pwc-archive/papers-with-abstracts huggingface dataset:
Formal Hypothesis Tests for Additive Structure in Random Forests
Authors: Lucas Mentch, Giles Hooker
Date: 2014-06-07 00:00:00

While statistical learning methods have proved powerful tools for predictive
modeling, the black-box nature of the models they produce can severely limit
their interpretability and the ability to conduct formal inference. However,
the natural structure of ensemble learners like bagged trees and random forests
has been shown to admit desirable asymptotic properties when base learners are
built with proper subsamples. In this work, we demonstrate that by defining an
appropriate grid structure on the covariate space, we may carry out formal
hypothesis tests for both variable importance and underlying additive model
structure. To our knowledge, these tests represent the first statistical tools
for investigating the underlying regression structure in a context such as
random forests. We develop notions of total and partial additivity and further
demonstrate that testing can be carried out at no additional computational cost
by estimating the variance within the process of constructing the ensemble.
Furthermore, we propose a novel extension of these testing procedures utilizing
random projections in order to allow for computationally efficient testing
procedures that retain high power even when the grid size is much larger than
that of the training set.
Syntactically Informed Text Compression with Recurrent Neural Networks
Authors: David Cox
Date: 2016-08-08 00:00:00
Tasks: Text Compression

We present a self-contained system for constructing natural language models
for use in text compression. Our system improves upon previous neural network
based models by utilizing recent advances in syntactic parsing -- Google's
SyntaxNet -- to augment character-level recurrent neural networks. RNNs have
proven exceptional in modeling sequence data such as text, as their
architecture allows for modeling of long-term contextual information.
Clustering and Community Detection with Imbalanced Clusters
Authors: Cem Aksoylar, Jing Qian, Venkatesh Saligrama
Date: 2016-08-26 00:00:00
Tasks: Clustering, Community Detection, graph partitioning

Spectral clustering methods which are frequently used in clustering and
community detection applications are sensitive to the specific graph
constructions particularly when imbalanced clusters are present. We show that
ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to
imbalanced cluster sizes since they tend to emphasize cut sizes over cut
values. We propose a graph partitioning problem that seeks minimum cut
partitions under minimum size constraints on partitions to deal with imbalanced
cluster sizes. Our approach parameterizes a family of graphs by adaptively
modulating node degrees on a fixed node set, yielding a set of parameter
dependent cuts reflecting varying levels of imbalance. The solution to our
problem is then obtained by optimizing over these parameters. We present
rigorous limit cut analysis results to justify our approach and demonstrate the
superiority of our method through experiments on synthetic and real datasets
for data clustering, semi-supervised learning and community detection.
A Unified Approach for Learning the Parameters of Sum-Product Networks
Authors: Han Zhao, Pascal Poupart, Geoff Gordon
Date: 2016-01-03 00:00:00
Proceeding: NeurIPS 2016 12

We present a unified approach for learning the parameters of Sum-Product
networks (SPNs). We prove that any complete and decomposable SPN is equivalent
to a mixture of trees where each tree corresponds to a product of univariate
distributions. Based on the mixture model perspective, we characterize the
objective function when learning SPNs based on the maximum likelihood
estimation (MLE) principle and show that the optimization problem can be
formulated as a signomial program. We construct two parameter learning
algorithms for SPNs by using sequential monomial approximations (SMA) and the
concave-convex procedure (CCCP), respectively. The two proposed methods
naturally admit multiplicative updates, hence effectively avoiding the
projection operation. With the help of the unified framework, we also show
that, in the case of SPNs, CCCP leads to the same algorithm as Expectation
Maximization (EM) despite the fact that they are different in general.
Leveraging over intact priors for boosting control and dexterity of prosthetic hands by amputees
Authors: Valentina Gregori, Barbara Caputo
Date: 2016-08-26 00:00:00
Tasks: Domain Adaptation

