pwc-archive/papers-with-abstracts huggingface dataset:
An Ensemble Dialogue System for Facts-Based Sentence Generation
Authors: Ryota Tanaka, Akihide Ozeki, Shugo Kato, Akinobu Lee
Date: 2019-02-05 00:00:00
Tasks: Reranking, Retrieval, Sentence

This study aims to generate responses based on real-world facts by
conditioning context and external facts extracted from information websites.
Our system is an ensemble system that combines three modules: generated-based
module, retrieval-based module, and reranking module. Therefore, this system
can return diverse and meaningful responses from various perspectives. The
experiments and evaluations are conducted with the sentence generation task in
Dialog System Technology Challenges 7 (DSTC7-Task2). As a result, the proposed
system performed significantly better than sole modules, and worked fine at the
DSTC7-Task2, specifically on the objective evaluation.
Active Image Synthesis for Efficient Labeling
Authors: Jialei Chen, Yujia Xie, Kan Wang, Chuck Zhang, Mani A. Vannan, Ben Wang, Zhen Qian
Date: 2019-02-05 00:00:00
Tasks: Image Generation, Small Data Image Classification

The great success achieved by deep neural networks attracts increasing attention from the manufacturing and healthcare communities. However, the limited availability of data and high costs of data collection are the major challenges for the applications in those fields. We propose in this work AISEL, an active image synthesis method for efficient labeling to improve the performance of the small-data learning tasks. Specifically, a complementary AISEL dataset is generated, with labels actively acquired via a physics-based method to incorporate underlining physical knowledge at hand. An important component of our AISEL method is the bidirectional generative invertible network (GIN), which can extract interpretable features from the training images and generate physically meaningful virtual images. Our AISEL method then efficiently samples virtual images not only further exploits the uncertain regions, but also explores the entire image space. We then discuss the interpretability of GIN both theoretically and experimentally, demonstrating clear visual improvements over the benchmarks. Finally, we demonstrate the effectiveness of our AISEL framework on aortic stenosis application, in which our method lower the labeling cost by $90\%$ while achieving a $15\%$ improvement in prediction accuracy.
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Authors: Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Date: 2019-02-05 00:00:00
Tasks: Multi-Armed Bandits

We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively different regret bounds: one competes with a smoothed version of the policy class under no continuity assumptions, while the other requires standard Lipschitz assumptions. Both bounds exhibit data-dependent "zooming" behavior and, with no tuning, yield improved guarantees for benign problems. We also study adapting to unknown smoothness parameters, establishing a price-of-adaptivity and deriving optimal adaptive algorithms that require no additional information.
Perturbative GAN: GAN with Perturbation Layers
Authors: Yuma Kishi, Tsutomu Ikegami, Shin-ichi O'uchi, Ryousei Takano, Wakana Nogami, Tomohiro Kudoh
Date: 2019-02-05 00:00:00

Perturbative GAN, which replaces convolution layers of existing convolutional
GANs (DCGAN, WGAN-GP, BIGGAN, etc.) with perturbation layers that adds a fixed
noise mask, is proposed. Compared with the convolu-tional GANs, the number of
parameters to be trained is smaller, the convergence of training is faster, the
incep-tion score of generated images is higher, and the overall training cost
is reduced. Algorithmic generation of the noise masks is also proposed, with
which the training, as well as the generation, can be boosted with hardware
acceleration. Perturbative GAN is evaluated using con-ventional datasets
(CIFAR10, LSUN, ImageNet), both in the cases when a perturbation layer is
adopted only for Generators and when it is introduced to both Generator and
Discriminator.
Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation
Authors: Vladimir Karpukhin, Omer Levy, Jacob Eisenstein, Marjan Ghazvininejad
Date: 2019-02-05 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, Translation

We consider the problem of making machine translation more robust to
character-level variation at the source side, such as typos. Existing methods
achieve greater coverage by applying subword models such as byte-pair encoding
(BPE) and character-level encoders, but these methods are highly sensitive to
spelling mistakes. We show how training on a mild amount of random synthetic
noise can dramatically improve robustness to these variations, without
diminishing performance on clean text. We focus on translation performance on
natural noise, as captured by frequent corrections in Wikipedia edit logs, and
show that robustness to such noise can be achieved using a balanced diet of
simple synthetic noises at training time, without access to the natural noise
data or distribution.
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data
Authors: Jackson A. Killian, Bryan Wilder, Amit Sharma, Daksha Shah, Vinod Choudhary, Bistra Dilkina, Milind Tambe
Date: 2019-02-05 00:00:00

