pwc-archive/papers-with-abstracts huggingface dataset:
Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge
Authors: Nicholas Locascio, Karthik Narasimhan, Eduardo DeLeon, Nate Kushman, Regina Barzilay
Date: 2016-08-09 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Natural Language Queries

This paper explores the task of translating natural language queries into
regular expressions which embody their meaning. In contrast to prior work, the
proposed neural model does not utilize domain-specific crafting, learning to
translate directly from a parallel corpus. To fully explore the potential of
neural models, we propose a methodology for collecting a large corpus of
regular expression, natural language pairs. Our resulting model achieves a
performance gain of 19.6% over previous state-of-the-art models.
Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
Authors: Antonio Valerio Miceli Barone
Date: 2016-08-09 00:00:00
Proceeding: WS 2016 8
Tasks: Sentence

Current approaches to learning vector representations of text that are
compatible between different languages usually require some amount of parallel
text, aligned at word, sentence or at least document level. We hypothesize
however, that different natural languages share enough semantic structure that
it should be possible, in principle, to learn compatible vector representations
just by analyzing the monolingual distribution of words.
  In order to evaluate this hypothesis, we propose a scheme to map word vectors
trained on a source language to vectors semantically compatible with word
vectors trained on a target language using an adversarial autoencoder.
  We present preliminary qualitative results and discuss possible future
developments of this technique, such as applications to cross-lingual sentence
representations.
Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics
Authors: John A. Quinn, Rose Nakasi, Pius K. B. Mugagga, Patrick Byanyima, William Lubega, Alfred Andama
Date: 2016-08-09 00:00:00
Tasks: Object Recognition, Specificity

Point of care diagnostics using microscopy and computer vision methods have
been applied to a number of practical problems, and are particularly relevant
to low-income, high disease burden areas. However, this is subject to the
limitations in sensitivity and specificity of the computer vision methods used.
In general, deep learning has recently revolutionised the field of computer
vision, in some cases surpassing human performance for other object recognition
tasks. In this paper, we evaluate the performance of deep convolutional neural
networks on three different microscopy tasks: diagnosis of malaria in thick
blood smears, tuberculosis in sputum samples, and intestinal parasite eggs in
stool samples. In all cases accuracy is very high and substantially better than
an alternative approach more representative of traditional medical imaging
techniques.
Robust High-Dimensional Linear Regression
Authors: Chang Liu, Bo Li, Yevgeniy Vorobeychik, Alina Oprea
Date: 2016-08-07 00:00:00
Tasks: Dimensionality Reduction, regression, Vocal Bursts Intensity Prediction

The effectiveness of supervised learning techniques has made them ubiquitous
in research and practice. In high-dimensional settings, supervised learning
commonly relies on dimensionality reduction to improve performance and identify
the most important factors in predicting outcomes. However, the economic
importance of learning has made it a natural target for adversarial
manipulation of training data, which we term poisoning attacks. Prior
approaches to dealing with robust supervised learning rely on strong
assumptions about the nature of the feature matrix, such as feature
independence and sub-Gaussian noise with low variance. We propose an integrated
method for robust regression that relaxes these assumptions, assuming only that
the feature matrix can be well approximated by a low-rank matrix. Our
techniques integrate improved robust low-rank matrix approximation and robust
principle component regression, and yield strong performance guarantees.
Moreover, we experimentally show that our methods significantly outperform
state of the art both in running time and prediction error.
Neuroevolution-Based Inverse Reinforcement Learning
Authors: Karan K. Budhraja, Tim Oates
Date: 2016-08-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The problem of Learning from Demonstration is targeted at learning to perform
tasks based on observed examples. One approach to Learning from Demonstration
is Inverse Reinforcement Learning, in which actions are observed to infer
rewards. This work combines a feature based state evaluation approach to
Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying
neural networks based on their performance on a given task. Neural networks are
used to learn from a demonstrated expert policy and are evolved to generate a
policy similar to the demonstration. The algorithm is discussed and evaluated
against competitive feature-based Inverse Reinforcement Learning approaches. At
the cost of execution time, neural networks allow for non-linear combinations
of features in state evaluations. These valuations may correspond to state
value or state reward. This results in better correspondence to observed
examples as opposed to using linear combinations. This work also extends
existing work on Bayesian Non-Parametric Feature Construction for Inverse
Reinforcement Learning by using non-linear combinations of intermediate data to
improve performance. The algorithm is observed to be specifically suitable for
a linearly solvable non-deterministic Markov Decision Processes in which
multiple rewards are sparsely scattered in state space. A conclusive
performance hierarchy between evaluated algorithms is presented.
Temporal Attention Model for Neural Machine Translation
Authors: Baskaran Sankaran, Haitao Mi, Yaser Al-Onaizan, Abe Ittycheriah
Date: 2016-08-09 00:00:00
Tasks: Decoder, Machine Translation, model, NMT, Sentence, Translation

