critical in advancing bioelectronic technologies and human neuroscience.
Correlation-Based Method for Sentiment Classification
Authors: Hussam Hamdan
Date: 2016-10-10 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

The classic supervised classification algorithms are efficient, but
time-consuming, complicated and not interpretable, which makes it difficult to
analyze their results that limits the possibility to improve them based on real
observations. In this paper, we propose a new and a simple classifier to
predict a sentiment label of a short text. This model keeps the capacity of
human interpret-ability and can be extended to integrate NLP techniques in a
more interpretable way. Our model is based on a correlation metric which
measures the degree of association between a sentiment label and a word. Ten
correlation metrics are proposed and evaluated intrinsically. And then a
classifier based on each metric is proposed, evaluated and compared to the
classic classification algorithms which have proved their performance in many
studies. Our model outperforms these algorithms with several correlation
metrics.
First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time
Authors: Yi Xu, Rong Jin, Tianbao Yang
Date: 2017-11-03 00:00:00
Proceeding: NeurIPS 2018 12

Two classes of methods have been proposed for escaping from saddle points
with one using the second-order information carried by the Hessian and the
other adding the noise into the first-order information. The existing analysis
for algorithms using noise in the first-order information is quite involved and
hides the essence of added noise, which hinder further improvements of these
algorithms. In this paper, we present a novel perspective of noise-adding
technique, i.e., adding the noise into the first-order information can help
extract the negative curvature from the Hessian matrix, and provide a formal
reasoning of this perspective by analyzing a simple first-order procedure. More
importantly, the proposed procedure enables one to design purely first-order
stochastic algorithms for escaping from non-degenerate saddle points with a
much better time complexity (almost linear time in terms of the problem's
dimensionality). In particular, we develop a {\bf first-order stochastic
algorithm} based on our new technique and an existing algorithm that only
converges to a first-order stationary point to enjoy a time complexity of
{$\widetilde O(d/\epsilon^{3.5})$ for finding a nearly second-order stationary
point $\bf{x}$ such that $\|\nabla F(bf{x})\|\leq \epsilon$ and $\nabla^2
F(bf{x})\geq -\sqrt{\epsilon}I$ (in high probability), where $F(\cdot)$ denotes
the objective function and $d$ is the dimensionality of the problem. To the
best of our knowledge, this is the best theoretical result of first-order
algorithms for stochastic non-convex optimization, which is even competitive
with if not better than existing stochastic algorithms hinging on the
second-order information.
Learning Integral Representations of Gaussian Processes
Authors: Zilong Tan, Sayan Mukherjee
Date: 2018-02-21 00:00:00
Tasks: Dimensionality Reduction, Gaussian Processes, regression

We propose a representation of Gaussian processes (GPs) based on powers of
the integral operator defined by a kernel function, we call these stochastic
processes integral Gaussian processes (IGPs). Sample paths from IGPs are
functions contained within the reproducing kernel Hilbert space (RKHS) defined
by the kernel function, in contrast sample paths from the standard GP are not
functions within the RKHS. We develop computationally efficient non-parametric
regression models based on IGPs. The main innovation in our regression
algorithm is the construction of a low dimensional subspace that captures the
information most relevant to explaining variation in the response. We use ideas
from supervised dimension reduction to compute this subspace. The result of
using the construction we propose involves significant improvements in the
computational complexity of estimating kernel hyper-parameters as well as
reducing the prediction variance.
Brain Tumor Type Classification via Capsule Networks
Authors: Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis
Date: 2018-02-27 00:00:00
Tasks: Brain Tumor Classification, Classification, General Classification, Vocal Bursts Type Prediction

