pwc-archive/papers-with-abstracts huggingface dataset:
A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding
Authors: Renqiao Zhang, Jiajun Wu, Chengkai Zhang, William T. Freeman, Joshua B. Tenenbaum
Date: 2016-05-04 00:00:00
Tasks: Scene Understanding

Humans demonstrate remarkable abilities to predict physical events in complex
scenes. Two classes of models for physical scene understanding have recently
been proposed: "Intuitive Physics Engines", or IPEs, which posit that people
make predictions by running approximate probabilistic simulations in causal
mental models similar in nature to video-game physics engines, and memory-based
models, which make judgments based on analogies to stored experiences of
previously encountered scenes and physical outcomes. Versions of the latter
have recently been instantiated in convolutional neural network (CNN)
architectures. Here we report four experiments that, to our knowledge, are the
first rigorous comparisons of simulation-based and CNN-based models, where both
approaches are concretely instantiated in algorithms that can run on raw image
inputs and produce as outputs physical judgments such as whether a stack of
blocks will fall. Both approaches can achieve super-human accuracy levels and
can quantitatively predict human judgments to a similar degree, but only the
simulation-based models generalize to novel situations in ways that people do,
and are qualitatively consistent with systematic perceptual illusions and
judgment asymmetries that people show.
Adaptive Neuron Apoptosis for Accelerating Deep Learning on Large Scale Systems
Authors: Charles Siegel, Jeff Daily, Abhinav Vishnu
Date: 2016-10-03 00:00:00
Tasks: General Classification

We present novel techniques to accelerate the convergence of Deep Learning
algorithms by conducting low overhead removal of redundant neurons -- apoptosis
of neurons -- which do not contribute to model learning, during the training
phase itself. We provide in-depth theoretical underpinnings of our heuristics
(bounding accuracy loss and handling apoptosis of several neuron types), and
present the methods to conduct adaptive neuron apoptosis. Specifically, we are
able to improve the training time for several datasets by 2-3x, while reducing
the number of parameters by up to 30x (4-5x on average) on datasets such as
ImageNet classification. For the Higgs Boson dataset, our implementation
improves the accuracy (measured by Area Under Curve (AUC)) for classification
from 0.88/1 to 0.94/1, while reducing the number of parameters by 3x in
comparison to existing literature. The proposed methods achieve a 2.44x speedup
in comparison to the default (no apoptosis) algorithm.
Distributed Representations of Lexical Sets and Prototypes in Causal Alternation Verbs
Authors: Edoardo Maria Ponti, Elisabetta Jezek, Bernardo Magnini
Date: 2016-10-03 00:00:00

Lexical sets contain the words filling an argument slot of a verb, and are in part determined by selectional preferences. The purpose of this paper is to unravel the properties of lexical sets through distributional semantics. We investigate 1) whether lexical set behave as prototypical categories with a centre and a periphery; 2) whether they are polymorphic, i.e. composed by subcategories; 3) whether the distance between lexical sets of different arguments is explanatory of verb properties. In particular, our case study are lexical sets of causative-inchoative verbs in Italian. Having studied several vector models, we find that 1) based on spatial distance from the centroid, object fillers are scattered uniformly across the category, whereas intransitive subject fillers lie on its edge; 2) a correlation exists between the amount of verb senses and that of clusters discovered automatically, especially for intransitive subjects; 3) the distance between the centroids of object and intransitive subject is correlated with other properties of verbs, such as their cross-lingual tendency to appear in the intransitive pattern rather than transitive one. This paper is noncommittal with respect to the hypothesis that this connection is underpinned by a semantic reason, namely the spontaneity of the event denoted by the verb.
Prediction of Manipulation Actions
Authors: Cornelia Fermüller, Fang Wang, Yezhou Yang, Konstantinos Zampogiannis, Yi Zhang, Francisco Barranco, Michael Pfeiffer
Date: 2016-10-03 00:00:00
Tasks: Prediction