Attention-based Neural Machine Translation (NMT) models suffer from attention
deficiency issues as has been observed in recent research. We propose a novel
mechanism to address some of these limitations and improve the NMT attention.
Specifically, our approach memorizes the alignments temporally (within each
sentence) and modulates the attention with the accumulated temporal memory, as
the decoder generates the candidate translation. We compare our approach
against the baseline NMT model and two other related approaches that address
this issue either explicitly or implicitly. Large-scale experiments on two
language pairs show that our approach achieves better and robust gains over the
baseline and related NMT approaches. Our model further outperforms strong SMT
baselines in some settings even without using ensembles.
Credibilistic TOPSIS Model for Evaluation and Selection of Municipal Solid Waste Disposal Methods
Authors: Jagannath Roy, Krishnendu Adhikary, Samarjit Kar
Date: 2016-06-29 00:00:00
Tasks: Decision Making, Management, Sensitivity

Municipal solid waste management (MSWM) is a challenging issue of urban
development in developing countries. Each country having different
socio-economic-environmental background, might not accept a particular disposal
method as the optimal choice. Selection of suitable disposal method in MSWM,
under vague and imprecise information can be considered as multi criteria
decision making problem (MCDM). In the present paper, TOPSIS (Technique for
Order Preference by Similarity to Ideal Solution) methodology is extended based
on credibility theory for evaluating the performances of MSW disposal methods
under some criteria fixed by experts. The proposed model helps decision makers
to choose a preferable alternative for their municipal area. A sensitivity
analysis by our proposed model confirms this fact.
Linear Regression with an Unknown Permutation: Statistical and Computational Limits
Authors: Ashwin Pananjady, Martin J. Wainwright, Thomas A. Courtade
Date: 2016-08-09 00:00:00
Tasks: regression

Consider a noisy linear observation model with an unknown permutation, based
on observing $y = \Pi^* A x^* + w$, where $x^* \in \mathbb{R}^d$ is an unknown
vector, $\Pi^*$ is an unknown $n \times n$ permutation matrix, and $w \in
\mathbb{R}^n$ is additive Gaussian noise. We analyze the problem of permutation
recovery in a random design setting in which the entries of the matrix $A$ are
drawn i.i.d. from a standard Gaussian distribution, and establish sharp
conditions on the SNR, sample size $n$, and dimension $d$ under which $\Pi^*$
is exactly and approximately recoverable. On the computational front, we show
that the maximum likelihood estimate of $\Pi^*$ is NP-hard to compute, while
also providing a polynomial time algorithm when $d =1$.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Authors: Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
Date: 2016-02-16 00:00:00
Tasks: Image Attribution, image-classification, Image Classification, Interpretability Techniques for Deep Learning, Interpretable Machine Learning, Prediction

Despite widespread adoption, machine learning models remain mostly black
boxes. Understanding the reasons behind predictions is, however, quite
important in assessing trust, which is fundamental if one plans to take action
based on a prediction, or when choosing whether to deploy a new model. Such
understanding also provides insights into the model, which can be used to
transform an untrustworthy model or prediction into a trustworthy one. In this
work, we propose LIME, a novel explanation technique that explains the
predictions of any classifier in an interpretable and faithful manner, by
learning an interpretable model locally around the prediction. We also propose
a method to explain models by presenting representative individual predictions
and their explanations in a non-redundant way, framing the task as a submodular
optimization problem. We demonstrate the flexibility of these methods by
explaining different models for text (e.g. random forests) and image
classification (e.g. neural networks). We show the utility of explanations via
novel experiments, both simulated and with human subjects, on various scenarios
that require trust: deciding if one should trust a prediction, choosing between
models, improving an untrustworthy classifier, and identifying why a classifier
should not be trusted.
Bag of Tricks for Efficient Text Classification
Authors: Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
Date: 2016-07-06 00:00:00
Proceeding: EACL 2017 4
Tasks: CPU, Emotion Recognition in Conversation, General Classification, Sentiment Analysis, Text Classification

This paper explores a simple and efficient baseline for text classification.
Our experiments show that our fast text classifier fastText is often on par
with deep learning classifiers in terms of accuracy, and many orders of
magnitude faster for training and evaluation. We can train fastText on more
than one billion words in less than ten minutes using a standard multicore~CPU,
and classify half a million sentences among~312K classes in less than a minute.
Automatically selecting inference algorithms for discrete energy minimisation
Authors: Paul Henderson, Vittorio Ferrari
Date: 2015-11-19 00:00:00

