pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks
Authors: Aditya Gudimella, Ross Story, Matineh Shaker, Ruofan Kong, Matthew Brown, Victor Shnayder, Marcos Campos
Date: 2017-09-20 00:00:00
Tasks: Deep Reinforcement Learning, MuJoCo, Problem Decomposition, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning yields great results for a large array of
problems, but models are generally retrained anew for each new problem to be
solved. Prior learning and knowledge are difficult to incorporate when training
new models, requiring increasingly longer training as problems become more
complex. This is especially problematic for problems with sparse rewards. We
provide a solution to these problems by introducing Concept Network
Reinforcement Learning (CNRL), a framework which allows us to decompose
problems using a multi-level hierarchy. Concepts in a concept network are
reusable, and flexible enough to encapsulate feature extractors, skills, or
other concept networks. With this hierarchical learning approach, deep
reinforcement learning can be used to solve complex tasks in a modular way,
through problem decomposition. We demonstrate the strength of CNRL by training
a model to grasp a rectangular prism and precisely stack it on top of a cube
using a gripper on a Kinova JACO arm, simulated in MuJoCo. Our experiments show
that our use of hierarchy results in a 45x reduction in environment
interactions compared to the state-of-the-art on this task.
Jointly Optimizing Placement and Inference for Beacon-based Localization
Authors: Charles Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter
Date: 2017-03-24 00:00:00

The ability of robots to estimate their location is crucial for a wide
variety of autonomous operations. In settings where GPS is unavailable,
measurements of transmissions from fixed beacons provide an effective means of
estimating a robot's location as it navigates. The accuracy of such a
beacon-based localization system depends both on how beacons are distributed in
the environment, and how the robot's location is inferred based on noisy and
potentially ambiguous measurements. We propose an approach for making these
design decisions automatically and without expert supervision, by explicitly
searching for the placement and inference strategies that, together, are
optimal for a given environment. Since this search is computationally
expensive, our approach encodes beacon placement as a differential neural layer
that interfaces with a neural network for inference. This formulation allows us
to employ standard techniques for training neural networks to carry out the
joint optimization. We evaluate this approach on a variety of environments and
settings, and find that it is able to discover designs that enable high
localization accuracy.
Spatial features of synaptic adaptation affecting learning performance
Authors: Damian L. Berger, Lucilla de Arcangelis, Hans J. Herrmann
Date: 2017-09-20 00:00:00

Recent studies have proposed that the diffusion of messenger molecules, such
as monoamines, can mediate the plastic adaptation of synapses in supervised
learning of neural networks. Based on these findings we developed a model for
neural learning, where the signal for plastic adaptation is assumed to
propagate through the extracellular space. We investigate the conditions
allowing learning of Boolean rules in a neural network. Even fully excitatory
networks show very good learning performances. Moreover, the investigation of
the plastic adaptation features optimizing the performance suggests that
learning is very sensitive to the extent of the plastic adaptation and the
spatial range of synaptic connections.
A Functional Regression approach to Facial Landmark Tracking
Authors: Enrique Sánchez-Lozano, Georgios Tzimiropoulos, Brais Martinez, Fernando de la Torre, Michel Valstar
Date: 2016-12-07 00:00:00
Tasks: Face Detection, Incremental Learning, Landmark Tracking, regression

Linear regression is a fundamental building block in many face detection and
tracking algorithms, typically used to predict shape displacements from image
features through a linear mapping. This paper presents a Functional Regression
solution to the least squares problem, which we coin Continuous Regression,
resulting in the first real-time incremental face tracker. Contrary to prior
work in Functional Regression, in which B-splines or Fourier series were used,
we propose to approximate the input space by its first-order Taylor expansion,
yielding a closed-form solution for the continuous domain of displacements. We
then extend the continuous least squares problem to correlated variables, and
demonstrate the generalisation of our approach. We incorporate Continuous
Regression into the cascaded regression framework, and show its computational
benefits for both training and testing. We then present a fast approach for
incremental learning within Cascaded Continuous Regression, coined iCCR, and
show that its complexity allows real-time face tracking, being 20 times faster
than the state of the art. To the best of our knowledge, this is the first
incremental face tracker that is shown to operate in real-time. We show that
iCCR achieves state-of-the-art performance on the 300-VW dataset, the most
recent, large-scale benchmark for face tracking.
Anthropic decision theory
Authors: Stuart Armstrong
Date: 2011-10-28 00:00:00

