pwc-archive/papers-with-abstracts huggingface dataset:
Fully automatic multi-language translation with a catalogue of phrases - successful employment for the Swiss avalanche bulletin
Authors: Kurt Winkler, Tobias Kuhn
Date: 2015-09-23 00:00:00
Tasks: Translation

The Swiss avalanche bulletin is produced twice a day in four languages. Due
to the lack of time available for manual translation, a fully automated
translation system is employed, based on a catalogue of predefined phrases and
predetermined rules of how these phrases can be combined to produce sentences.
Because this catalogue of phrases is limited to a small sublanguage, the system
is able to automatically translate such sentences from German into the target
languages French, Italian and English without subsequent proofreading or
correction. Having been operational for two winter seasons, we assess here the
quality of the produced texts based on two different surveys where participants
rated texts from real avalanche bulletins from both origins, the catalogue of
phrases versus manually written and translated texts. With a mean recognition
rate of 55%, users can hardly distinguish between thetwo types of texts, and
give very similar ratings with respect to their language quality. Overall, the
output from the catalogue system can be considered virtually equivalent to a
text written by avalanche forecasters and then manually translated by
professional translators. Furthermore, forecasters declared that all relevant
situations were captured by the system with sufficient accuracy. Forecaster's
working load did not change with the introduction of the catalogue: the extra
time to find matching sentences is compensated by the fact that they no longer
need to double-check manually translated texts. The reduction of daily
translation costs is expected to offset the initial development costs within a
few years.
Predicting Climate Variability over the Indian Region Using Data Mining Strategies
Authors: Naresh Kumar Mallenahalli
Date: 2015-09-23 00:00:00
Tasks: Clustering, General Classification, regression

In this paper an approach based on expectation maximization (EM) clustering
to find the climate regions and a support vector machine to build a predictive
model for each of these regions is proposed. To minimize the biases in the
estimations a ten cross fold validation is adopted both for obtaining clusters
and building the predictive models. The EM clustering could identify all the
zones as per the Koppen classification over Indian region. The proposed
strategy when employed for predicting temperature has resulted in an RMSE of
$1.19$ in the Montane climate region and $0.89$ in the Humid Sub Tropical
region as compared to $2.9$ and $0.95$ respectively predicted using k-means and
linear regression method.
P-CNN: Pose-based CNN Features for Action Recognition
Authors: Guilhem Chéron, Ivan Laptev, Cordelia Schmid
Date: 2015-06-11 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Recognition, Temporal Action Localization

This work targets human action recognition in video. While recent methods
typically represent actions by statistics of local video features, here we
argue for the importance of a representation derived from human pose. To this
end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN)
for action recognition. The descriptor aggregates motion and appearance
information along tracks of human body parts. We investigate different schemes
of temporal aggregation and experiment with P-CNN features obtained both for
automatically estimated and manually annotated human poses. We evaluate our
method on the recent and challenging JHMDB and MPII Cooking datasets. For both
datasets our method shows consistent improvement over the state of the art.
Efficient reconstruction of transmission probabilities in a spreading process from partial observations
Authors: Andrey Y. Lokhov, Theodor Misiakiewicz
Date: 2015-09-23 00:00:00

An important problem of reconstruction of diffusion network and transmission
probabilities from the data has attracted a considerable attention in the past
several years. A number of recent papers introduced efficient algorithms for
the estimation of spreading parameters, based on the maximization of the
likelihood of observed cascades, assuming that the full information for all the
nodes in the network is available. In this work, we focus on a more realistic
and restricted scenario, in which only a partial information on the cascades is
available: either the set of activation times for a limited number of nodes, or
the states of nodes for a subset of observation times. To tackle this problem,
we first introduce a framework based on the maximization of the likelihood of
the incomplete diffusion trace. However, we argue that the computation of this
incomplete likelihood is a computationally hard problem, and show that a fast
and robust reconstruction of transmission probabilities in sparse networks can
be achieved with a new algorithm based on recently introduced dynamic
message-passing equations for the spreading processes. The suggested approach
can be easily generalized to a large class of discrete and continuous dynamic
models, as well as to the cases of dynamically-changing networks and noisy
information.
New Fuzzy LBP Features for Face Recognition
Authors: Abdullah Gubbi, Mohammed Fazle Azeem, Zahid Ansari
Date: 2015-09-23 00:00:00
Tasks: Face Recognition

