pwc-archive/papers-with-abstracts huggingface dataset:
Sequential Attention: A Context-Aware Alignment Function for Machine Reading
Authors: Sebastian Brarda, Philip Yeres, Samuel R. Bowman
Date: 2017-05-05 00:00:00
Proceeding: WS 2017 8
Tasks: Reading Comprehension

In this paper we propose a neural network model with a novel Sequential
Attention layer that extends soft attention by assigning weights to words in an
input sequence in a way that takes into account not just how well that word
matches a query, but how well surrounding words match. We evaluate this
approach on the task of reading comprehension (on the Who did What and CNN
datasets) and show that it dramatically improves a strong baseline--the
Stanford Reader--and is competitive with the state of the art.
Relating Complexity-theoretic Parameters with SAT Solver Performance
Authors: Edward Zulkoski, Ruben Martins, Christoph Wintersteiger, Robert Robere, Jia Liang, Krzysztof Czarnecki, Vijay Ganesh
Date: 2017-06-26 00:00:00

Over the years complexity theorists have proposed many structural parameters
to explain the surprising efficiency of conflict-driven clause-learning (CDCL)
SAT solvers on a wide variety of large industrial Boolean instances. While some
of these parameters have been studied empirically, until now there has not been
a unified comparative study of their explanatory power on a comprehensive
benchmark. We correct this state of affairs by conducting a large-scale
empirical evaluation of CDCL SAT solver performance on nearly 7000 industrial
and crafted formulas against several structural parameters such as backdoors,
treewidth, backbones, and community structure.
  Our study led us to several results. First, we show that while such
parameters only weakly correlate with CDCL solving time, certain combinations
of them yield much better regression models. Second, we show how some
parameters can be used as a "lens" to better understand the efficiency of
different solving heuristics. Finally, we propose a new complexity-theoretic
parameter, which we call learning-sensitive with restarts (LSR) backdoors, that
extends the notion of learning-sensitive (LS) backdoors to incorporate restarts
and discuss algorithms to compute them. We mathematically prove that for
certain class of instances minimal LSR-backdoors are exponentially smaller than
minimal-LS backdoors.
Developing Bug-Free Machine Learning Systems With Formal Mathematics
Authors: Daniel Selsam, Percy Liang, David L. Dill
Date: 2017-06-26 00:00:00
Proceeding: ICML 2017 8
Tasks: BIG-bench Machine Learning

Noisy data, non-convex objectives, model misspecification, and numerical
instability can all cause undesired behaviors in machine learning systems. As a
result, detecting actual implementation errors can be extremely difficult. We
demonstrate a methodology in which developers use an interactive proof
assistant to both implement their system and to state a formal theorem defining
what it means for their system to be correct. The process of proving this
theorem interactively in the proof assistant exposes all implementation errors
since any error in the program would cause the proof to fail. As a case study,
we implement a new system, Certigrad, for optimizing over stochastic
computation graphs, and we generate a formal (i.e. machine-checkable) proof
that the gradients sampled by the system are unbiased estimates of the true
mathematical gradients. We train a variational autoencoder using Certigrad and
find the performance comparable to training the same model in TensorFlow.
Tensor-on-tensor regression
Authors: Eric F. Lock
Date: 2017-01-04 00:00:00
Tasks: regression

We propose a framework for the linear prediction of a multi-way array (i.e.,
a tensor) from another multi-way array of arbitrary dimension, using the
contracted tensor product. This framework generalizes several existing
approaches, including methods to predict a scalar outcome from a tensor, a
matrix from a matrix, or a tensor from a scalar. We describe an approach that
exploits the multiway structure of both the predictors and the outcomes by
restricting the coefficients to have reduced CP-rank. We propose a general and
efficient algorithm for penalized least-squares estimation, which allows for a
ridge (L_2) penalty on the coefficients. The objective is shown to give the
mode of a Bayesian posterior, which motivates a Gibbs sampling algorithm for
inference. We illustrate the approach with an application to facial image data.
An R package is available at https://github.com/lockEF/MultiwayRegression .
Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification
Authors: John McKay, Vishal Monga, Raghu G. Raj
Date: 2017-06-26 00:00:00
Tasks: Anomaly Detection, Classification, Dictionary Learning, General Classification

