pwc-archive/papers-with-abstracts huggingface dataset:
The cognitive roots of regularization in language
Authors: Vanessa Ferdinand, Simon Kirby, Kenny Smith
Date: 2017-03-09 00:00:00

Regularization occurs when the output a learner produces is less variable
than the linguistic data they observed. In an artificial language learning
experiment, we show that there exist at least two independent sources of
regularization bias in cognition: a domain-general source based on cognitive
load and a domain-specific source triggered by linguistic stimuli. Both of
these factors modulate how frequency information is encoded and produced, but
only the production-side modulations result in regularization (i.e. cause
learners to eliminate variation from the observed input). We formalize the
definition of regularization as the reduction of entropy and find that entropy
measures are better at identifying regularization behavior than frequency-based
analyses. Using our experimental data and a model of cultural transmission, we
generate predictions for the amount of regularity that would develop in each
experimental condition if the artificial language were transmitted over several
generations of learners. Here we find that the effect of cognitive constraints
can become more complex when put into the context of cultural evolution:
although learning biases certainly carry information about the course of
language evolution, we should not expect a one-to-one correspondence between
the micro-level processes that regularize linguistic datasets and the
macro-level evolution of linguistic regularity.
Generalizing Skills with Semi-Supervised Reinforcement Learning
Authors: Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine
Date: 2016-12-01 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) can acquire complex behaviors from low-level
inputs, such as images. However, real-world applications of such methods
require generalizing to the vast variability of the real world. Deep networks
are known to achieve remarkable generalization when provided with massive
amounts of labeled data, but can we provide this breadth of experience to an RL
agent, such as a robot? The robot might continuously learn as it explores the
world around it, even while deployed. However, this learning requires access to
a reward function, which is often hard to measure in real-world domains, where
the reward could depend on, for example, unknown positions of objects or the
emotional state of the user. Conversely, it is often quite practical to provide
the agent with reward functions in a limited set of situations, such as when a
human supervisor is present or in a controlled setting. Can we make use of this
limited supervision, and still benefit from the breadth of experience an agent
might collect on its own? In this paper, we formalize this problem as
semisupervised reinforcement learning, where the reward function can only be
evaluated in a set of "labeled" MDPs, and the agent must generalize its
behavior to the wide range of states it might encounter in a set of "unlabeled"
MDPs, by using experience from both settings. Our proposed method infers the
task objective in the unlabeled MDPs through an algorithm that resembles
inverse RL, using the agent's own prior experience in the labeled MDPs as a
kind of demonstration of optimal behavior. We evaluate our method on
challenging tasks that require control directly from images, and show that our
approach can improve the generalization of a learned deep neural network policy
by using experience for which no reward function is available. We also show
that our method outperforms direct supervised learning of the reward.
What can you do with a rock? Affordance extraction via word embeddings
Authors: Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate
Date: 2017-03-09 00:00:00
Tasks: Affordance Detection, Reinforcement Learning, Reinforcement Learning (RL), Word Embeddings

Autonomous agents must often detect affordances: the set of behaviors enabled
by a situation. Affordance detection is particularly helpful in domains with
large action spaces, allowing the agent to prune its search space by avoiding
futile behaviors. This paper presents a method for affordance extraction via
word embeddings trained on a Wikipedia corpus. The resulting word vectors are
treated as a common knowledge database which can be queried using linear
algebra. We apply this method to a reinforcement learning agent in a text-only
environment and show that affordance-based action selection improves
performance most of the time. Our method increases the computational complexity
of each learning step but significantly reduces the total number of steps
needed. In addition, the agent's action selections begin to resemble those a
human would choose.
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis
Authors: Jürgen Bernard, Christian Ritter, David Sessler, Matthias Zeppelzauer, Jörn Kohlhammer, Dieter Fellner
Date: 2017-03-09 00:00:00
Tasks: Active Learning, Information Retrieval, Retrieval

The definition of similarity is a key prerequisite when analyzing complex
data types in data mining, information retrieval, or machine learning. However,
the meaningful definition is often hampered by the complexity of data objects
and particularly by different notions of subjective similarity latent in
targeted user groups. Taking the example of soccer players, we present a
visual-interactive system that learns users' mental models of similarity. In a
visual-interactive interface, users are able to label pairs of soccer players
with respect to their subjective notion of similarity. Our proposed similarity
model automatically learns the respective concept of similarity using an active
learning strategy. A visual-interactive retrieval technique is provided to
validate the model and to execute downstream retrieval tasks for soccer player
analysis. The applicability of the approach is demonstrated in different
evaluation strategies, including usage scenarions and cross-validation tests.
Loss is its own Reward: Self-Supervision for Reinforcement Learning
Authors: Evan Shelhamer, Parsa Mahmoudieh, Max Argus, Trevor Darrell
Date: 2016-12-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Reinforcement learning optimizes policies for expected cumulative reward.
Need the supervision be so narrow? Reward is delayed and sparse for many tasks,
making it a difficult and impoverished signal for end-to-end optimization. To
augment reward, we consider a range of self-supervised tasks that incorporate
states, actions, and successors to provide auxiliary losses. These losses offer
ubiquitous and instantaneous supervision for representation learning even in
the absence of reward. While current results show that learning from reward
alone is feasible, pure reinforcement learning methods are constrained by
computational and data efficiency issues that can be remedied by auxiliary
losses. Self-supervised pre-training and joint optimization improve the data
efficiency and policy returns of end-to-end reinforcement learning.
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Authors: Itay Safran, Ohad Shamir
Date: 2016-10-31 00:00:00
Proceeding: ICML 2017 8