This paper sets out to resolve how agents ought to act in the Sleeping Beauty
problem and various related anthropic (self-locating belief) problems, not
through the calculation of anthropic probabilities, but through finding the
correct decision to make. It creates an anthropic decision theory (ADT) that
decides these problems from a small set of principles. By doing so, it
demonstrates that the attitude of agents with regards to each other (selfish or
altruistic) changes the decisions they reach, and that it is very important to
take this into account. To illustrate ADT, it is then applied to two major
anthropic problems and paradoxes, the Presumptuous Philosopher and Doomsday
problems, thus resolving some issues about the probability of human extinction.
Constructing a Hierarchical User Interest Structure based on User Profiles
Authors: Chao Zhao, Min Zhao, Yi Guan
Date: 2017-09-20 00:00:00

The interests of individual internet users fall into a hierarchical structure
which is useful in regards to building personalized searches and
recommendations. Most studies on this subject construct the interest hierarchy
of a single person from the document perspective. In this study, we constructed
the user interest hierarchy via user profiles. We organized 433,397 user
interests, referred to here as "attentions", into a user attention network
(UAN) from 200 million user profiles; we then applied the Louvain algorithm to
detect hierarchical clusters in these attentions. Finally, a 26-level hierarchy
with 34,676 clusters was obtained. We found that these attention clusters were
aggregated according to certain topics as opposed to the hyponymy-relation
based conceptual ontologies. The topics can be entities or concepts, and the
relations were not restrained by hyponymy. The concept relativity encapsulated
in the user's interest can be captured by labeling the attention clusters with
corresponding concepts.
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Authors: Chao Zhao, Jingchi Jiang, Yi Guan
Date: 2017-09-20 00:00:00
Tasks: Representation Learning

Objective: Electronic medical records (EMRs) contain an amount of medical
knowledge which can be used for clinical decision support (CDS). Our objective
is a general system that can extract and represent these knowledge contained in
EMRs to support three CDS tasks: test recommendation, initial diagnosis, and
treatment plan recommendation, with the given condition of one patient.
Methods: We extracted four kinds of medical entities from records and
constructed an EMR-based medical knowledge network (EMKN), in which nodes are
entities and edges reflect their co-occurrence in a single record. Three
bipartite subgraphs (bi-graphs) were extracted from the EMKN to support each
task. One part of the bi-graph was the given condition (e.g., symptoms), and
the other was the condition to be inferred (e.g., diseases). Each bi-graph was
regarded as a Markov random field to support the inference. Three lazy energy
functions and one parameter-based energy function were proposed, as well as two
knowledge representation learning-based energy functions, which can provide a
distributed representation of medical entities. Three measures were utilized
for performance evaluation. Results: On the initial diagnosis task, 80.11% of
the test records identified at least one correct disease from top 10
candidates. Test and treatment recommendation results were 87.88% and 92.55%,
respectively. These results altogether indicate that the proposed system
outperformed the baseline methods. The distributed representation of medical
entities does reflect similarity relationships in regards to knowledge level.
Conclusion: Combining EMKN and MRF is an effective approach for general medical
knowledge representation and inference. Different tasks, however, require
designing their energy functions individually.
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]
Authors: Simon Razniewski, Vevake Balaraman, Werner Nutt
Date: 2017-09-20 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Text Generation

In knowledge bases such as Wikidata, it is possible to assert a large set of
properties for entities, ranging from generic ones such as name and place of
birth to highly profession-specific or background-specific ones such as
doctoral advisor or medical condition. Determining a preference or ranking in
this large set is a challenge in tasks such as prioritisation of edits or
natural-language generation. Most previous approaches to ranking knowledge base
properties are purely data-driven, that is, as we show, mistake frequency for
interestingness.
  In this work, we have developed a human-annotated dataset of 350 preference
judgments among pairs of knowledge base properties for fixed entities. From
this set, we isolate a subset of pairs for which humans show a high level of
agreement (87.5% on average). We show, however, that baseline and
state-of-the-art techniques achieve only 61.3% precision in predicting human
preferences for this subset.
  We then analyze what contributes to one property being rated as more
important than another one, and identify that at least three factors play a
role, namely (i) general frequency, (ii) applicability to similar entities and
(iii) semantic similarity between property and entity. We experimentally
analyze the contribution of each factor and show that a combination of
techniques addressing all the three factors achieves 74% precision on the task.
  The dataset is available at
www.kaggle.com/srazniewski/wikidatapropertyranking.
A minimax and asymptotically optimal algorithm for stochastic bandits
Authors: Pierre Ménard, Aurélien Garivier
Date: 2017-02-23 00:00:00

We propose the kl-UCB ++ algorithm for regret minimization in stochastic
bandit models with exponential families of distributions. We prove that it is
simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower
bound) and minimax optimal. This is the first algorithm proved to enjoy these
two properties at the same time. This work thus merges two different lines of
research with simple and clear proofs.
Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens
Authors: Philip J. Corr, Guenole C. Silvestre, Chris J. Bleakley
Date: 2017-09-20 00:00:00
Tasks: Gesture Recognition, Handwriting Recognition