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. The data contains nearly 17,000 patients and 2.1M dose records. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. We then construct a deep learning model, demonstrate its interpretability, and show how it can be adapted and trained in different clinical scenarios to better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with 21% more patients and before 76% more missed doses than current heuristic baselines. For outcome prediction, our model performs 40% better than baseline methods, allowing cities to target more resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model can be trained in an end-to-end decision focused learning setting to achieve 15% better solution quality in an example decision problem faced by health workers.
Parameter-Free Online Convex Optimization with Sub-Exponential Noise
Authors: Kwang-Sung Jun, Francesco Orabona
Date: 2019-02-05 00:00:00

We consider the problem of unconstrained online convex optimization (OCO) with sub-exponential noise, a strictly more general problem than the standard OCO. In this setting, the learner receives a subgradient of the loss functions corrupted by sub-exponential noise and strives to achieve optimal regret guarantee, without knowledge of the competitor norm, i.e., in a parameter-free way. Recently, Cutkosky and Boahen (COLT 2017) proved that, given unbounded subgradients, it is impossible to guarantee a sublinear regret due to an exponential penalty. This paper shows that it is possible to go around the lower bound by allowing the observed subgradients to be unbounded via stochastic noise. However, the presence of unbounded noise in unconstrained OCO is challenging; existing algorithms do not provide near-optimal regret bounds or fail to have a guarantee. So, we design a novel parameter-free OCO algorithm for Banach space, which we call BANCO, via a reduction to betting on noisy coins. We show that BANCO achieves the optimal regret rate in our problem. Finally, we show the application of our results to obtain a parameter-free locally private stochastic subgradient descent algorithm, and the connection to the law of iterated logarithms.
A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras
Authors: Icaro O. de Oliveira, Keiko V. O. Fonseca, Rodrigo Minetto
Date: 2019-02-04 00:00:00
Tasks: Vehicle Re-Identification

We describe in this paper a Two-Stream Siamese Neural Network for vehicle re-identification. The proposed network is fed simultaneously with small coarse patches of the vehicle shape's, with 96 x 96 pixels, in one stream, and fine features extracted from license plate patches, easily readable by humans, with 96 x 48 pixels, in the other one. Then, we combined the strengths of both streams by merging the Siamese distance descriptors with a sequence of fully connected layers, as an attempt to tackle a major problem in the field, false alarms caused by a huge number of car design and models with nearly the same appearance or by similar license plate strings. In our experiments, with 2 hours of videos containing 2982 vehicles, extracted from two low-cost cameras in the same roadway, 546 ft away, we achieved a F-measure and accuracy of 92.6% and 98.7%, respectively. We show that the proposed network, available at https://github.com/icarofua/siamese-two-stream, outperforms other One-Stream architectures, even if they use higher resolution image features.
Optimally Scheduling CNN Convolutions for Efficient Memory Access
Authors: Arthur Stoutchinin, Francesco Conti, Luca Benini
Date: 2019-02-04 00:00:00
Tasks: Scheduling

Embedded inference engines for convolutional networks must be parsimonious in
memory bandwidth and buffer sizing to meet power and cost constraints. We
present an analytical memory bandwidth model for loop-nest optimization
targeting architectures with application managed buffers. We applied this model
to optimize the CNN convolution loop-nest. We show that our model is more
accurate than previously published models. Using this model we can identify
non-trivial dataflow schedules that result in lowest communication bandwidth
given tight local buffering constraints. We show that optimal dataflow
schedules are implementable in practice and that our model is accurate with
respect to a real implementation; moreover, we introduce an accelerator
architecture, named Hardware Convolution Block (HWC), which implements the
optimal schedules, and we show it achieves up to 14x memory bandwidth reduction
compared to a previously published accelerator with a similar memory interface,
but implementing a non-optimal schedule.
Confusion matrices and rough set data analysis
Authors: Ivo Düntsch, Günther Gediga
Date: 2019-02-04 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

A widespread approach in machine learning to evaluate the quality of a
classifier is to cross -- classify predicted and actual decision classes in a
confusion matrix, also called error matrix. A classification tool which does
not assume distributional parameters but only information contained in the data
is based on the rough set data model which assumes that knowledge is given only
up to a certain granularity. Using this assumption and the technique of
confusion matrices, we define various indices and classifiers based on rough
confusion matrices.
Bootstrapped Coordinate Search for Multidimensional Scaling
Authors: Efthymios Tzinis
Date: 2019-02-04 00:00:00

In this work, a unified framework for gradient-free Multidimensional Scaling
(MDS) based on Coordinate Search (CS) is proposed. This family of algorithms is
an instance of General Pattern Search (GPS) methods which avoid the explicit
computation of derivatives but instead evaluate the objective function while
searching on coordinate steps of the embedding space. The backbone element of
CSMDS framework is the corresponding probability matrix that correspond to how
likely is each corresponding coordinate to be evaluated. We propose a
Bootstrapped instance of CSMDS (BS CSMDS) which enhances the probability of the
direction that decreases the most the objective function while also reducing
the corresponding probability of all the other coordinates. BS CSMDS manages to
avoid unnecessary function evaluations and result to significant speedup over
other CSMDS alternatives while also obtaining the same error rate. Experiments
on both synthetic and real data reveal that BS CSMDS performs consistently
better than other CSMDS alternatives under various experimental setups.
What is the dimension of your binary data?
Authors: Nikolaj Tatti, Taneli Mielikainen, Aristides Gionis, Heikki Mannila
Date: 2019-02-04 00:00:00
Tasks: Clustering