Non-invasive myoelectric prostheses require a long training time to obtain
satisfactory control dexterity. These training times could possibly be reduced
by leveraging over training efforts by previous subjects. So-called domain
adaptation algorithms formalize this strategy and have indeed been shown to
significantly reduce the amount of required training data for intact subjects
for myoelectric movements classification. It is not clear, however, whether
these results extend also to amputees and, if so, whether prior information
from amputees and intact subjects is equally useful. To overcome this problem,
we evaluated several domain adaptation algorithms on data coming from both
amputees and intact subjects. Our findings indicate that: (1) the use of
previous experience from other subjects allows us to reduce the training time
by about an order of magnitude; (2) this improvement holds regardless of
whether an amputee exploits previous information from other amputees or from
intact subjects.
Maximum Correntropy Unscented Filter
Authors: Xi Liu, Badong Chen, Bin Xu, Zongze Wu, Paul Honeine
Date: 2016-08-26 00:00:00
Tasks: State Estimation

The unscented transformation (UT) is an efficient method to solve the state
estimation problem for a non-linear dynamic system, utilizing a derivative-free
higher-order approximation by approximating a Gaussian distribution rather than
approximating a non-linear function. Applying the UT to a Kalman filter type
estimator leads to the well-known unscented Kalman filter (UKF). Although the
UKF works very well in Gaussian noises, its performance may deteriorate
significantly when the noises are non-Gaussian, especially when the system is
disturbed by some heavy-tailed impulsive noises. To improve the robustness of
the UKF against impulsive noises, a new filter for nonlinear systems is
proposed in this work, namely the maximum correntropy unscented filter (MCUF).
In MCUF, the UT is applied to obtain the prior estimates of the state and
covariance matrix, and a robust statistical linearization regression based on
the maximum correntropy criterion (MCC) is then used to obtain the posterior
estimates of the state and covariance. The satisfying performance of the new
algorithm is confirmed by two illustrative examples.
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events
Authors: Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Kai Zhang
Date: 2016-08-26 00:00:00
Tasks: Anomaly Detection

Anomaly detection plays an important role in modern data-driven security
applications, such as detecting suspicious access to a socket from a process.
In many cases, such events can be described as a collection of categorical
values that are considered as entities of different types, which we call
heterogeneous categorical events. Due to the lack of intrinsic distance
measures among entities, and the exponentially large event space, most existing
work relies heavily on heuristics to calculate abnormal scores for events.
Different from previous work, we propose a principled and unified probabilistic
model APE (Anomaly detection via Probabilistic pairwise interaction and Entity
embedding) that directly models the likelihood of events. In this model, we
embed entities into a common latent space using their observed co-occurrence in
different events. More specifically, we first model the compatibility of each
pair of entities according to their embeddings. Then we utilize the weighted
pairwise interactions of different entity types to define the event
probability. Using Noise-Contrastive Estimation with "context-dependent" noise
distribution, our model can be learned efficiently regardless of the large
event space. Experimental results on real enterprise surveillance data show
that our methods can accurately detect abnormal events compared to other
state-of-the-art abnormal detection techniques.
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Authors: Ramesh Nallapati, Bo-Wen Zhou, Cicero Nogueira dos santos, Caglar Gulcehre, Bing Xiang
Date: 2016-02-19 00:00:00
Proceeding: CONLL 2016 8
Tasks: Abstractive Text Summarization, Decoder, Sentence, Sentence Summarization, Summarization

In this work, we model abstractive text summarization using Attentional
Encoder-Decoder Recurrent Neural Networks, and show that they achieve
state-of-the-art performance on two different corpora. We propose several novel
models that address critical problems in summarization that are not adequately
modeled by the basic architecture, such as modeling key-words, capturing the
hierarchy of sentence-to-word structure, and emitting words that are rare or
unseen at training time. Our work shows that many of our proposed models
contribute to further improvement in performance. We also propose a new dataset
consisting of multi-sentence summaries, and establish performance benchmarks
for further research.
Analyzing features learned for Offline Signature Verification using Deep CNNs
Authors: Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Date: 2016-07-15 00:00:00