This paper presents an evaluation of deep neural networks for recognition of
digits entered by users on a smartphone touchscreen. A new large dataset of
Arabic numerals was collected for training and evaluation of the network. The
dataset consists of spatial and temporal touch data recorded for 80 digits
entered by 260 users. Two neural network models were investigated. The first
model was a 2D convolutional neural (ConvNet) network applied to bitmaps of the
glpyhs created by interpolation of the sensed screen touches and its topology
is similar to that of previously published models for offline handwriting
recognition from scanned images. The second model used a 1D ConvNet
architecture but was applied to the sequence of polar vectors connecting the
touch points. The models were found to provide accuracies of 98.50% and 95.86%,
respectively. The second model was much simpler, providing a reduction in the
number of parameters from 1,663,370 to 287,690. The dataset has been made
available to the community as an open source resource.
Learning quadrangulated patches for 3D shape parameterization and completion
Authors: Kripasindhu Sarkar, Kiran varanasi, Didier Stricker
Date: 2017-09-20 00:00:00
Tasks: Denoising, Dictionary Learning

We propose a novel 3D shape parameterization by surface patches, that are
oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail
on local patches, we learn a patch dictionary that identifies principal surface
features of the shape. Unlike previous methods, we are able to encode surface
patches of variable size as determined by the user. We propose novel methods
for dictionary learning and patch reconstruction based on the query of a noisy
input patch with holes. We evaluate the patch dictionary towards various
applications in 3D shape inpainting, denoising and compression. Our method is
able to predict missing vertices and inpaint moderately sized holes. We
demonstrate a complete pipeline for reconstructing the 3D mesh from the patch
encoding. We validate our shape parameterization and reconstruction methods on
both synthetic shapes and real world scans. We show that our patch dictionary
performs successful shape completion of complicated surface textures.
An Attention Mechanism for Answer Selection Using a Combined Global and Local View
Authors: Yoram Bachrach, Andrej Zukov-Gregoric, Sam Coope, Ed Tovell, Bogdan Maksak, Jose Rodriguez, Conan McMurtie
Date: 2017-07-05 00:00:00
Tasks: Answer Selection, Question Answering

We propose a new attention mechanism for neural based question answering,
which depends on varying granularities of the input. Previous work focused on
augmenting recurrent neural networks with simple attention mechanisms which are
a function of the similarity between a question embedding and an answer
embeddings across time. We extend this by making the attention mechanism
dependent on a global embedding of the answer attained using a separate
network.
  We evaluate our system on InsuranceQA, a large question answering dataset.
Our model outperforms current state-of-the-art results on InsuranceQA. Further,
we visualize which sections of text our attention mechanism focuses on, and
explore its performance across different parameter settings.
A selectional auto-encoder approach for document image binarization
Authors: Jorge Calvo-Zaragoza, Antonio-Javier Gallego
Date: 2017-06-30 00:00:00
Tasks: Binarization, Information Retrieval, Retrieval

Binarization plays a key role in the automatic information retrieval from
document images. This process is usually performed in the first stages of
documents analysis systems, and serves as a basis for subsequent steps. Hence
it has to be robust in order to allow the full analysis workflow to be
successful. Several methods for document image binarization have been proposed
so far, most of which are based on hand-crafted image processing strategies.
Recently, Convolutional Neural Networks have shown an amazing performance in
many disparate duties related to computer vision. In this paper we discuss the
use of convolutional auto-encoders devoted to learning an end-to-end map from
an input image to its selectional output, in which activations indicate the
likelihood of pixels to be either foreground or background. Once trained,
documents can therefore be binarized by parsing them through the model and
applying a threshold. This approach has proven to outperform existing
binarization strategies in a number of document domains.
Random matrix approach for primal-dual portfolio optimization problems
Authors: Daichi Tada, Hisashi Yamamoto, Takashi Shinzato
Date: 2017-09-14 00:00:00
Tasks: Portfolio Optimization

In this paper, we revisit the portfolio optimization problems of the
minimization/maximization of investment risk under constraints of budget and
investment concentration (primal problem) and the maximization/minimization of
investment concentration under constraints of budget and investment risk (dual
problem) for the case that the variances of the return rates of the assets are
identical. We analyze both optimization problems by using the Lagrange
multiplier method and the random matrix approach. Thereafter, we compare the
results obtained from our proposed approach with the results obtained in
previous work. Moreover, we use numerical experiments to validate the results
obtained from the replica approach and the random matrix approach as methods
for analyzing both the primal and dual portfolio optimization problems.
Updating the silent speech challenge benchmark with deep learning
Authors: Yan Ji, Licheng Liu, Hongcui Wang, Zhilei Liu, Zhibin Niu, Bruce Denby
Date: 2017-09-20 00:00:00
Tasks: Deep Learning