Many 0/1 datasets have a very large number of variables; on the other hand,
they are sparse and the dependency structure of the variables is simpler than
the number of variables would suggest. Defining the effective dimensionality of
such a dataset is a nontrivial problem. We consider the problem of defining a
robust measure of dimension for 0/1 datasets, and show that the basic idea of
fractal dimension can be adapted for binary data. However, as such the fractal
dimension is difficult to interpret. Hence we introduce the concept of
normalized fractal dimension. For a dataset $D$, its normalized fractal
dimension is the number of columns in a dataset $D'$ with independent columns
and having the same (unnormalized) fractal dimension as $D$. The normalized
fractal dimension measures the degree of dependency structure of the data. We
study the properties of the normalized fractal dimension and discuss its
computation. We give empirical results on the normalized fractal dimension,
comparing it against baseline measures such as PCA. We also study the
relationship of the dimension of the whole dataset and the dimensions of
subgroups formed by clustering. The results indicate interesting differences
between and within datasets.
Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers
Authors: Yunong Shi, Nelson Leung, Pranav Gokhale, Zane Rossi, David I. Schuster, Henry Hoffman, Fred T. Chong
Date: 2019-02-04 00:00:00

Recent developments in engineering and algorithms have made real-world
applications in quantum computing possible in the near future. Existing quantum
programming languages and compilers use a quantum assembly language composed of
1- and 2-qubit (quantum bit) gates. Quantum compiler frameworks translate this
quantum assembly to electric signals (called control pulses) that implement the
specified computation on specific physical devices. However, there is a
mismatch between the operations defined by the 1- and 2-qubit logical ISA and
their underlying physical implementation, so the current practice of directly
translating logical instructions into control pulses results in inefficient,
high-latency programs. To address this inefficiency, we propose a universal
quantum compilation methodology that aggregates multiple logical operations
into larger units that manipulate up to 10 qubits at a time. Our methodology
then optimizes these aggregates by (1) finding commutative intermediate
operations that result in more efficient schedules and (2) creating custom
control pulses optimized for the aggregate (instead of individual 1- and
2-qubit operations). Compared to the standard gate-based compilation, the
proposed approach realizes a deeper vertical integration of high-level quantum
software and low-level, physical quantum hardware. We evaluate our approach on
important near-term quantum applications on simulations of superconducting
quantum architectures. Our proposed approach provides a mean speedup of
$5\times$, with a maximum of $10\times$. Because latency directly affects the
feasibility of quantum computation, our results not only improve performance
but also have the potential to enable quantum computation sooner than otherwise
possible.
TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis
Authors: Chenge Li, Gregory Dobler, Xin Feng, Yao Wang
Date: 2019-02-04 00:00:00
Tasks: Object, object-detection, Object Detection, Object Tracking, Open-Ended Question Answering, vehicle detection

Object detection and object tracking are usually treated as two separate
processes. Significant progress has been made for object detection in 2D images
using deep learning networks. The usual tracking-by-detection pipeline for
object tracking requires that the object is successfully detected in the first
frame and all subsequent frames, and tracking is done by associating detection
results. Performing object detection and object tracking through a single
network remains a challenging open question. We propose a novel network
structure named trackNet that can directly detect a 3D tube enclosing a moving
object in a video segment by extending the faster R-CNN framework. A Tube
Proposal Network (TPN) inside the trackNet is proposed to predict the
objectness of each candidate tube and location parameters specifying the
bounding tube. The proposed framework is applicable for detecting and tracking
any object and in this paper, we focus on its application for traffic video
analysis. The proposed model is trained and tested on UA-DETRAC, a large
traffic video dataset available for multi-vehicle detection and tracking, and
obtained very promising results.
PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction
Authors: Johan Mathe, Nina Miolane, Nicolas Sebastien, Jeremie Lequeux
Date: 2019-02-04 00:00:00

Photovoltaic (PV) power generation has emerged as one of the lead renewable energy sources. Yet, its production is characterized by high uncertainty, being dependent on weather conditions like solar irradiance and temperature. Predicting PV production, even in the 24-hour forecast, remains a challenge and leads energy providers to left idling - often carbon emitting - plants. In this paper, we introduce a Long-Term Recurrent Convolutional Network using Numerical Weather Predictions (NWP) to predict, in turn, PV production in the 24-hour and 48-hour forecast horizons. This network architecture fully leverages both temporal and spatial weather data, sampled over the whole geographical area of interest. We train our model on an NWP dataset from the National Oceanic and Atmospheric Administration (NOAA) to predict spatially aggregated PV production in Germany. We compare its performance to the persistence model and state-of-the-art methods.
Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders
Authors: Baruch Epstein, Ron Meir
Date: 2019-02-04 00:00:00
Tasks: Generalization Bounds

