pwc-archive/papers-with-abstracts huggingface dataset:
Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field
Authors: Muneki Yasuda, Junpei Watanabe, Shun Kataoka, Kazuyuki Tanaka
Date: 2017-10-20 00:00:00
Tasks: Denoising, Image Denoising

In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image denoising problems, including hyperparameter estimation, in $O(n)$-time, where $n$ is the number of pixels in a given image. From the perspective of the order of the computational time, this is a state-of-the-art algorithm for the present problem setting. Moreover, the results of our numerical experiments we show our method is in fact effective in practice.
Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
Authors: Yi Luan, Chris Brockett, Bill Dolan, Jianfeng Gao, Michel Galley
Date: 2017-10-20 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Multi-Task Learning

Building a persona-based conversation agent is challenging owing to the lack
of large amounts of speaker-specific conversation data for model training. This
paper addresses the problem by proposing a multi-task learning approach to
training neural conversation models that leverages both conversation data
across speakers and other types of data pertaining to the speaker and speaker
roles to be modeled. Experiments show that our approach leads to significant
improvements over baseline model quality, generating responses that capture
more precisely speakers' traits and speaking styles. The model offers the
benefits of being algorithmically simple and easy to implement, and not relying
on large quantities of data representing specific individual speakers.
On the Statistical Efficiency of Compositional Nonparametric Prediction
Authors: Yixi Xu, Jean Honorio, Xiao Wang
Date: 2017-04-06 00:00:00
Tasks: 2k, Prediction, regression

In this paper, we propose a compositional nonparametric method in which a
model is expressed as a labeled binary tree of $2k+1$ nodes, where each node is
either a summation, a multiplication, or the application of one of the $q$
basis functions to one of the $p$ covariates. We show that in order to recover
a labeled binary tree from a given dataset, the sufficient number of samples is
$O(k\log(pq)+\log(k!))$, and the necessary number of samples is $\Omega(k\log
(pq)-\log(k!))$. We further propose a greedy algorithm for regression in order
to validate our theoretical findings through synthetic experiments.
Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models
Authors: Johan Dahlin, Thomas B. Schön
Date: 2015-11-05 00:00:00
Tasks: State Space Models

This tutorial provides a gentle introduction to the particle
Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear
state-space models together with a software implementation in the statistical
programming language R. We employ a step-by-step approach to develop an
implementation of the PMH algorithm (and the particle filter within) together
with the reader. This final implementation is also available as the package
pmhtutorial in the CRAN repository. Throughout the tutorial, we provide some
intuition as to how the algorithm operates and discuss some solutions to
problems that might occur in practice. To illustrate the use of PMH, we
consider parameter inference in a linear Gaussian state-space model with
synthetic data and a nonlinear stochastic volatility model with real-world
data.
Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery for Pathologically-Proven Lung Cancer Detection
Authors: Mohammad Javad Shafiee, Audrey G. Chung, Farzad Khalvati, Masoom A. Haider, Alexander Wong
Date: 2017-05-10 00:00:00
Tasks: Descriptive, Diagnostic, Specificity

While lung cancer is the second most diagnosed form of cancer in men and
women, a sufficiently early diagnosis can be pivotal in patient survival rates.
Imaging-based, or radiomics-driven, detection methods have been developed to
aid diagnosticians, but largely rely on hand-crafted features which may not
fully encapsulate the differences between cancerous and healthy tissue.
Recently, the concept of discovery radiomics was introduced, where custom
abstract features are discovered from readily available imaging data. We
propose a novel evolutionary deep radiomic sequencer discovery approach based
on evolutionary deep intelligence. Motivated by patient privacy concerns and
the idea of operational artificial intelligence, the evolutionary deep radiomic
sequencer discovery approach organically evolves increasingly more efficient
deep radiomic sequencers that produce significantly more compact yet similarly
descriptive radiomic sequences over multiple generations. As a result, this
framework improves operational efficiency and enables diagnosis to be run
locally at the radiologist's computer while maintaining detection accuracy. We
evaluated the evolved deep radiomic sequencer (EDRS) discovered via the
proposed evolutionary deep radiomic sequencer discovery framework against
state-of-the-art radiomics-driven and discovery radiomics methods using
clinical lung CT data with pathologically-proven diagnostic data from the
LIDC-IDRI dataset. The evolved deep radiomic sequencer shows improved
sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%)
relative to previous radiomics approaches.
Symmetric Variational Autoencoder and Connections to Adversarial Learning
Authors: Liqun Chen, Shuyang Dai, Yunchen Pu, Chunyuan Li, Qinliang Su, Lawrence Carin
Date: 2017-09-06 00:00:00