There are many Local texture features each very in way they implement and
each of the Algorithm trying improve the performance. An attempt is made in
this paper to represent a theoretically very simple and computationally
effective approach for face recognition. In our implementation the face image
is divided into 3x3 sub-regions from which the features are extracted using the
Local Binary Pattern (LBP) over a window, fuzzy membership function and at the
central pixel. The LBP features possess the texture discriminative property and
their computational cost is very low. By utilising the information from LBP,
membership function, and central pixel, the limitations of traditional LBP is
eliminated. The bench mark database like ORL and Sheffield Databases are used
for the evaluation of proposed features with SVM classifier. For the proposed
approach K-fold and ROC curves are obtained and results are compared.
Minimum Weight Perfect Matching via Blossom Belief Propagation
Authors: Sungsoo Ahn, Sejun Park, Michael Chertkov, Jinwoo Shin
Date: 2015-09-23 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Combinatorial Optimization

Max-product Belief Propagation (BP) is a popular message-passing algorithm
for computing a Maximum-A-Posteriori (MAP) assignment over a distribution
represented by a Graphical Model (GM). It has been shown that BP can solve a
number of combinatorial optimization problems including minimum weight
matching, shortest path, network flow and vertex cover under the following
common assumption: the respective Linear Programming (LP) relaxation is tight,
i.e., no integrality gap is present. However, when LP shows an integrality gap,
no model has been known which can be solved systematically via sequential
applications of BP. In this paper, we develop the first such algorithm, coined
Blossom-BP, for solving the minimum weight matching problem over arbitrary
graphs. Each step of the sequential algorithm requires applying BP over a
modified graph constructed by contractions and expansions of blossoms, i.e.,
odd sets of vertices. Our scheme guarantees termination in O(n^2) of BP runs,
where n is the number of vertices in the original graph. In essence, the
Blossom-BP offers a distributed version of the celebrated Edmonds' Blossom
algorithm by jumping at once over many sub-steps with a single BP. Moreover,
our result provides an interpretation of the Edmonds' algorithm as a sequence
of LPs.
Learning Deep Neural Network Policies with Continuous Memory States
Authors: Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel
Date: 2015-07-05 00:00:00
Tasks: continuous-control, Continuous Control, Memorization

Policy learning for partially observed control tasks requires policies that
can remember salient information from past observations. In this paper, we
present a method for learning policies with internal memory for
high-dimensional, continuous systems, such as robotic manipulators. Our
approach consists of augmenting the state and action space of the system with
continuous-valued memory states that the policy can read from and write to.
Learning general-purpose policies with this type of memory representation
directly is difficult, because the policy must automatically figure out the
most salient information to memorize at each time step. We show that, by
decomposing this policy search problem into a trajectory optimization phase and
a supervised learning phase through a method called guided policy search, we
can acquire policies with effective memorization and recall strategies.
Intuitively, the trajectory optimization phase chooses the values of the memory
states that will make it easier for the policy to produce the right action in
future states, while the supervised learning phase encourages the policy to use
memorization actions to produce those memory states. We evaluate our method on
tasks involving continuous control in manipulation and navigation settings, and
show that our method can learn complex policies that successfully complete a
range of tasks that require memory.
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation
Authors: Shayan Poursoltan, Frank Neumann
Date: 2015-09-23 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary algorithms have been frequently applied to constrained
continuous optimisation problems. We carry out feature based comparisons of
different types of evolutionary algorithms such as evolution strategies,
differential evolution and particle swarm optimisation for constrained
continuous optimisation. In our study, we examine how sets of constraints
influence the difficulty of obtaining close to optimal solutions. Using a
multi-objective approach, we evolve constrained continuous problems having a
set of linear and/or quadratic constraints where the different evolutionary
approaches show a significant difference in performance. Afterwards, we discuss
the features of the constraints that exhibit a difference in performance of the
different evolutionary approaches under consideration.
The Computational Principles of Learning Ability
Authors: Hao Wu
Date: 2015-09-23 00:00:00
Tasks: Common Sense Reasoning

It has been quite a long time since AI researchers in the field of computer
science stop talking about simulating human intelligence or trying to explain
how brain works. Recently, represented by deep learning techniques, the field
of machine learning is experiencing unprecedented prosperity and some
applications with near human-level performance bring researchers confidence to
imply that their approaches are the promising candidate for understanding the
mechanism of human brain. However apart from several ancient philological
criteria and some imaginary black box tests (Turing test, Chinese room) there
is no computational level explanation, definition or criteria about
intelligence or any of its components. Base on the common sense that learning
ability is one critical component of intelligence and inspect from the
viewpoint of mapping relations, this paper presents two laws which explains
what is the "learning ability" as we familiar with and under what conditions a
mapping relation can be acknowledged as "Learning Model".
Probabilistic Group Testing under Sum Observations: A Parallelizable 2-Approximation for Entropy Loss
Authors: Weidong Han, Purnima Rajan, Peter I. Frazier, Bruno M. Jedynak
Date: 2014-07-16 00:00:00