Autoencoders are widely used for unsupervised learning and as a
regularization scheme in semi-supervised learning. However, theoretical
understanding of their generalization properties and of the manner in which
they can assist supervised learning has been lacking. We utilize recent
advances in the theory of deep learning generalization, together with a novel
reconstruction loss, to provide generalization bounds for autoencoders. To the
best of our knowledge, this is the first such bound. We further show that,
under appropriate assumptions, an autoencoder with good generalization
properties can improve any semi-supervised learning scheme. We support our
theoretical results with empirical demonstrations.
Very Long Term Field of View Prediction for 360-degree Video Streaming
Authors: Chenge Li, Weixi Zhang, Yong liu, Yao Wang
Date: 2019-02-04 00:00:00

360-degree videos have gained increasing popularity in recent years with the
developments and advances in Virtual Reality (VR) and Augmented Reality (AR)
technologies. In such applications, a user only watches a video scene within a
field of view (FoV) centered in a certain direction. Predicting the future FoV
in a long time horizon (more than seconds ahead) can help save bandwidth
resources in on-demand video streaming while minimizing video freezing in
networks with significant bandwidth variations. In this work, we treat the FoV
prediction as a sequence learning problem, and propose to predict the target
user's future FoV not only based on the user's own past FoV center trajectory
but also other users' future FoV locations. We propose multiple prediction
models based on two different FoV representations: one using FoV center
trajectories and another using equirectangular heatmaps that represent the FoV
center distributions. Extensive evaluations with two public datasets
demonstrate that the proposed models can significantly outperform benchmark
models, and other users' FoVs are very helpful for improving long-term
predictions.
Blaze: Simplified High Performance Cluster Computing
Authors: Junhao Li, Hang Zhang
Date: 2019-02-04 00:00:00
Tasks: Vocal Bursts Intensity Prediction

MapReduce and its variants have significantly simplified and accelerated the
process of developing parallel programs. However, most MapReduce
implementations focus on data-intensive tasks while many real-world tasks are
compute intensive and their data can fit distributedly into the memory. For
these tasks, the speed of MapReduce programs can be much slower than those
hand-optimized ones. We present Blaze, a C++ library that makes it easy to
develop high performance parallel programs for such compute intensive tasks. At
the core of Blaze is a highly-optimized in-memory MapReduce function, which has
three main improvements over conventional MapReduce implementations: eager
reduction, fast serialization, and special treatment for a small fixed key
range. We also offer additional conveniences that make developing parallel
programs similar to developing serial programs. These improvements make Blaze
an easy-to-use cluster computing library that approaches the speed of
hand-optimized parallel code. We apply Blaze to some common data mining tasks,
including word frequency count, PageRank, k-means, expectation maximization
(Gaussian mixture model), and k-nearest neighbors. Blaze outperforms Apache
Spark by more than 10 times on average for these tasks, and the speed of Blaze
scales almost linearly with the number of nodes. In addition, Blaze uses only
the MapReduce function and 3 utility functions in its implementation while
Spark uses almost 30 different parallel primitives in its official
implementation.
Visualization tools for parameter selection in cluster analysis
Authors: Alexander Rolle, Luis Scoccola
Date: 2019-02-04 00:00:00
Tasks: Clustering

We propose an algorithm, HPREF (Hierarchical Partitioning by Repeated Features), that produces a hierarchical partition of a set of clusterings of a fixed dataset, such as sets of clusterings produced by running a clustering algorithm with a range of parameters. This gives geometric structure to such sets of clustering, and can be used to visualize the set of results one obtains by running a clustering algorithm with a range of parameters.
A Forest from the Trees: Generation through Neighborhoods
Authors: Yang Li, Tianxiang Gao, Junier B. Oliva
Date: 2019-02-04 00:00:00

In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors). Although human learning makes seamless use of both learned perceptual features and instance recall, current generative learning paradigms only make use of one of these two components. Take, for instance, flow models, which learn a latent space of invertible features that follow a simple distribution. Conversely, kernel density techniques use instances to shift a simple distribution into an aggregate mixture model. Here we propose multiple methods to enhance the latent space of a flow model with neighborhood information. Not only does our proposed framework represent a more human-like approach by leveraging both learned features and memories, but it may also be viewed as a step forward in non-parametric methods. The efficacy of our model is shown empirically with standard image datasets. We observe compelling results and a significant improvement over baselines.