Sonar imaging has seen vast improvements over the last few decades due in
part to advances in synthetic aperture Sonar (SAS). Sophisticated
classification techniques can now be used in Sonar automatic target recognition
(ATR) to locate mines and other threatening objects. Among the most promising
of these methods is sparse reconstruction-based classification (SRC) which has
shown an impressive resiliency to noise, blur, and occlusion. We present a
coherent strategy for expanding upon SRC for Sonar ATR that retains SRC's
robustness while also being able to handle targets with diverse geometric
arrangements, bothersome Rayleigh noise, and unavoidable background clutter.
Our method, pose corrected sparsity (PCS), incorporates a novel interpretation
of a spike and slab probability distribution towards use as a Bayesian prior
for class-specific discrimination in combination with a dictionary learning
scheme for localized patch extractions. Additionally, PCS offers the potential
for anomaly detection in order to avoid false identifications of tested objects
from outside the training set with no additional training required. Compelling
results are shown using a database provided by the United States Naval Surface
Warfare Center.
A nonparametric sequential test for online randomized experiments
Authors: Vineet Abhishek, Shie Mannor
Date: 2016-10-08 00:00:00

We propose a nonparametric sequential test that aims to address two practical
problems pertinent to online randomized experiments: (i) how to do a hypothesis
test for complex metrics; (ii) how to prevent type $1$ error inflation under
continuous monitoring. The proposed test does not require knowledge of the
underlying probability distribution generating the data. We use the bootstrap
to estimate the likelihood for blocks of data followed by mixture sequential
probability ratio test. We validate this procedure on data from a major online
e-commerce website. We show that the proposed test controls type $1$ error at
any time, has good power, is robust to misspecification in the distribution
generating the data, and allows quick inference in online randomized
experiments.
Learning Local Feature Aggregation Functions with Backpropagation
Authors: Angelos Katharopoulos, Despoina Paschalidou, Christos Diou, Anastasios Delopoulos
Date: 2017-06-26 00:00:00
Tasks: General Classification

This paper introduces a family of local feature aggregation functions and a
novel method to estimate their parameters, such that they generate optimal
representations for classification (or any task that can be expressed as a cost
function minimization problem). To achieve that, we compose the local feature
aggregation function with the classifier cost function and we backpropagate the
gradient of this cost function in order to update the local feature aggregation
function parameters. Experiments on synthetic datasets indicate that our method
discovers parameters that model the class-relevant information in addition to
the local feature space. Further experiments on a variety of motion and visual
descriptors, both on image and video datasets, show that our method outperforms
other state-of-the-art local feature aggregation functions, such as Bag of
Words, Fisher Vectors and VLAD, by a large margin.
Using Frame Theoretic Convolutional Gridding for Robust Synthetic Aperture Sonar Imaging
Authors: John McKay, Anne Gelb, Vishal Monga, Raghu Raj
Date: 2017-06-26 00:00:00

Recent progress in synthetic aperture sonar (SAS) technology and processing
has led to significant advances in underwater imaging, outperforming previously
common approaches in both accuracy and efficiency. There are, however, inherent
limitations to current SAS reconstruction methodology. In particular, popular
and efficient Fourier domain SAS methods require a 2D interpolation which is
often ill conditioned and inaccurate, inevitably reducing robustness with
regard to speckle and inaccurate sound-speed estimation. To overcome these
issues, we propose using the frame theoretic convolution gridding (FTCG)
algorithm to handle the non-uniform Fourier data. FTCG extends upon non-uniform
fast Fourier transform (NUFFT) algorithms by casting the NUFFT as an
approximation problem given Fourier frame data. The FTCG has been show to yield
improved accuracy at little more computational cost. Using simulated data, we
outline how the FTCG can be used to enhance current SAS processing.
Detecting Small Signs from Large Images
Authors: Zibo Meng, Xiaochuan Fan, Xin Chen, Min Chen, Yan Tong
Date: 2017-06-26 00:00:00
Tasks: Object, object-detection, Object Detection