We provide several new depth-based separation results for feed-forward neural networks, proving that various types of simple and natural functions can be better approximated using deeper networks than shallower ones, even if the shallower networks are much larger. This includes indicators of balls and ellipses; non-linear functions which are radial with respect to the $L_1$ norm; and smooth non-linear functions. We also show that these gaps can be observed experimentally: Increasing the depth indeed allows better learning than increasing width, when training neural networks to learn an indicator of a unit ball.
Computational Mapping of the Ground Reflectivity with Laser Scanners
Authors: Juan Castorena
Date: 2016-11-28 00:00:00
Tasks: Autonomous Vehicles

In this investigation we focus on the problem of mapping the ground
reflectivity with multiple laser scanners mounted on mobile robots/vehicles.
The problem originates because regions of the ground become populated with a
varying number of reflectivity measurements whose value depends on the observer
and its corresponding perspective. Here, we propose a novel automatic,
data-driven computational mapping framework specifically aimed at preserving
edge sharpness in the map reconstruction process and that considers the sources
of measurement variation. Our new formulation generates map-perspective
gradients and applies sub-set selection fusion and de-noising operators to
these through iterative algorithms that minimize an $\ell_1$ sparse regularized
least squares formulation. Reconstruction of the ground reflectivity is then
carried out based on Poisson's formulation posed as an $\ell_2$ term promoting
consistency with the fused gradient of map-perspectives and a term that ensures
equality constraints with reference measurement data. We demonstrate our new
framework outperforms the capabilities of existing ones with experiments
realized on Ford's fleet of autonomous vehicles. For example, we show we can
achieve map enhancement (i.e., contrast enhancement), artifact removal,
de-noising and map-stitching without requiring an additional reflectivity
adjustment to calibrate sensors to the specific mounting and robot/vehicle
motion.
Tunable GMM Kernels
Authors: Ping Li
Date: 2017-01-09 00:00:00
Tasks: General Classification

The recently proposed "generalized min-max" (GMM) kernel can be efficiently
linearized, with direct applications in large-scale statistical learning and
fast near neighbor search. The linearized GMM kernel was extensively compared
in with linearized radial basis function (RBF) kernel. On a large number of
classification tasks, the tuning-free GMM kernel performs (surprisingly) well
compared to the best-tuned RBF kernel. Nevertheless, one would naturally expect
that the GMM kernel ought to be further improved if we introduce tuning
parameters.
  In this paper, we study three simple constructions of tunable GMM kernels:
(i) the exponentiated-GMM (or eGMM) kernel, (ii) the powered-GMM (or pGMM)
kernel, and (iii) the exponentiated-powered-GMM (epGMM) kernel. The pGMM kernel
can still be efficiently linearized by modifying the original hashing procedure
for the GMM kernel. On about 60 publicly available classification datasets, we
verify that the proposed tunable GMM kernels typically improve over the
original GMM kernel. On some datasets, the improvements can be astonishingly
significant.
  For example, on 11 popular datasets which were used for testing deep learning
algorithms and tree methods, our experiments show that the proposed tunable GMM
kernels are strong competitors to trees and deep nets. The previous studies
developed tree methods including "abc-robust-logitboost" and demonstrated the
excellent performance on those 11 datasets (and other datasets), by
establishing the second-order tree-split formula and new derivatives for
multi-class logistic loss. Compared to tree methods like
"abc-robust-logitboost" (which are slow and need substantial model sizes), the
tunable GMM kernels produce largely comparable results.
A log-linear time algorithm for constrained changepoint detection
Authors: Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque
Date: 2017-03-09 00:00:00
Tasks: Time Series, Time Series Analysis