Brain tumor is considered as one of the deadliest and most common form of
cancer both in children and in adults. Consequently, determining the correct
type of brain tumor in early stages is of significant importance to devise a
precise treatment plan and predict patient's response to the adopted treatment.
In this regard, there has been a recent surge of interest in designing
Convolutional Neural Networks (CNNs) for the problem of brain tumor type
classification. However, CNNs typically require large amount of training data
and can not properly handle input transformations. Capsule networks (referred
to as CapsNets) are brand new machine learning architectures proposed very
recently to overcome these shortcomings of CNNs, and posed to revolutionize
deep learning solutions. Of particular interest to this work is that Capsule
networks are robust to rotation and affine transformation, and require far less
training data, which is the case for processing medical image datasets
including brain Magnetic Resonance Imaging (MRI) images. In this paper, we
focus to achieve the following four objectives: (i) Adopt and incorporate
CapsNets for the problem of brain tumor classification to design an improved
architecture which maximizes the accuracy of the classification problem at
hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set
of MRI images; (iii) Explore whether or not CapsNets are capable of providing
better fit for the whole brain images or just the segmented tumor, and; (iv)
Develop a visualization paradigm for the output of the CapsNet to better
explain the learned features. Our results show that the proposed approach can
successfully overcome CNNs for the brain tumor classification problem.
Evaluating Overfit and Underfit in Models of Network Community Structure
Authors: Amir Ghasemian, Homa Hosseinmardi, Aaron Clauset
Date: 2018-02-28 00:00:00
Tasks: Community Detection, Diagnostic, Link Prediction, Stochastic Block Model

A common data mining task on networks is community detection, which seeks an
unsupervised decomposition of a network into structural groups based on
statistical regularities in the network's connectivity. Although many methods
exist, the No Free Lunch theorem for community detection implies that each
makes some kind of tradeoff, and no algorithm can be optimal on all inputs.
Thus, different algorithms will over or underfit on different inputs, finding
more, fewer, or just different communities than is optimal, and evaluation
methods that use a metadata partition as a ground truth will produce misleading
conclusions about general accuracy. Here, we present a broad evaluation of over
and underfitting in community detection, comparing the behavior of 16
state-of-the-art community detection algorithms on a novel and structurally
diverse corpus of 406 real-world networks. We find that (i) algorithms vary
widely both in the number of communities they find and in their corresponding
composition, given the same input, (ii) algorithms can be clustered into
distinct high-level groups based on similarities of their outputs on real-world
networks, and (iii) these differences induce wide variation in accuracy on link
prediction and link description tasks. We introduce a new diagnostic for
evaluating overfitting and underfitting in practice, and use it to roughly
divide community detection methods into general and specialized learning
algorithms. Across methods and inputs, Bayesian techniques based on the
stochastic block model and a minimum description length approach to
regularization represent the best general learning approach, but can be
outperformed under specific circumstances. These results introduce both a
theoretically principled approach to evaluate over and underfitting in models
of network community structure and a realistic benchmark by which new methods
may be evaluated and compared.
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis
Authors: Arjuna Flenner, Lawrence Peterson, Jason Bunk, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, B. S. Manjunath
Date: 2018-03-01 00:00:00
Tasks: Deep Learning, Two-sample testing

The amount of digital imagery recorded has recently grown exponentially, and
with the advancement of software, such as Photoshop or Gimp, it has become
easier to manipulate images. However, most images on the internet have not been
manipulated and any automated manipulation detection algorithm must carefully
control the false alarm rate. In this paper we discuss a method to
automatically detect local resampling using deep learning while controlling the
false alarm rate using a-contrario analysis. The automated procedure consists
of three primary steps. First, resampling features are calculated for image
blocks. A deep learning classifier is then used to generate a heatmap that
indicates if the image block has been resampled. We expect some of these blocks
to be falsely identified as resampled. We use a-contrario hypothesis testing to
both identify if the patterns of the manipulated blocks indicate if the image
has been tampered with and to localize the manipulation. We demonstrate that
this strategy is effective in indicating if an image has been manipulated and
localizing the manipulations.
Re-examination of Bregman functions and new properties of their divergences
Authors: Daniel Reem, Simeon Reich, Alvaro De Pierro
Date: 2018-03-01 00:00:00