Minimisation of discrete energies defined over factors is an important
problem in computer vision, and a vast number of MAP inference algorithms have
been proposed. Different inference algorithms perform better on factor graph
models (GMs) from different underlying problem classes, and in general it is
difficult to know which algorithm will yield the lowest energy for a given GM.
To mitigate this difficulty, survey papers advise the practitioner on what
algorithms perform well on what classes of models. We take the next step
forward, and present a technique to automatically select the best inference
algorithm for an input GM. We validate our method experimentally on an extended
version of the OpenGM2 benchmark, containing a diverse set of vision problems.
On average, our method selects an inference algorithm yielding labellings with
96% of variables the same as the best available algorithm.
Classification with the pot-pot plot
Authors: Oleksii Pokotylo, Karl Mosler
Date: 2016-08-09 00:00:00
Tasks: Classification, General Classification

We propose a procedure for supervised classification that is based on
potential functions. The potential of a class is defined as a kernel density
estimate multiplied by the class's prior probability. The method transforms the
data to a potential-potential (pot-pot) plot, where each data point is mapped
to a vector of potentials. Separation of the classes, as well as classification
of new data points, is performed on this plot. For this, either the
$\alpha$-procedure ($\alpha$-P) or $k$-nearest neighbors ($k$-NN) are employed.
For data that are generated from continuous distributions, these classifiers
prove to be strongly Bayes-consistent. The potentials depend on the kernel and
its bandwidth used in the density estimate. We investigate several variants of
bandwidth selection, including joint and separate pre-scaling and a bandwidth
regression approach. The new method is applied to benchmark data from the
literature, including simulated data sets as well as 50 sets of real data. It
compares favorably to known classification methods such as LDA, QDA, max kernel
density estimates, $k$-NN, and $DD$-plot classification using depth functions.
Liftago On-Demand Transport Dataset and Market Formation Algorithm Based on Machine Learning
Authors: Jan Mrkos, Jan Drchal, Malcolm Egan, Michal Jakob
Date: 2016-08-09 00:00:00
Tasks: BIG-bench Machine Learning

This document serves as a technical report for the analysis of on-demand
transport dataset. Moreover we show how the dataset can be used to develop a
market formation algorithm based on machine learning. Data used in this work
comes from Liftago, a Prague based company which connects taxi drivers and
customers through a smartphone app. The dataset is analysed from the
machine-learning perspective: we give an overview of features available as well
as results of feature ranking. Later we propose the SImple Data-driven MArket
Formation (SIDMAF) algorithm which aims to improve a relevance while connecting
customers with relevant drivers. We compare the heuristics currently used by
Liftago with SIDMAF using two key performance indicators.
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation
Authors: Dong Huang, Chang-Dong Wang, Jian-Huang Lai, Yun Liang, Shan Bian, Yu Chen
Date: 2016-08-03 00:00:00
Tasks: Clustering, Ensemble Learning, parameter estimation

Support vector clustering (SVC) is a versatile clustering technique that is
able to identify clusters of arbitrary shapes by exploiting the kernel trick.
However, one hurdle that restricts the application of SVC lies in its
sensitivity to the kernel parameter and the trade-off parameter. Although many
extensions of SVC have been developed, to the best of our knowledge, there is
still no algorithm that is able to effectively estimate the two crucial
parameters in SVC without supervision. In this paper, we propose a novel
support vector clustering approach termed ensemble-driven support vector
clustering (EDSVC), which for the first time tackles the automatic parameter
estimation problem for SVC based on ensemble learning, and is capable of
producing robust clustering results in a purely unsupervised manner.
Experimental results on multiple real-world datasets demonstrate the
effectiveness of our approach.
Multi-Task Cross-Lingual Sequence Tagging from Scratch
Authors: Zhilin Yang, Ruslan Salakhutdinov, William Cohen
Date: 2016-03-20 00:00:00
Tasks: Chunking, Feature Engineering, NER, POS, POS Tagging

We present a deep hierarchical recurrent neural network for sequence tagging.
Given a sequence of words, our model employs deep gated recurrent units on both
character and word levels to encode morphology and context information, and
applies a conditional random field layer to predict the tags. Our model is task
independent, language independent, and feature engineering free. We further
extend our model to multi-task and cross-lingual joint training by sharing the
architecture and parameters. Our model achieves state-of-the-art results in
multiple languages on several benchmark tasks including POS tagging, chunking,
and NER. We also demonstrate that multi-task and cross-lingual joint training
can improve the performance in various cases.
Camera Pose Estimation from Lines using Plücker Coordinates
Authors: Bronislav Přibyl, Pavel Zemčík, Martin Čadík
Date: 2016-08-09 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation, Position