In the past decade, Convolutional Neural Networks (CNNs) have been
demonstrated successful for object detections. However, the size of network
input is limited by the amount of memory available on GPUs. Moreover,
performance degrades when detecting small objects. To alleviate the memory
usage and improve the performance of detecting small traffic signs, we proposed
an approach for detecting small traffic signs from large images under real
world conditions. In particular, large images are broken into small patches as
input to a Small-Object-Sensitive-CNN (SOS-CNN) modified from a Single Shot
Multibox Detector (SSD) framework with a VGG-16 network as the base network to
produce patch-level object detection results. Scale invariance is achieved by
applying the SOS-CNN on an image pyramid. Then, image-level object detection is
obtained by projecting all the patch-level detection results to the image at
the original scale. Experimental results on a real-world conditioned traffic
sign dataset have demonstrated the effectiveness of the proposed method in
terms of detection accuracy and recall, especially for those with small sizes.
Neural Question Answering at BioASQ 5B
Authors: Georg Wiese, Dirk Weissenborn, Mariana Neves
Date: 2017-06-26 00:00:00
Proceeding: WS 2017 8
Tasks: Question Answering, Word Embeddings

This paper describes our submission to the 2017 BioASQ challenge. We
participated in Task B, Phase B which is concerned with biomedical question
answering (QA). We focus on factoid and list question, using an extractive QA
model, that is, we restrict our system to output substrings of the provided
text snippets. At the core of our system, we use FastQA, a state-of-the-art
neural QA system. We extended it with biomedical word embeddings and changed
its answer layer to be able to answer list questions in addition to factoid
questions. We pre-trained the model on a large-scale open-domain QA dataset,
SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our
approach, we achieve state-of-the-art results on factoid questions and
competitive results on list questions.
Illuminating Pedestrians via Simultaneous Detection & Segmentation
Authors: Garrick Brazil, Xi Yin, Xiaoming Liu
Date: 2017-06-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: Autonomous Driving, Pedestrian Detection, Segmentation, Semantic Segmentation

Pedestrian detection is a critical problem in computer vision with
significant impact on safety in urban autonomous driving. In this work, we
explore how semantic segmentation can be used to boost pedestrian detection
accuracy while having little to no impact on network efficiency. We propose a
segmentation infusion network to enable joint supervision on semantic
segmentation and pedestrian detection. When placed properly, the additional
supervision helps guide features in shared layers to become more sophisticated
and helpful for the downstream pedestrian detector. Using this approach, we
find weakly annotated boxes to be sufficient for considerable performance
gains. We provide an in-depth analysis to demonstrate how shared layers are
shaped by the segmentation supervision. In doing so, we show that the resulting
feature maps become more semantically meaningful and robust to shape and
occlusion. Overall, our simultaneous detection and segmentation framework
achieves a considerable gain over the state-of-the-art on the Caltech
pedestrian dataset, competitive performance on KITTI, and executes 2x faster
than competitive methods.
On conditional parity as a notion of non-discrimination in machine learning
Authors: Ya'acov Ritov, Yuekai Sun, Ruofei Zhao
Date: 2017-06-26 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual

We identify conditional parity as a general notion of non-discrimination in
machine learning. In fact, several recently proposed notions of
non-discrimination, including a few counterfactual notions, are instances of
conditional parity. We show that conditional parity is amenable to statistical
analysis by studying randomization as a general mechanism for achieving
conditional parity and a kernel-based test of conditional parity.
Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach
Authors: Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro
Date: 2017-06-26 00:00:00