The Bregman divergence (Bregman distance, Bregman measure of distance) is a
certain useful substitute for a distance, obtained from a well-chosen function
(the "Bregman function"). Bregman functions and divergences have been
extensively investigated during the last decades and have found applications in
optimization, operations research, information theory, nonlinear analysis,
machine learning and more. This paper re-examines various aspects related to
the theory of Bregman functions and divergences. In particular, it presents
many sufficient conditions which allow the construction of Bregman functions in
a general setting and introduces new Bregman functions (such as a negative
iterated log entropy). Moreover, it sheds new light on several known Bregman
functions such as quadratic entropies, the negative Havrda-Charv\'at-Tsallis
entropy, and the negative Boltzmann-Gibbs-Shannon entropy, and it shows that
the negative Burg entropy, which is not a Bregman function according to the
classical theory but nevertheless is known to have "Bregmanian properties",
can, by our re-examination of the theory, be considered as a Bregman function.
Our analysis yields several by-products of independent interest such as the
introduction of the concept of relative uniform convexity (a certain
generalization of uniform convexity), new properties of uniformly and strongly
convex functions, and results in Banach space theory.
Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints
Authors: Pan Li, Baihong Jin, Dai Wang, Baosen Zhang
Date: 2017-04-28 00:00:00

Voltage control plays an important role in the operation of electricity
distribution networks, especially with high penetration of distributed energy
resources. These resources introduce significant and fast varying
uncertainties. In this paper, we focus on reactive power compensation to
control voltage in the presence of uncertainties. We adopt a chance constraint
approach that accounts for arbitrary correlations between renewable resources
at each of the buses. We show how the problem can be solved efficiently using
historical samples via a stochastic quasi gradient method. We also show that
this optimization problem is convex for a wide variety of probabilistic
distributions. Compared to conventional per-bus chance constraints, our
formulation is more robust to uncertainty and more computationally tractable.
We illustrate the results using standard IEEE distribution test feeders.
SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation
Authors: Wenqiang Xu, Yonglu Li, Cewu Lu
Date: 2018-01-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Adaptation, Instance Segmentation, Segmentation, Semantic Segmentation

Instance segmentation is a problem of significance in computer vision.
However, preparing annotated data for this task is extremely time-consuming and
costly. By combining the advantages of 3D scanning, reasoning, and GAN-based
domain adaptation techniques, we introduce a novel pipeline named SRDA to
obtain large quantities of training samples with very minor effort. Our
pipeline is well-suited to scenes that can be scanned, i.e. most indoor and
some outdoor scenarios. To evaluate our performance, we build three
representative scenes and a new dataset, with 3D models of various common
objects categories and annotated real-world scene images. Extensive experiments
show that our pipeline can achieve decent instance segmentation performance
given very low human labor cost.
Moderate Environmental Variation Promotes the Evolution of Robust Solutions
Authors: Nicola Milano, Jônata Tyska Carvalho, Stefano Nolfi
Date: 2017-10-22 00:00:00

Previous evolutionary studies demonstrated how evaluating evolving agents in
variable environmental conditions enable them to develop solutions that are
robust to environmental variation. We demonstrate how the robustness of the
agents can be further improved by exposing them also to environmental
variations throughout generations. These two types of environmental variations
play partially distinct roles as demonstrated by the fact that agents evolved
in environments that do not vary throughout generations display lower
performance than agents evolved in varying environments independently from the
amount of environmental variation experienced during evaluation. Moreover, our
results demonstrate that performance increases when the amount of variations
introduced during agents evaluation and the rate at which the environment
varies throughout generations are moderate. This is explained by the fact that
the probability to retain genetic variations, including non-neutral variations
that alter the behavior of the agents, increases when the environment varies
throughout generations but also when new environmental conditions persist over
time long enough to enable genetic accommodation.
Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo
Authors: Mitsuru Igami
Date: 2017-10-30 00:00:00
Tasks: Econometrics, Reinforcement Learning, Reinforcement Learning (RL)