We consider the problem of group testing with sum observations and noiseless
answers, in which we aim to locate multiple objects by querying the number of
objects in each of a sequence of chosen sets. We study a probabilistic setting
with entropy loss, in which we assume a joint Bayesian prior density on the
locations of the objects and seek to choose the sets queried to minimize the
expected entropy of the Bayesian posterior distribution after a fixed number of
questions. We present a new non-adaptive policy, called the dyadic policy, show
it is optimal among non-adaptive policies, and is within a factor of two of
optimal among adaptive policies. This policy is quick to compute, its
nonadaptive nature makes it easy to parallelize, and our bounds show it
performs well even when compared with adaptive policies. We also study an
adaptive greedy policy, which maximizes the one-step expected reduction in
entropy, and show that it performs at least as well as the dyadic policy,
offering greater query efficiency but reduced parallelism. Numerical
experiments demonstrate that both procedures outperform a divide-and-conquer
benchmark policy from the literature, called sequential bifurcation, and show
how these procedures may be applied in a stylized computer vision problem.
Density Estimation via Discrepancy
Authors: Kun Yang, Hao Su, Wing Hung Wang
Date: 2015-09-23 00:00:00
Tasks: Density Estimation

Given i.i.d samples from some unknown continuous density on hyper-rectangle
$[0, 1]^d$, we attempt to learn a piecewise constant function that approximates
this underlying density non-parametrically. Our density estimate is defined on
a binary split of $[0, 1]^d$ and built up sequentially according to discrepancy
criteria; the key ingredient is to control the discrepancy adaptively in each
sub-rectangle to achieve overall bound. We prove that the estimate, even though
simple as it appears, preserves most of the estimation power. By exploiting its
structure, it can be directly applied to some important pattern recognition
tasks such as mode seeking and density landscape exploration. We demonstrate
its applicability through simulations and examples.
Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours
Authors: Lerrel Pinto, Abhinav Gupta
Date: 2015-09-23 00:00:00
Tasks: Binary Classification

Current learning-based robot grasping approaches exploit human-labeled
datasets for training the models. However, there are two problems with such a
methodology: (a) since each object can be grasped in multiple ways, manually
labeling grasp locations is not a trivial task; (b) human labeling is biased by
semantics. While there have been attempts to train robots using trial-and-error
experiments, the amount of data used in such experiments remains substantially
low and hence makes the learner prone to over-fitting. In this paper, we take
the leap of increasing the available training data to 40 times more than prior
work, leading to a dataset size of 50K data points collected over 700 hours of
robot grasping attempts. This allows us to train a Convolutional Neural Network
(CNN) for the task of predicting grasp locations without severe overfitting. In
our formulation, we recast the regression problem to an 18-way binary
classification over image patches. We also present a multi-stage learning
approach where a CNN trained in one stage is used to collect hard negatives in
subsequent stages. Our experiments clearly show the benefit of using
large-scale datasets (and multi-stage training) for the task of grasping. We
also compare to several baselines and show state-of-the-art performance on
generalization to unseen objects for grasping.
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
Authors: Yuchen Zhang, Michael. I. Jordan
Date: 2015-06-24 00:00:00
Tasks: Collaborative Filtering, Distributed Computing

Stochastic algorithms are efficient approaches to solving machine learning
and optimization problems. In this paper, we propose a general framework called
Splash for parallelizing stochastic algorithms on multi-node distributed
systems. Splash consists of a programming interface and an execution engine.
Using the programming interface, the user develops sequential stochastic
algorithms without concerning any detail about distributed computing. The
algorithm is then automatically parallelized by a communication-efficient
execution engine. We provide theoretical justifications on the optimal rate of
convergence for parallelizing stochastic gradient descent. Splash is built on
top of Apache Spark. The real-data experiments on logistic regression,
collaborative filtering and topic modeling verify that Splash yields
order-of-magnitude speedup over single-thread stochastic algorithms and over
state-of-the-art implementations on Spark.
Training deep neural networks with low precision multiplications
Authors: Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
Date: 2014-12-22 00:00:00

Multipliers are the most space and power-hungry arithmetic operators of the
digital implementation of deep neural networks. We train a set of
state-of-the-art neural networks (Maxout networks) on three benchmark datasets:
MNIST, CIFAR-10 and SVHN. They are trained with three distinct formats:
floating point, fixed point and dynamic fixed point. For each of those datasets
and for each of those formats, we assess the impact of the precision of the
multiplications on the final error after training. We find that very low
precision is sufficient not just for running trained networks but also for
training them. For example, it is possible to train Maxout networks with 10
bits multiplications.
Learning Wake-Sleep Recurrent Attention Models
Authors: Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov, Brendan Frey
Date: 2015-09-22 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Caption Generation, Computational Efficiency, General Classification, image-classification, Image Classification