Current grammar-based NeuroEvolution approaches have several shortcomings. On
the one hand, they do not allow the generation of Artificial Neural Networks
(ANNs composed of more than one hidden-layer. On the other, there is no way to
evolve networks with more than one output neuron. To properly evolve ANNs with
more than one hidden-layer and multiple output nodes there is the need to know
the number of neurons available in previous layers. In this paper we introduce
Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation
that overcomes the aforementioned limitations. By enabling the creation of
dynamic rules that specify the connection possibilities of each neuron, the
methodology enables the evolution of multi-layered ANNs with more than one
output neuron. Results in different classification problems show that DSGE
evolves effective single and multi-layered ANNs, with a varying number of
output neurons.
Curvature Integration in a 5D Kernel for Extracting Vessel Connections in Retinal Images
Authors: Samaneh Abbasi-Sureshjani, Marta Favali, Giovanna Citti, Alessandro Sarti, Bart M. ter Haar Romeny
Date: 2016-08-29 00:00:00
Tasks: Clustering

Tree-like structures such as retinal images are widely studied in
computer-aided diagnosis systems for large-scale screening programs. Despite
several segmentation and tracking methods proposed in the literature, there
still exist several limitations specifically when two or more curvilinear
structures cross or bifurcate, or in the presence of interrupted lines or
highly curved blood vessels. In this paper, we propose a novel approach based
on multi-orientation scores augmented with a contextual affinity matrix, which
both are inspired by the geometry of the primary visual cortex (V1) and their
contextual connections. The connectivity is described with a five-dimensional
kernel obtained as the fundamental solution of the Fokker-Planck equation
modelling the cortical connectivity in the lifted space of positions,
orientations, curvatures and intensity. It is further used in a self-tuning
spectral clustering step to identify the main perceptual units in the stimuli.
The proposed method has been validated on several easy and challenging
structures in a set of artificial images and actual retinal patches. Supported
by quantitative and qualitative results, the method is capable of overcoming
the limitations of current state-of-the-art techniques.
Deep Network Flow for Multi-Object Tracking
Authors: Samuel Schulter, Paul Vernaza, Wongun Choi, Manmohan Chandraker
Date: 2017-06-26 00:00:00
Proceeding: CVPR 2017 7
Tasks: Graph Matching, Multi-Object Tracking, Object, Object Tracking

Data association problems are an important component of many computer vision
applications, with multi-object tracking being one of the most prominent
examples. A typical approach to data association involves finding a graph
matching or network flow that minimizes a sum of pairwise association costs,
which are often either hand-crafted or learned as linear functions of fixed
features. In this work, we demonstrate that it is possible to learn features
for network-flow-based data association via backpropagation, by expressing the
optimum of a smoothed network flow problem as a differentiable function of the
pairwise association costs. We apply this approach to multi-object tracking
with a network flow formulation. Our experiments demonstrate that we are able
to successfully learn all cost functions for the association problem in an
end-to-end fashion, which outperform hand-crafted costs in all settings. The
integration and combination of various sources of inputs becomes easy and the
cost functions can be learned entirely from data, alleviating tedious
hand-designing of costs.
The Causality/Repair Connection in Databases: Causality-Programs
Authors: Leopoldo Bertossi
Date: 2017-04-17 00:00:00

In this work, answer-set programs that specify repairs of databases are used
as a basis for solving computational and reasoning problems about causes for
query answers from databases.
Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability
Authors: Tiancheng Zhao, Allen Lu, Kyusong Lee, Maxine Eskenazi
Date: 2017-06-26 00:00:00
Proceeding: WS 2017 8
Tasks: Decision Making, Decoder, slot-filling, Slot Filling

Generative encoder-decoder models offer great promise in developing
domain-general dialog systems. However, they have mainly been applied to
open-domain conversations. This paper presents a practical and novel framework
for building task-oriented dialog systems based on encoder-decoder models. This
framework enables encoder-decoder models to accomplish slot-value independent
decision-making and interact with external databases. Moreover, this paper
shows the flexibility of the proposed method by interleaving chatting
capability with a slot-filling system for better out-of-domain recovery. The
models were trained on both real-user data from a bus information system and
human-human chat data. Results show that the proposed framework achieves good
performance in both offline evaluation metrics and in task success rate with
human users.
Learning to Map Vehicles into Bird's Eye View
Authors: Andrea Palazzi, Guido Borghi, Davide Abati, Simone Calderara, Rita Cucchiara
Date: 2017-06-26 00:00:00
Tasks: Autonomous Vehicles