Correspondences between 3D lines and their 2D images captured by a camera are
often used to determine position and orientation of the camera in space. In
this work, we propose a novel algebraic algorithm to estimate the camera pose.
We parameterize 3D lines using Pl\"ucker coordinates that allow linear
projection of the lines into the image. A line projection matrix is estimated
using Linear Least Squares and the camera pose is then extracted from the
matrix. An algebraic approach to handle mismatched line correspondences is also
included. The proposed algorithm is an order of magnitude faster yet comparably
accurate and robust to the state-of-the-art, it does not require
initialization, and it yields only one solution. The described method requires
at least 9 lines and is particularly suitable for scenarios with 25 and more
lines, as also shown in the results.
Efficient Dodgson-Score Calculation Using Heuristics and Parallel Computing
Authors: Arne Recknagel, Tarek R. Besold
Date: 2015-07-21 00:00:00
Tasks: scoring rule

Conflict of interest is the permanent companion of any population of agents
(computational or biological). For that reason, the ability to compromise is of
paramount importance, making voting a key element of societal mechanisms. One
of the voting procedures most often discussed in the literature and, due to its
intuitiveness, also conceptually quite appealing is Charles Dodgson's scoring
rule, basically using the respective closeness to being a Condorcet winner for
evaluating competing alternatives. In this paper, we offer insights on the
practical limits of algorithms computing the exact Dodgson scores from a number
of votes. While the problem itself is theoretically intractable, this work
proposes and analyses five different solutions which try distinct approaches to
practically solve the issue in an effective manner. Additionally, three of the
discussed procedures can be run in parallel which has the potential of
drastically reducing the problem size.
Deep Convolution Networks for Compression Artifacts Reduction
Authors: Ke Yu, Chao Dong, Chen Change Loy, Xiaoou Tang
Date: 2016-08-09 00:00:00
Tasks: Blocking, Transfer Learning

Lossy compression introduces complex compression artifacts, particularly
blocking artifacts, ringing effects and blurring. Existing algorithms either
focus on removing blocking artifacts and produce blurred output, or restore
sharpened images that are accompanied with ringing effects. Inspired by the
success of deep convolutional networks (DCN) on superresolution, we formulate a
compact and efficient network for seamless attenuation of different compression
artifacts. To meet the speed requirement of real-world applications, we further
accelerate the proposed baseline model by layer decomposition and joint use of
large-stride convolutional and deconvolutional layers. This also leads to a
more general CNN framework that has a close relationship with the conventional
Multi-Layer Perceptron (MLP). Finally, the modified network achieves a speed up
of 7.5 times with almost no performance loss compared to the baseline model. We
also demonstrate that a deeper model can be effectively trained with features
learned in a shallow network. Following a similar "easy to hard" idea, we
systematically investigate three practical transfer settings and show the
effectiveness of transfer learning in low-level vision problems. Our method
shows superior performance than the state-of-the-art methods both on benchmark
datasets and a real-world use case.
Resolving Spatial-Time Conflicts In A Set Of Any-angle Or Angle-constrained Grid Paths
Authors: Konstantin Yakovlev, Anton Andreychuk
Date: 2016-08-09 00:00:00
Tasks: Multi-Agent Path Finding

We study the multi-agent path finding problem (MAPF) for a group of agents
which are allowed to move into arbitrary directions on a 2D square grid. We
focus on centralized conflict resolution for independently computed plans. We
propose an algorithm that eliminates conflicts by using local re-planning and
introducing time offsets to the execution of paths by different agents.
Experimental results show that the algorithm can find high quality
conflict-free solutions at low computational cost.
Convolutional Oriented Boundaries
Authors: Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez, Luc van Gool
Date: 2016-08-09 00:00:00
Tasks: Contour Detection, General Classification, image-classification, Image Classification, Segmentation

We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for contour detection
and it uses a novel sparse boundary representation for hierarchical
segmentation; it gives a significant leap in performance over the
state-of-the-art, and it generalizes very well to unseen categories and
datasets. Particularly, we show that learning to estimate not only contour
strength but also orientation provides more accurate results. We perform
extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and
MS-COCO, showing that COB provides state-of-the-art contours, region
hierarchies, and object proposals in all datasets.
On Lower Bounds for Regret in Reinforcement Learning
Authors: Ian Osband, Benjamin Van Roy
Date: 2016-08-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This is a brief technical note to clarify the state of lower bounds on regret
for reinforcement learning. In particular, this paper:
  - Reproduces a lower bound on regret for reinforcement learning, similar to
the result of Theorem 5 in the journal UCRL2 paper (Jaksch et al 2010).
  - Clarifies that the proposed proof of Theorem 6 in the REGAL paper (Bartlett
and Tewari 2009) does not hold using the standard techniques without further
work. We suggest that this result should instead be considered a conjecture as
it has no rigorous proof.
  - Suggests that the conjectured lower bound given by (Bartlett and Tewari
2009) is incorrect and, in fact, it is possible to improve the scaling of the
upper bound to match the weaker lower bounds presented in this paper.
  We hope that this note serves to clarify existing results in the field of
reinforcement learning and provides interesting motivation for future work.