Research on Offline Handwritten Signature Verification explored a large
variety of handcrafted feature extractors, ranging from graphology, texture
descriptors to interest points. In spite of advancements in the last decades,
performance of such systems is still far from optimal when we test the systems
against skilled forgeries - signature forgeries that target a particular
individual. In previous research, we proposed a formulation of the problem to
learn features from data (signature images) in a Writer-Independent format,
using Deep Convolutional Neural Networks (CNNs), seeking to improve performance
on the task. In this research, we push further the performance of such method,
exploring a range of architectures, and obtaining a large improvement in
state-of-the-art performance on the GPDS dataset, the largest publicly
available dataset on the task. In the GPDS-160 dataset, we obtained an Equal
Error Rate of 2.74%, compared to 6.97% in the best result published in
literature (that used a combination of multiple classifiers). We also present a
visual analysis of the feature space learned by the model, and an analysis of
the errors made by the classifier. Our analysis shows that the model is very
effective in separating signatures that have a different global appearance,
while being particularly vulnerable to forgeries that very closely resemble
genuine signatures, even if their line quality is bad, which is the case of
slowly-traced forgeries.
A Fast Ellipse Detector Using Projective Invariant Pruning
Authors: Qi Jia, Xin Fan, Zhongxuan Luo, Lianbo Song, Tie Qiu
Date: 2016-08-26 00:00:00
Tasks: Robot Navigation

Detecting elliptical objects from an image is a central task in robot
navigation and industrial diagnosis where the detection time is always a
critical issue. Existing methods are hardly applicable to these real-time
scenarios of limited hardware resource due to the huge number of fragment
candidates (edges or arcs) for fitting ellipse equations. In this paper, we
present a fast algorithm detecting ellipses with high accuracy. The algorithm
leverage a newly developed projective invariant to significantly prune the
undesired candidates and to pick out elliptical ones. The invariant is able to
reflect the intrinsic geometry of a planar curve, giving the value of -1 on any
three collinear points and +1 for any six points on an ellipse. Thus, we apply
the pruning and picking by simply comparing these binary values. Moreover, the
calculation of the invariant only involves the determinant of a 3*3 matrix.
Extensive experiments on three challenging data sets with 650 images
demonstrate that our detector runs 20%-50% faster than the state-of-the-art
algorithms with the comparable or higher precision.
Fine Hand Segmentation using Convolutional Neural Networks
Authors: Tadej Vodopivec, Vincent Lepetit, Peter Peer
Date: 2016-08-26 00:00:00
Tasks: Deep Learning, Hand Segmentation

We propose a method for extracting very accurate masks of hands in egocentric
views. Our method is based on a novel Deep Learning architecture: In contrast
with current Deep Learning methods, we do not use upscaling layers applied to a
low-dimensional representation of the input image. Instead, we extract features
with convolutional layers and map them directly to a segmentation mask with a
fully connected layer. We show that this approach, when applied in a
multi-scale fashion, is both accurate and efficient enough for real-time. We
demonstrate it on a new dataset made of images captured in various
environments, from the outdoors to offices.
Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study
Authors: Qin Zou, Zheng Zhang, Qian Wang, Qingquan Li, Long Chen, Song Wang
Date: 2016-08-26 00:00:00
Tasks: General Classification

It is well known that clothing fashion is a distinctive and often habitual
trend in the style in which a person dresses. Clothing fashions are usually
expressed with visual stimuli such as style, color, and texture. However, it is
not clear which visual stimulus places higher/lower influence on the updating
of clothing fashion. In this study, computer vision and machine learning
techniques are employed to analyze the influence of different visual stimuli on
clothing-fashion updates. Specifically, a classification-based model is
proposed to quantify the influence of different visual stimuli, in which each
visual stimulus's influence is quantified by its corresponding accuracy in
fashion classification. Experimental results demonstrate that, on
clothing-fashion updates, the style holds a higher influence than the color,
and the color holds a higher influence than the texture.
Hard Negative Mining for Metric Learning Based Zero-Shot Classification
Authors: Maxime Bucher, Stéphane Herbin, Frédéric Jurie
Date: 2016-08-26 00:00:00
Tasks: Classification, Domain Adaptation, General Classification, Metric Learning, zero-shot-classification, Zero-Shot Learning