Despite their success, convolutional neural networks are computationally
expensive because they must examine all image locations. Stochastic
attention-based models have been shown to improve computational efficiency at
test time, but they remain difficult to train because of intractable posterior
inference and high variance in the stochastic gradient estimates. Borrowing
techniques from the literature on training deep generative models, we present
the Wake-Sleep Recurrent Attention Model, a method for training stochastic
attention networks which improves posterior inference and which reduces the
variability in the stochastic gradients. We show that our method can greatly
speed up the training time for stochastic attention networks in the domains of
image classification and caption generation.
Bandit Label Inference for Weakly Supervised Learning
Authors: Ke Li, Jitendra Malik
Date: 2015-09-22 00:00:00
Tasks: Weakly-supervised Learning

The scarcity of data annotated at the desired level of granularity is a
recurring issue in many applications. Significant amounts of effort have been
devoted to developing weakly supervised methods tailored to each individual
setting, which are often carefully designed to take advantage of the particular
properties of weak supervision regimes, form of available data and prior
knowledge of the task at hand. Unfortunately, it is difficult to adapt these
methods to new tasks and/or forms of data, which often require different weak
supervision regimes or models. We present a general-purpose method that can
solve any weakly supervised learning problem irrespective of the weak
supervision regime or the model. The proposed method turns any off-the-shelf
strongly supervised classifier into a weakly supervised classifier and allows
the user to specify any arbitrary weakly supervision regime via a loss
function. We apply the method to several different weak supervision regimes and
demonstrate competitive results compared to methods specifically engineered for
those settings.
Denoising without access to clean data using a partitioned autoencoder
Authors: Dan Stowell, Richard E. Turner
Date: 2015-09-20 00:00:00
Tasks: Denoising

Training a denoising autoencoder neural network requires access to truly
clean data, a requirement which is often impractical. To remedy this, we
introduce a method to train an autoencoder using only noisy data, having
examples with and without the signal class of interest. The autoencoder learns
a partitioned representation of signal and noise, learning to reconstruct each
separately. We illustrate the method by denoising birdsong audio (available
abundantly in uncontrolled noisy datasets) using a convolutional autoencoder.
Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games
Authors: Nikolai Yakovenko, Liangliang Cao, Colin Raffel, James Fan
Date: 2015-09-22 00:00:00
Tasks: Card Games, Game of Poker

Poker is a family of card games that includes many variations. We hypothesize
that most poker games can be solved as a pattern matching problem, and propose
creating a strong poker playing system based on a unified poker representation.
Our poker player learns through iterative self-play, and improves its
understanding of the game by training on the results of its previous actions
without sophisticated domain knowledge. We evaluate our system on three poker
games: single player video poker, two-player Limit Texas Hold'em, and finally
two-player 2-7 triple draw poker. We show that our model can quickly learn
patterns in these very different poker games while it improves from zero
knowledge to a competitive player against human experts.
  The contributions of this paper include: (1) a novel representation for poker
games, extendable to different poker variations, (2) a CNN based learning model
that can effectively learn the patterns in three different games, and (3) a
self-trained system that significantly beats the heuristic-based program on
which it is trained, and our system is competitive against human expert
players.
Detecting Table Region in PDF Documents Using Distant Supervision
Authors: Miao Fan, Doo Soon Kim
Date: 2015-06-29 00:00:00
Tasks: Articles, Table Detection, Table Recognition

Superior to state-of-the-art approaches which compete in table recognition
with 67 annotated government reports in PDF format released by {\it ICDAR 2013
Table Competition}, this paper contributes a novel paradigm leveraging
large-scale unlabeled PDF documents to open-domain table detection. We
integrate the paradigm into our latest developed system ({\it PdfExtra}) to
detect the region of tables by means of 9,466 academic articles from the entire
repository of {\it ACL Anthology}, where almost all papers are archived by PDF
format without annotation for tables. The paradigm first designs heuristics to
automatically construct weakly labeled data. It then feeds diverse evidences,
such as layouts of documents and linguistic features, which are extracted by
{\it Apache PDFBox} and processed by {\it Stanford NLP} toolkit, into different
canonical classifiers. We finally use these classifiers, i.e. {\it Naive
Bayes}, {\it Logistic Regression} and {\it Support Vector Machine}, to
collaboratively vote on the region of tables. Experimental results show that
{\it PdfExtra} achieves a great leap forward, compared with the
state-of-the-art approach. Moreover, we discuss the factors of different
features, learning models and even domains of documents that may impact the
performance. Extensive evaluations demonstrate that our paradigm is compatible
enough to leverage various features and learning models for open-domain table
region detection within PDF files.