Looking at a person's hands one often can tell what the person is going to do
next, how his/her hands are moving and where they will be, because an actor's
intentions shape his/her movement kinematics during action execution.
Similarly, active systems with real-time constraints must not simply rely on
passive video-segment classification, but they have to continuously update
their estimates and predict future actions. In this paper, we study the
prediction of dexterous actions. We recorded from subjects performing different
manipulation actions on the same object, such as "squeezing", "flipping",
"washing", "wiping" and "scratching" with a sponge. In psychophysical
experiments, we evaluated human observers' skills in predicting actions from
video sequences of different length, depicting the hand movement in the
preparation and execution of actions before and after contact with the object.
We then developed a recurrent neural network based method for action prediction
using as input patches around the hand. We also used the same formalism to
predict the forces on the finger tips using for training synchronized video and
force data streams. Evaluations on two new datasets showed that our system
closely matches human performance in the recognition task, and demonstrate the
ability of our algorithm to predict what and how a dexterous action is
performed.
Real-Time RGB-D based Template Matching Pedestrian Detection
Authors: Omid Hosseini jafari, Michael Ying Yang
Date: 2016-10-03 00:00:00
Tasks: Pedestrian Detection, Template Matching

Pedestrian detection is one of the most popular topics in computer vision and
robotics. Considering challenging issues in multiple pedestrian detection, we
present a real-time depth-based template matching people detector. In this
paper, we propose different approaches for training the depth-based template.
We train multiple templates for handling issues due to various upper-body
orientations of the pedestrians and different levels of detail in depth-map of
the pedestrians with various distances from the camera. And, we take into
account the degree of reliability for different regions of sliding window by
proposing the weighted template approach. Furthermore, we combine the
depth-detector with an appearance based detector as a verifier to take
advantage of the appearance cues for dealing with the limitations of depth
data. We evaluate our method on the challenging ETH dataset sequence. We show
that our method outperforms the state-of-the-art approaches.
Can Ground Truth Label Propagation from Video help Semantic Segmentation?
Authors: Siva Karthik Mustikovela, Michael Ying Yang, Carsten Rother
Date: 2016-10-03 00:00:00
Tasks: Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

For state-of-the-art semantic segmentation task, training convolutional
neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling,
which is expensive and involves extensive human effort. In this work, we study
the possibility of using auxiliary ground truth, so-called \textit{pseudo
ground truth} (PGT) to improve the performance. The PGT is obtained by
propagating the labels of a GT frame to its subsequent frames in the video
using a simple CRF-based, cue integration framework. Our main contribution is
to demonstrate the use of noisy PGT along with GT to improve the performance of
a CNN. We perform a systematic analysis to find the right kind of PGT that
needs to be added along with the GT for training a CNN. In this regard, we
explore three aspects of PGT which influence the learning of a CNN: i) the PGT
labeling has to be of good quality; ii) the PGT images have to be different
compared to the GT images; iii) the PGT has to be trusted differently than GT.
We conclude that PGT which is diverse from GT images and has good quality of
labeling can indeed help improve the performance of a CNN. Also, when PGT is
multiple folds larger than GT, weighing down the trust on PGT helps in
improving the accuracy. Finally, We show that using PGT along with GT, the
performance of Fully Convolutional Network (FCN) on Camvid data is increased by
$2.7\%$ on IoU accuracy. We believe such an approach can be used to train CNNs
for semantic video segmentation where sequentially labeled image frames are
needed. To this end, we provide recommendations for using PGT strategically for
semantic segmentation and hence bypass the need for extensive human efforts in
labeling.
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
Authors: Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, Sergey Levine
Date: 2016-10-03 00:00:00
Tasks: Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In principle, reinforcement learning and policy search methods can enable
robots to learn highly complex and general skills that may allow them to
function amid the complexity and diversity of the real world. However, training
a policy that generalizes well across a wide range of real-world conditions
requires far greater quantity and diversity of experience than is practical to
collect with a single robot. Fortunately, it is possible for multiple robots to
share their experience with one another, and thereby, learn a policy
collectively. In this work, we explore distributed and asynchronous policy
learning as a means to achieve generalization and improved training times on
challenging, real-world manipulation tasks. We propose a distributed and
asynchronous version of Guided Policy Search and use it to demonstrate
collective policy learning on a vision-based door opening task using four
robots. We show that it achieves better generalization, utilization, and
training times than the single robot alternative.
Data Integration with High Dimensionality
Authors: Xin Gao, Raymond J. Carroll
Date: 2016-10-03 00:00:00
Tasks: Data Integration, Vocal Bursts Intensity Prediction