Awareness of the road scene is an essential component for both autonomous
vehicles and Advances Driver Assistance Systems and is gaining importance both
for the academia and car companies. This paper presents a way to learn a
semantic-aware transformation which maps detections from a dashboard camera
view onto a broader bird's eye occupancy map of the scene. To this end, a huge
synthetic dataset featuring 1M couples of frames, taken from both car dashboard
and bird's eye view, has been collected and automatically annotated. A
deep-network is then trained to warp detections from the first to the second
view. We demonstrate the effectiveness of our model against several baselines
and observe that is able to generalize on real-world data despite having been
trained solely on synthetic ones.
Image Processing in Floriculture Using a robotic Mobile Platform
Authors: Juan Garcia-Torres, Diana Caro-Prieto
Date: 2017-06-26 00:00:00
Tasks: Diagnostic, Diversity, Marketing

Colombia has a privileged geographical location which makes it a cornerstone
and equidistant point to all regional markets. The country has a great
ecological diversity and it is one of the largest suppliers of flowers for US.
Colombian flower companies have made innovations in the marketing process,
using methods to reach all conditions for final consumers. This article
develops a monitoring system for floriculture industries. The system was
implemented in a robotic platform. This device has the ability to be programmed
in different programming languages. The robot takes the necessary environment
information from its camera. The algorithm of the monitoring system was
developed with the image processing toolbox on Matlab. The implemented
algorithm acquires images through its camera, it performs a preprocessing of
the image, noise filter, enhancing of the color and adjusting the dimension in
order to increase processing speed. Then, the image is segmented by color and
with the binarized version of the image using morphological operations (erosion
and dilation), extract relevant features such as centroid, perimeter and area.
The data obtained from the image processing helps the robot with the automatic
identification of objectives, orientation and move towards them. Also, the
results generate a diagnostic quality of each object scanned.
Approximate Steepest Coordinate Descent
Authors: Sebastian U. Stich, Anant Raj, Martin Jaggi
Date: 2017-06-26 00:00:00
Proceeding: ICML 2017 8
Tasks: Computational Efficiency, regression

We propose a new selection rule for the coordinate selection in coordinate
descent methods for huge-scale optimization. The efficiency of this novel
scheme is provably better than the efficiency of uniformly random selection,
and can reach the efficiency of steepest coordinate descent (SCD), enabling an
acceleration of a factor of up to $n$, the number of coordinates. In many
practical applications, our scheme can be implemented at no extra cost and
computational efficiency very close to the faster uniform selection. Numerical
experiments with Lasso and Ridge regression show promising improvements, in
line with our theoretical guarantees.
Poisson intensity estimation with reproducing kernels
Authors: Seth Flaxman, Yee Whye Teh, Dino Sejdinovic
Date: 2016-10-27 00:00:00

Despite the fundamental nature of the inhomogeneous Poisson process in the
theory and application of stochastic processes, and its attractive
generalizations (e.g. Cox process), few tractable nonparametric modeling
approaches of intensity functions exist, especially when observed points lie in
a high-dimensional space. In this paper we develop a new, computationally
tractable Reproducing Kernel Hilbert Space (RKHS) formulation for the
inhomogeneous Poisson process. We model the square root of the intensity as an
RKHS function. Whereas RKHS models used in supervised learning rely on the
so-called representer theorem, the form of the inhomogeneous Poisson process
likelihood means that the representer theorem does not apply. However, we prove
that the representer theorem does hold in an appropriately transformed RKHS,
guaranteeing that the optimization of the penalized likelihood can be cast as a
tractable finite-dimensional problem. The resulting approach is simple to
implement, and readily scales to high dimensions and large-scale datasets.