Zero-Shot learning has been shown to be an efficient strategy for domain
adaptation. In this context, this paper builds on the recent work of Bucher et
al. [1], which proposed an approach to solve Zero-Shot classification problems
(ZSC) by introducing a novel metric learning based objective function. This
objective function allows to learn an optimal embedding of the attributes
jointly with a measure of similarity between images and attributes. This paper
extends their approach by proposing several schemes to control the generation
of the negative pairs, resulting in a significant improvement of the
performance and giving above state-of-the-art results on three challenging ZSC
datasets.
Activity Networks with Delays An application to toxicity analysis
Authors: Franck Delaplace, Cinzia Di Giusto, Jean-Louis Giavitto, Hanna Klaudel
Date: 2016-08-26 00:00:00
Tasks: Attribute

ANDy , Activity Networks with Delays, is a discrete time framework aimed at
the qualitative modelling of time-dependent activities. The modular and concise
syntax makes ANDy suitable for an easy and natural modelling of time-dependent
biological systems (i.e., regulatory pathways). Activities involve entities
playing the role of activators, inhibitors or products of biochemical network
operation. Activities may have given duration, i.e., the time required to
obtain results. An entity may represent an object (e.g., an agent, a
biochemical species or a family of thereof) with a local attribute, a state
denoting its level (e.g., concentration, strength). Entities levels may change
as a result of an activity or may decay gradually as time passes by. The
semantics of ANDy is formally given via high-level Petri nets ensuring this way
some modularity. As main results we show that ANDy systems have finite state
representations even for potentially infinite processes and it well adapts to
the modelling of toxic behaviours. As an illustration, we present a
classification of toxicity properties and give some hints on how they can be
verified with existing tools on ANDy systems. A small case study on blood
glucose regulation is provided to exemplify the ANDy framework and the toxicity
properties.
Variational methods for Conditional Multimodal Deep Learning
Authors: Gaurav Pandey, Ambedkar Dukkipati
Date: 2016-03-06 00:00:00
Tasks: Deep Learning, Multimodal Deep Learning

In this paper, we address the problem of conditional modality learning,
whereby one is interested in generating one modality given the other. While it
is straightforward to learn a joint distribution over multiple modalities using
a deep multimodal architecture, we observe that such models aren't very
effective at conditional generation. Hence, we address the problem by learning
conditional distributions between the modalities. We use variational methods
for maximizing the corresponding conditional log-likelihood. The resultant deep
model, which we refer to as conditional multimodal autoencoder (CMMA), forces
the latent representation obtained from a single modality alone to be `close'
to the joint representation obtained from multiple modalities. We use the
proposed model to generate faces from attributes. We show that the faces
generated from attributes using the proposed model, are qualitatively and
quantitatively more representative of the attributes from which they were
generated, than those obtained by other deep generative models. We also propose
a secondary task, whereby the existing faces are modified by modifying the
corresponding attributes. We observe that the modifications in face introduced
by the proposed model are representative of the corresponding modifications in
attributes.
An Octree-Based Approach towards Efficient Variational Range Data Fusion
Authors: Wadim Kehl, Tobias Holl, Federico Tombari, Slobodan Ilic, Nassir Navab
Date: 2016-08-26 00:00:00