We consider a problem of data integration. Consider determining which genes
affect a disease. The genes, which we call predictor objects, can be measured
in different experiments on the same individual. We address the question of
finding which genes are predictors of disease by any of the experiments. Our
formulation is more general. In a given data set, there are a fixed number of
responses for each individual, which may include a mix of discrete, binary and
continuous variables. There is also a class of predictor objects, which may
differ within a subject depending on how the predictor object is measured,
i.e., depend on the experiment. The goal is to select which predictor objects
affect any of the responses, where the number of such informative predictor
objects or features tends to infinity as sample size increases. There are
marginal likelihoods for each way the predictor object is measured, i.e., for
each experiment. We specify a pseudolikelihood combining the marginal
likelihoods, and propose a pseudolikelihood information criterion. Under
regularity conditions, we establish selection consistency for the
pseudolikelihood information criterion with unbounded true model size, which
includes a Bayesian information criterion with appropriate penalty term as a
special case. Simulations indicate that data integration improves upon,
sometimes dramatically, using only one of the data sources.
Kernel Selection using Multiple Kernel Learning and Domain Adaptation in Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance Scenario
Authors: Samik Banerjee, Sukhendu Das
Date: 2016-10-03 00:00:00
Tasks: Domain Adaptation, Face Recognition, Unsupervised Domain Adaptation