Artificial intelligence (AI) has achieved superhuman performance in a growing
number of tasks, but understanding and explaining AI remain challenging. This
paper clarifies the connections between machine-learning algorithms to develop
AIs and the econometrics of dynamic structural models through the case studies
of three famous game AIs. Chess-playing Deep Blue is a calibrated value
function, whereas shogi-playing Bonanza is an estimated value function via
Rust's (1987) nested fixed-point method. AlphaGo's "supervised-learning policy
network" is a deep neural network implementation of Hotz and Miller's (1993)
conditional choice probability estimation; its "reinforcement-learning value
network" is equivalent to Hotz, Miller, Sanders, and Smith's (1994) conditional
choice simulation method. Relaxing these AIs' implicit econometric assumptions
would improve their structural interpretability.
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
Authors: Haowen Deng, Tolga Birdal, Slobodan Ilic
Date: 2018-02-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: Point Cloud Registration

We present PPFNet - Point Pair Feature NETwork for deeply learning a globally
informed 3D local feature descriptor to find correspondences in unorganized
point clouds. PPFNet learns local descriptors on pure geometry and is highly
aware of the global context, an important cue in deep learning. Our 3D
representation is computed as a collection of point-pair-features combined with
the points and normals within a local vicinity. Our permutation invariant
network design is inspired by PointNet and sets PPFNet to be ordering-free. As
opposed to voxelization, our method is able to consume raw point clouds to
exploit the full sparsity. PPFNet uses a novel $\textit{N-tuple}$ loss and
architecture injecting the global information naturally into the local
descriptor. It shows that context awareness also boosts the local feature
representation. Qualitative and quantitative evaluations of our network suggest
increased recall, improved robustness and invariance as well as a vital step in
the 3D descriptor extraction performance.
Text Annotation Graphs: Annotating Complex Natural Language Phenomena
Authors: Angus G. Forbes, Kristine Lee, Gus Hahn-Powell, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
Date: 2017-11-01 00:00:00
Proceeding: LREC 2018 5
Tasks: Articles, Event Extraction, TAG, text annotation

This paper introduces a new web-based software tool for annotating text, Text
Annotation Graphs, or TAG. It provides functionality for representing complex
relationships between words and word phrases that are not available in other
software tools, including the ability to define and visualize relationships
between the relationships themselves (semantic hypergraphs). Additionally, we
include an approach to representing text annotations in which annotation
subgraphs, or semantic summaries, are used to show relationships outside of the
sequential context of the text itself. Users can use these subgraphs to quickly
find similar structures within the current document or external annotated
documents. Initially, TAG was developed to support information extraction tasks
on a large database of biomedical articles. However, our software is flexible
enough to support a wide range of annotation tasks for any domain. Examples are
provided that showcase TAG's capabilities on morphological parsing and event
extraction tasks. The TAG software is available at: https://github.com/
CreativeCodingLab/TextAnnotationGraphs.
Semi-Supervised Online Structure Learning for Composite Event Recognition
Authors: Evangelos Michelioudakis, Alexander Artikis, Georgios Paliouras
Date: 2018-03-01 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Missing Labels, Relational Reasoning

Online structure learning approaches, such as those stemming from Statistical
Relational Learning, enable the discovery of complex relations in noisy data
streams. However, these methods assume the existence of fully-labelled training
data, which is unrealistic for most real-world applications. We present a novel
approach for completing the supervision of a semi-supervised structure learning
task. We incorporate graph-cut minimisation, a technique that derives labels
for unlabelled data, based on their distance to their labelled counterparts. In
order to adapt graph-cut minimisation to first order logic, we employ a
suitable structural distance for measuring the distance between sets of logical
atoms. The labelling process is achieved online (single-pass) by means of a
caching mechanism and the Hoeffding bound, a statistical tool to approximate
globally-optimal decisions from locally-optimal ones. We evaluate our approach
on the task of composite event recognition by using a benchmark dataset for
human activity recognition, as well as a real dataset for maritime monitoring.
The evaluation suggests that our approach can effectively complete the missing
labels and eventually, improve the accuracy of the underlying structure
learning system.