Changepoint detection is a central problem in time series and genomic data.
For some applications, it is natural to impose constraints on the directions of
changes. One example is ChIP-seq data, for which adding an up-down constraint
improves peak detection accuracy, but makes the optimization problem more
complicated. We show how a recently proposed functional pruning technique can
be adapted to solve such constrained changepoint detection problems. This leads
to a new algorithm which can solve problems with arbitrary affine constraints
on adjacent segment means, and which has empirical time complexity that is
log-linear in the amount of data. This algorithm achieves state-of-the-art
accuracy in a benchmark of several genomic data sets, and is orders of
magnitude faster than existing algorithms that have similar accuracy. Our
implementation is available as the PeakSegPDPA function in the coseg R package,
https://github.com/tdhock/coseg
Fast and Robust Detection of Fallen People from a Mobile Robot
Authors: Morris Antonello, Marco Carraro, Marco Pierobon, Emanuele Menegatti
Date: 2017-03-09 00:00:00
Tasks: Robot Navigation, Semantic Segmentation

This paper deals with the problem of detecting fallen people lying on the
floor by means of a mobile robot equipped with a 3D depth sensor. In the
proposed algorithm, inspired by semantic segmentation techniques, the 3D scene
is over-segmented into small patches. Fallen people are then detected by means
of two SVM classifiers: the first one labels each patch, while the second one
captures the spatial relations between them. This novel approach showed to be
robust and fast. Indeed, thanks to the use of small patches, fallen people in
real cluttered scenes with objects side by side are correctly detected.
Moreover, the algorithm can be executed on a mobile robot fitted with a
standard laptop making it possible to exploit the 2D environmental map built by
the robot and the multiple points of view obtained during the robot navigation.
Additionally, this algorithm is robust to illumination changes since it does
not rely on RGB data but on depth data. All the methods have been thoroughly
validated on the IASLAB-RGBD Fallen Person Dataset, which is published online
as a further contribution. It consists of several static and dynamic sequences
with 15 different people and 2 different environments.
Universal adversarial perturbations
Authors: Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard
Date: 2016-10-26 00:00:00
Proceeding: CVPR 2017 7

Given a state-of-the-art deep neural network classifier, we show the
existence of a universal (image-agnostic) and very small perturbation vector
that causes natural images to be misclassified with high probability. We
propose a systematic algorithm for computing universal perturbations, and show
that state-of-the-art deep neural networks are highly vulnerable to such
perturbations, albeit being quasi-imperceptible to the human eye. We further
empirically analyze these universal perturbations and show, in particular, that
they generalize very well across neural networks. The surprising existence of
universal perturbations reveals important geometric correlations among the
high-dimensional decision boundary of classifiers. It further outlines
potential security breaches with the existence of single directions in the
input space that adversaries can possibly exploit to break a classifier on most
natural images.
End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
Authors: Umut Güçlü, Yağmur Güçlütürk, Meysam Madadi, Sergio Escalera, Xavier Baró, Jordi González, Rob Van Lier, Marcel A. J. van Gerven
Date: 2017-03-09 00:00:00
Tasks: Segmentation, Semantic Segmentation

Recent years have seen a sharp increase in the number of related yet distinct
advances in semantic segmentation. Here, we tackle this problem by leveraging
the respective strengths of these advances. That is, we formulate a conditional
random field over a four-connected graph as end-to-end trainable convolutional
and recurrent networks, and estimate them via an adversarial process.
Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order
inconsistencies. We evaluate our model on two standard benchmark datasets for
semantic face segmentation, achieving state-of-the-art results on both of them.
Stride Length Estimation with Deep Learning
Authors: Julius Hannink, Thomas Kautz, Cristian F. Pasluosta, Jens Barth, Samuel Schülein, Karl-Günter Gaßmann, Jochen Klucken, Bjoern M. Eskofier
Date: 2016-09-12 00:00:00
Tasks: Deep Learning

Accurate estimation of spatial gait characteristics is critical to assess
motor impairments resulting from neurological or musculoskeletal disease.
Currently, however, methodological constraints limit clinical applicability of
state-of-the-art double integration approaches to gait patterns with a clear
zero-velocity phase. We describe a novel approach to stride length estimation
that uses deep convolutional neural networks to map stride-specific inertial
sensor data to the resulting stride length. The model is trained on a publicly
available and clinically relevant benchmark dataset consisting of 1220 strides
from 101 geriatric patients. Evaluation is done in a 10-fold cross validation
and for three different stride definitions. Even though best results are
achieved with strides defined from mid-stance to mid-stance with average
accuracy and precision of 0.01 $\pm$ 5.37 cm, performance does not strongly
depend on stride definition. The achieved precision outperforms
state-of-the-art methods evaluated on this benchmark dataset by 3.0 cm (36%).
Due to the independence of stride definition, the proposed method is not
subject to the methodological constrains that limit applicability of
state-of-the-art double integration methods. Furthermore, precision on the
benchmark dataset could be improved. With more precise mobile stride length
estimation, new insights to the progression of neurological disease or early
indications might be gained. Due to the independence of stride definition,
previously uncharted diseases in terms of mobile gait analysis can now be
investigated by re-training and applying the proposed method.
Template Matching via Densities on the Roto-Translation Group
Authors: Erik J. Bekkers, Marco Loog, Bart M. ter Haar Romeny, Remco Duits
Date: 2016-03-10 00:00:00
Tasks: Template Matching, Translation