Volume-based reconstruction is usually expensive both in terms of memory
consumption and runtime. Especially for sparse geometric structures, volumetric
representations produce a huge computational overhead. We present an efficient
way to fuse range data via a variational Octree-based minimization approach by
taking the actual range data geometry into account. We transform the data into
Octree-based truncated signed distance fields and show how the optimization can
be conducted on the newly created structures. The main challenge is to uphold
speed and a low memory footprint without sacrificing the solutions' accuracy
during optimization. We explain how to dynamically adjust the optimizer's
geometric structure via joining/splitting of Octree nodes and how to define the
operators. We evaluate on various datasets and outline the suitability in terms
of performance and geometric accuracy.
Proceedings First Workshop on Causal Reasoning for Embedded and safety-critical Systems Technologies
Authors: Gregor Gössler, Oleg Sokolsky
Date: 2016-08-26 00:00:00
Tasks: Fault localization, Legal Reasoning

Formal approaches for automated causality analysis, fault localization,
explanation of events, accountability and blaming have been proposed
independently by several communities --- in particular, AI, concurrency,
model-based diagnosis, formal methods. Work on these topics has significantly
gained speed during the last years. The goals of CREST are to bring together
and foster exchange between researchers from the different communities, and to
present and discuss recent advances and new ideas in the field.
  The workshop program consisted of a set of invited and contributed
presentations that illustrate different techniques for, and applications of,
causality analysis and fault localization.
  The program was anchored by two keynote talks. The keynote by Hana Chockler
(King's College) provided a broad perspective on the application of causal
reasoning based on Halpern and Pearl's definitions of actual causality to a
variety of application domains ranging from formal verification to legal
reasoning. The keynote by Chao Wang (Virginia Tech) concentrated on
constraint-based analysis techniques for debugging and verifying concurrent
programs.
  Workshop papers deal with compositional causality analysis and a wide
spectrum of application for causal reasoning, such as debugging of
probabilistic models, accountability and responsibility, hazard analysis in
practice based on Lewis' counterfactuals, and fault localization and repair.
On the Robustness of Decision Tree Learning under Label Noise
Authors: Aritra Ghosh, Naresh Manwani, P. S. Sastry
Date: 2016-05-20 00:00:00

In most practical problems of classifier learning, the training data suffers
from the label noise. Hence, it is important to understand how robust is a
learning algorithm to such label noise. This paper presents some theoretical
analysis to show that many popular decision tree algorithms are robust to
symmetric label noise under large sample size. We also present some sample
complexity results which provide some bounds on the sample size for the
robustness to hold with a high probability. Through extensive simulations we
illustrate this robustness.
Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals
Authors: Jen-Yu Liu, Shyh-Kang Jeng, Yi-Hsuan Yang
Date: 2016-08-26 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Music Tagging

Recent years have witnessed an increased interest in the application of
persistent homology, a topological tool for data analysis, to machine learning
problems. Persistent homology is known for its ability to numerically
characterize the shapes of spaces induced by features or functions. On the
other hand, deep neural networks have been shown effective in various tasks. To
our best knowledge, however, existing neural network models seldom exploit
shape information. In this paper, we investigate a way to use persistent
homology in the framework of deep neural networks. Specifically, we propose to
embed the so-called "persistence landscape," a rather new topological summary
for data, into a convolutional neural network (CNN) for dealing with audio
signals. Our evaluation on automatic music tagging, a multi-label
classification task, shows that the resulting persistent convolutional neural
network (PCNN) model can perform significantly better than state-of-the-art
models in prediction accuracy. We also discuss the intuition behind the design
of the proposed model, and offer insights into the features that it learns.
The Grail theorem prover: Type theory for syntax and semantics
Authors: Richard Moot
Date: 2016-02-02 00:00:00
Tasks: Vocal Bursts Type Prediction

As the name suggests, type-logical grammars are a grammar formalism based on
logic and type theory. From the prespective of grammar design, type-logical
grammars develop the syntactic and semantic aspects of linguistic phenomena
hand-in-hand, letting the desired semantics of an expression inform the
syntactic type and vice versa. Prototypical examples of the successful
application of type-logical grammars to the syntax-semantics interface include
coordination, quantifier scope and extraction.This chapter describes the Grail
theorem prover, a series of tools for designing and testing grammars in various
modern type-logical grammars which functions as a tool . All tools described in
this chapter are freely available.