Face Recognition (FR) has been the interest to several researchers over the
past few decades due to its passive nature of biometric authentication. Despite
high accuracy achieved by face recognition algorithms under controlled
conditions, achieving the same performance for face images obtained in
surveillance scenarios, is a major hurdle. Some attempts have been made to
super-resolve the low-resolution face images and improve the contrast, without
considerable degree of success. The proposed technique in this paper tries to
cope with the very low resolution and low contrast face images obtained from
surveillance cameras, for FR under surveillance conditions. For Support Vector
Machine classification, the selection of appropriate kernel has been a widely
discussed issue in the research community. In this paper, we propose a novel
kernel selection technique termed as MFKL (Multi-Feature Kernel Learning) to
obtain the best feature-kernel pairing. Our proposed technique employs a
effective kernel selection by Multiple Kernel Learning (MKL) method, to choose
the optimal kernel to be used along with unsupervised domain adaptation method
in the Reproducing Kernel Hilbert Space (RKHS), for a solution to the problem.
Rigorous experimentation has been performed on three real-world surveillance
face datasets : FR\_SURV, SCface and ChokePoint. Results have been shown using
Rank-1 Recognition Accuracy, ROC and CMC measures. Our proposed method
outperforms all other recent state-of-the-art techniques by a considerable
margin.
Data-driven detrending of nonstationary fractal time series with echo state networks
Authors: Enrico Maiorino, Filippo Maria Bianchi, Lorenzo Livi, Antonello Rizzi, Alireza Sadeghian
Date: 2015-10-24 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we propose a novel data-driven approach for removing trends
(detrending) from nonstationary, fractal and multifractal time series. We
consider real-valued time series relative to measurements of an underlying
dynamical system that evolves through time. We assume that such a dynamical
process is predictable to a certain degree by means of a class of recurrent
networks called Echo State Network (ESN), which are capable to model a generic
dynamical process. In order to isolate the superimposed (multi)fractal
component of interest, we define a data-driven filter by leveraging on the ESN
prediction capability to identify the trend component of a given input time
series. Specifically, the (estimated) trend is removed from the original time
series and the residual signal is analyzed with the multifractal detrended
fluctuation analysis procedure to verify the correctness of the detrending
procedure. In order to demonstrate the effectiveness of the proposed technique,
we consider several synthetic time series consisting of different types of
trends and fractal noise components with known characteristics. We also process
a real-world dataset, the sunspot time series, which is well-known for its
multifractal features and has recently gained attention in the complex systems
field. Results demonstrate the validity and generality of the proposed
detrending method based on ESNs.
ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions
Authors: Fang Han, Han Liu
Date: 2013-10-14 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We present a robust alternative to principal component analysis (PCA) ---
called elliptical component analysis (ECA) --- for analyzing high dimensional,
elliptically distributed data. ECA estimates the eigenspace of the covariance
matrix of the elliptical data. To cope with heavy-tailed elliptical
distributions, a multivariate rank statistic is exploited. At the model-level,
we consider two settings: either that the leading eigenvectors of the
covariance matrix are non-sparse or that they are sparse. Methodologically, we
propose ECA procedures for both non-sparse and sparse settings. Theoretically,
we provide both non-asymptotic and asymptotic analyses quantifying the
theoretical performances of ECA. In the non-sparse setting, we show that ECA's
performance is highly related to the effective rank of the covariance matrix.
In the sparse setting, the results are twofold: (i) We show that the sparse ECA
estimator based on a combinatoric program attains the optimal rate of
convergence; (ii) Based on some recent developments in estimating sparse
leading eigenvectors, we show that a computationally efficient sparse ECA
estimator attains the optimal rate of convergence under a suboptimal scaling.
Orthographic Syllable as basic unit for SMT between Related Languages
Authors: Anoop Kunchukuttan, Pushpak Bhattacharyya
Date: 2016-10-03 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Translation

We explore the use of the orthographic syllable, a variable-length
consonant-vowel sequence, as a basic unit of translation between related
languages which use abugida or alphabetic scripts. We show that orthographic
syllable level translation significantly outperforms models trained over other
basic units (word, morpheme and character) when training over small parallel
corpora.
Uniform Generalization, Concentration, and Adaptive Learning
Authors: Ibrahim Alabdulmohsin
Date: 2016-08-22 00:00:00
Tasks: Learning Theory

One fundamental goal in any learning algorithm is to mitigate its risk for
overfitting. Mathematically, this requires that the learning algorithm enjoys a
small generalization risk, which is defined either in expectation or in
probability. Both types of generalization are commonly used in the literature.
For instance, generalization in expectation has been used to analyze
algorithms, such as ridge regression and SGD, whereas generalization in
probability is used in the VC theory, among others. Recently, a third notion of
generalization has been studied, called uniform generalization, which requires
that the generalization risk vanishes uniformly in expectation across all
bounded parametric losses. It has been shown that uniform generalization is, in
fact, equivalent to an information-theoretic stability constraint, and that it
recovers classical results in learning theory. It is achievable under various
settings, such as sample compression schemes, finite hypothesis spaces, finite
domains, and differential privacy. However, the relationship between uniform
generalization and concentration remained unknown. In this paper, we answer
this question by proving that, while a generalization in expectation does not
imply a generalization in probability, a uniform generalization in expectation
does imply concentration. We establish a chain rule for the uniform
generalization risk of the composition of hypotheses and use it to derive a
large deviation bound. Finally, we prove that the bound is tight.
Multimodal Semantic Simulations of Linguistically Underspecified Motion Events
Authors: Nikhil Krishnaswamy, James Pustejovsky
Date: 2016-10-03 00:00:00