The 2010 Silent Speech Challenge benchmark is updated with new results
obtained in a Deep Learning strategy, using the same input features and
decoding strategy as in the original article. A Word Error Rate of 6.4% is
obtained, compared to the published value of 17.4%. Additional results
comparing new auto-encoder-based features with the original features at reduced
dimensionality, as well as decoding scenarios on two different language models,
are also presented. The Silent Speech Challenge archive has been updated to
contain both the original and the new auto-encoder features, in addition to the
original raw data.
Exact Learning of Lightweight Description Logic Ontologies
Authors: Boris Konev, Carsten Lutz, Ana Ozaki, Frank Wolter
Date: 2017-09-20 00:00:00

We study the problem of learning description logic (DL) ontologies in Angluin
et al.'s framework of exact learning via queries. We admit membership queries
("is a given subsumption entailed by the target ontology?") and equivalence
queries ("is a given ontology equivalent to the target ontology?"). We present
three main results: (1) ontologies formulated in (two relevant versions of) the
description logic DL-Lite can be learned with polynomially many queries of
polynomial size; (2) this is not the case for ontologies formulated in the
description logic EL, even when only acyclic ontologies are admitted; and (3)
ontologies formulated in a fragment of EL related to the web ontology language
OWL 2 RL can be learned in polynomial time. We also show that neither
membership nor equivalence queries alone are sufficient in cases (1) and (3).
Modeling sequences and temporal networks with dynamic community structures
Authors: Tiago P. Peixoto, Martin Rosvall
Date: 2015-09-15 00:00:00
Tasks: Bayesian Inference

In evolving complex systems such as air traffic and social organizations,
collective effects emerge from their many components' dynamic interactions.
While the dynamic interactions can be represented by temporal networks with
nodes and links that change over time, they remain highly complex. It is
therefore often necessary to use methods that extract the temporal networks'
large-scale dynamic community structure. However, such methods are subject to
overfitting or suffer from effects of arbitrary, a priori imposed timescales,
which should instead be extracted from data. Here we simultaneously address
both problems and develop a principled data-driven method that determines
relevant timescales and identifies patterns of dynamics that take place on
networks as well as shape the networks themselves. We base our method on an
arbitrary-order Markov chain model with community structure, and develop a
nonparametric Bayesian inference framework that identifies the simplest such
model that can explain temporal interaction data.
Temporal Pattern Mining from Evolving Networks
Authors: Angelo Impedovo, Corrado Loglisci, Michelangelo Ceci
Date: 2017-09-20 00:00:00

Recently, evolving networks are becoming a suitable form to model many
real-world complex systems, due to their peculiarities to represent the systems
and their constituting entities, the interactions between the entities and the
time-variability of their structure and properties. Designing computational
models able to analyze evolving networks becomes relevant in many applications.
The goal of this research project is to evaluate the possible contribution of
temporal pattern mining techniques in the analysis of evolving networks. In
particular, we aim at exploiting available snapshots for the recognition of
valuable and potentially useful knowledge about the temporal dynamics exhibited
by the network over the time, without making any prior assumption about the
underlying evolutionary schema. Pattern-based approaches of temporal pattern
mining can be exploited to detect and characterize changes exhibited by a
network over the time, starting from observed snapshots.
Latent Embeddings for Collective Activity Recognition
Authors: Yongyi Tang, Peizhen Zhang, Jian-Fang Hu, Wei-Shi Zheng
Date: 2017-09-20 00:00:00
Tasks: Activity Recognition

Rather than simply recognizing the action of a person individually,
collective activity recognition aims to find out what a group of people is
acting in a collective scene. Previ- ous state-of-the-art methods using
hand-crafted potentials in conventional graphical model which can only define a
limited range of relations. Thus, the complex structural de- pendencies among
individuals involved in a collective sce- nario cannot be fully modeled. In
this paper, we overcome these limitations by embedding latent variables into
feature space and learning the feature mapping functions in a deep learning
framework. The embeddings of latent variables build a global relation
containing person-group interac- tions and richer contextual information by
jointly modeling broader range of individuals. Besides, we assemble atten- tion
mechanism during embedding for achieving more com- pact representations. We
evaluate our method on three col- lective activity datasets, where we
contribute a much larger dataset in this work. The proposed model has achieved
clearly better performance as compared to the state-of-the- art methods in our
experiments.