We propose a template matching method for the detection of 2D image objects
that are characterized by orientation patterns. Our method is based on data
representations via orientation scores, which are functions on the space of
positions and orientations, and which are obtained via a wavelet-type
transform. This new representation allows us to detect orientation patterns in
an intuitive and direct way, namely via cross-correlations. Additionally, we
propose a generalized linear regression framework for the construction of
suitable templates using smoothing splines. Here, it is important to recognize
a curved geometry on the position-orientation domain, which we identify with
the Lie group SE(2): the roto-translation group. Templates are then optimized
in a B-spline basis, and smoothness is defined with respect to the curved
geometry. We achieve state-of-the-art results on three different applications:
detection of the optic nerve head in the retina (99.83% success rate on 1737
images), of the fovea in the retina (99.32% success rate on 1616 images), and
of the pupil in regular camera images (95.86% on 1521 images). The high
performance is due to inclusion of both intensity and orientation features with
effective geometric priors in the template matching. Moreover, our method is
fast due to a cross-correlation based matching approach.
Counterfactuals, indicative conditionals, and negation under uncertainty: Are there cross-cultural differences?
Authors: Niki Pfeifer, Hiroshi Yama
Date: 2017-03-09 00:00:00
Tasks: Negation

In this paper we study selected argument forms involving counterfactuals and
indicative conditionals under uncertainty. We selected argument forms to
explore whether people with an Eastern cultural background reason differently
about conditionals compared to Westerners, because of the differences in the
location of negations. In a 2x2 between-participants design, 63 Japanese
university students were allocated to four groups, crossing indicative
conditionals and counterfactuals, and each presented in two random task orders.
The data show close agreement between the responses of Easterners and
Westerners. The modal responses provide strong support for the hypothesis that
conditional probability is the best predictor for counterfactuals and
indicative conditionals. Finally, the grand majority of the responses are
probabilistically coherent, which endorses the psychological plausibility of
choosing coherence-based probability logic as a rationality framework for
psychological reasoning research.
ASHACL: Alternative Shapes Constraint Language
Authors: Peter F. Patel-Schneider
Date: 2017-02-06 00:00:00

ASHACL, a variant of the W3C Shapes Constraint Language, is designed to
determine whether an RDF graph meets some conditions. These conditions are
grouped into shapes, which validate whether particular RDF terms each meet the
constraints of the shape. Shapes are themselves expressed as RDF triples in an
RDF graph, called a shapes graph.
Neighborhood Mixture Model for Knowledge Base Completion
Authors: Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, Mark Johnson
Date: 2016-06-21 00:00:00
Proceeding: CONLL 2016 8
Tasks: General Classification, Knowledge Base Completion, model, Prediction, Relation Prediction, Triple Classification

Knowledge bases are useful resources for many natural language processing
tasks, however, they are far from complete. In this paper, we define a novel
entity representation as a mixture of its neighborhood in the knowledge base
and apply this technique on TransE-a well-known embedding model for knowledge
base completion. Experimental results show that the neighborhood information
significantly helps to improve the results of the TransE model, leading to
better performance than obtained by other state-of-the-art embedding models on
three benchmark datasets for triple classification, entity prediction and
relation prediction tasks.
Alleviating Overfitting for Polysemous Words for Word Representation Estimation Using Lexicons
Authors: Yuanzhi Ke, Masafumi Hagiwara
Date: 2016-12-02 00:00:00

Though there are some works on improving distributed word representations
using lexicons, the improper overfitting of the words that have multiple
meanings is a remaining issue deteriorating the learning when lexicons are
used, which needs to be solved. An alternative method is to allocate a vector
per sense instead of a vector per word. However, the word representations
estimated in the former way are not as easy to use as the latter one. Our
previous work uses a probabilistic method to alleviate the overfitting, but it
is not robust with a small corpus. In this paper, we propose a new neural
network to estimate distributed word representations using a lexicon and a
corpus. We add a lexicon layer in the continuous bag-of-words model and a
threshold node after the output of the lexicon layer. The threshold rejects the
unreliable outputs of the lexicon layer that are less likely to be the same
with their inputs. In this way, it alleviates the overfitting of the polysemous
words. The proposed neural network can be trained using negative sampling,
which maximizing the log probabilities of target words given the context words,
by distinguishing the target words from random noises. We compare the proposed
neural network with the continuous bag-of-words model, the other works
improving it, and the previous works estimating distributed word
representations using both a lexicon and a corpus. The experimental results
show that the proposed neural network is more efficient and balanced for both
semantic tasks and syntactic tasks than the previous works, and robust to the
size of the corpus.