In this paper, we describe a system for generating three-dimensional visual
simulations of natural language motion expressions. We use a rich formal model
of events and their participants to generate simulations that satisfy the
minimal constraints entailed by the associated utterance, relying on semantic
knowledge of physical objects and motion events. This paper outlines technical
considerations and discusses implementing the aforementioned semantic models
into such a system.
An Arabic-Hebrew parallel corpus of TED talks
Authors: Mauro Cettolo
Date: 2016-10-03 00:00:00
Tasks: Sentence

We describe an Arabic-Hebrew parallel corpus of TED talks built upon WIT3,
the Web inventory that repurposes the original content of the TED website in a
way which is more convenient for MT researchers. The benchmark consists of
about 2,000 talks, whose subtitles in Arabic and Hebrew have been accurately
aligned and rearranged in sentences, for a total of about 3.5M tokens per
language. Talks have been partitioned in train, development and test sets
similarly in all respects to the MT tasks of the IWSLT 2016 evaluation
campaign. In addition to describing the benchmark, we list the problems
encountered in preparing it and the novel methods designed to solve them.
Baseline MT results and some measures on sentence length are provided as an
extrinsic evaluation of the quality of the benchmark.
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks
Authors: Timothy J. O'Shea, Seth Hitefield, Johnathan Corgan
Date: 2016-10-03 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Time Series, Time Series Analysis

We investigate sequence machine learning techniques on raw radio signal
time-series data. By applying deep recurrent neural networks we learn to
discriminate between several application layer traffic types on top of a
constant envelope modulation without using an expert demodulation algorithm. We
show that complex protocol sequences can be learned and used for both
classification and generation tasks using this approach.
Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
Authors: Anil Bas, William A. P. Smith, Timo Bolkart, Stefanie Wuhrer
Date: 2016-02-02 00:00:00

We propose a fully automatic method for fitting a 3D morphable model to
single face images in arbitrary pose and lighting. Our approach relies on
geometric features (edges and landmarks) and, inspired by the iterated closest
point algorithm, is based on computing hard correspondences between model
vertices and edge pixels. We demonstrate that this is superior to previous work
that uses soft correspondences to form an edge-derived cost surface that is
minimised by nonlinear optimisation.
Path Integral Guided Policy Search
Authors: Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, Sergey Levine
Date: 2016-10-03 00:00:00

We present a policy search method for learning complex feedback control
policies that map from high-dimensional sensory inputs to motor torques, for
manipulation tasks with discontinuous contact dynamics. We build on a prior
technique called guided policy search (GPS), which iteratively optimizes a set
of local policies for specific instances of a task, and uses these to train a
complex, high-dimensional global policy that generalizes across task instances.
We extend GPS in the following ways: (1) we propose the use of a model-free
local optimizer based on path integral stochastic optimal control (PI2), which
enables us to learn local policies for tasks with highly discontinuous contact
dynamics; and (2) we enable GPS to train on a new set of task instances in
every iteration by using on-policy sampling: this increases the diversity of
the instances that the policy is trained on, and is crucial for achieving good
generalization. We show that these contributions enable us to learn deep neural
network policies that can directly perform torque control from visual input. We
validate the method on a challenging door opening task and a pick-and-place
task, and we demonstrate that our approach substantially outperforms the prior
LQR-based local policy optimizer on these tasks. Furthermore, we show that
on-policy sampling significantly increases the generalization ability of these
policies.
Video Pixel Networks
Authors: Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu
Date: 2016-10-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Video Prediction

We propose a probabilistic video model, the Video Pixel Network (VPN), that
estimates the discrete joint distribution of the raw pixel values in a video.
The model and the neural architecture reflect the time, space and color
structure of video tensors and encode it as a four-dimensional dependency
chain. The VPN approaches the best possible performance on the Moving MNIST
benchmark, a leap over the previous state of the art, and the generated videos
show only minor deviations from the ground truth. The VPN also produces
detailed samples on the action-conditional Robotic Pushing benchmark and
generalizes to the motion of novel objects.