A new form of the variational autoencoder (VAE) is proposed, based on the
symmetric Kullback-Leibler divergence. It is demonstrated that learning of the
resulting symmetric VAE (sVAE) has close connections to previously developed
adversarial-learning methods. This relationship helps unify the previously
distinct techniques of VAE and adversarially learning, and provides insights
that allow us to ameliorate shortcomings with some previously developed
adversarial methods. In addition to an analysis that motivates and explains the
sVAE, an extensive set of experiments validate the utility of the approach.
Exploiting oddsmaker bias to improve the prediction of NFL outcomes
Authors: Erik J. Schlicht
Date: 2017-10-18 00:00:00
Tasks: Decision Making

Accurately predicting the outcome of sporting events has been a goal for many
groups who seek to maximize profit. What makes this challenging is that the
outcome of an event can be influenced by many factors that dynamically change
across time. Oddsmakers attempt to estimate these factors by using both
algorithmic and subjective methods to set the spread. However, it is well-known
that both human and algorithmic decision-making can be biased, so this paper
explores if oddsmaker biases can be used in an exploitative manner, in order to
improve the prediction of NFL game outcomes. Real-world gambling data was used
to train and test different predictive models under varying assumptions. The
results show that methods that leverage oddsmaker biases in an exploitative
manner perform best under the conditions tested in this paper. These findings
suggest that leveraging human and algorithmic decision biases in an
exploitative manner may be useful for predicting the outcomes of competitive
events, and could lead to increased profit for those who have financial
interest in the outcomes.
Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection
Authors: Youxuan Jiang, Jonathan K. Kummerfeld, Walter S. Lasecki
Date: 2017-04-19 00:00:00
Proceeding: ACL 2017 7
Tasks: Diversity, Paraphrase Generation

Linguistically diverse datasets are critical for training and evaluating
robust machine learning systems, but data collection is a costly process that
often requires experts. Crowdsourcing the process of paraphrase generation is
an effective means of expanding natural language datasets, but there has been
limited analysis of the trade-offs that arise when designing tasks. In this
paper, we present the first systematic study of the key factors in
crowdsourcing paraphrase collection. We consider variations in instructions,
incentives, data domains, and workflows. We manually analyzed paraphrases for
correctness, grammaticality, and linguistic diversity. Our observations provide
new insight into the trade-offs between accuracy and diversity in crowd
responses that arise as a result of task design, providing guidance for future
paraphrase generation procedures.
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
Authors: Bichen Wu, Alvin Wan, Xiangyu Yue, Kurt Keutzer
Date: 2017-10-19 00:00:00
Tasks: 3D Semantic Segmentation, Autonomous Driving, Clustering, Robust 3D Semantic Segmentation, Semantic Segmentation

In this paper, we address semantic segmentation of road-objects from 3D LiDAR
point clouds. In particular, we wish to detect and categorize instances of
interest, such as cars, pedestrians and cyclists. We formulate this problem as
a point- wise classification problem, and propose an end-to-end pipeline called
SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a
transformed LiDAR point cloud as input and directly outputs a point-wise label
map, which is then refined by a conditional random field (CRF) implemented as a
recurrent layer. Instance-level labels are then obtained by conventional
clustering algorithms. Our CNN model is trained on LiDAR point clouds from the
KITTI dataset, and our point-wise segmentation labels are derived from 3D
bounding boxes from KITTI. To obtain extra training data, we built a LiDAR
simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize
large amounts of realistic training data. Our experiments show that SqueezeSeg
achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per
frame), highly desirable for autonomous driving applications. Furthermore,
additionally training on synthesized data boosts validation accuracy on
real-world data. Our source code and synthesized data will be open-sourced.
Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks
Authors: Michele Alberti, Mathias Seuret, Vinaychandran Pondenkandath, Rolf Ingold, Marcus Liwicki
Date: 2017-10-19 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Transfer Learning

In this paper, we present a novel approach to perform deep neural networks
layer-wise weight initialization using Linear Discriminant Analysis (LDA).
Typically, the weights of a deep neural network are initialized with: random
values, greedy layer-wise pre-training (usually as Deep Belief Network or as
auto-encoder) or by re-using the layers from another network (transfer
learning). Hence, many training epochs are needed before meaningful weights are
learned, or a rather similar dataset is required for seeding a fine-tuning of
transfer learning. In this paper, we describe how to turn an LDA into either a
neural layer or a classification layer. We analyze the initialization technique
on historical documents. First, we show that an LDA-based initialization is
quick and leads to a very stable initialization. Furthermore, for the task of
layout analysis at pixel level, we investigate the effectiveness of LDA-based
initialization and show that it outperforms state-of-the-art random weight
initialization methods.
Building effective deep neural network architectures one feature at a time
Authors: Martin Mundt, Tobias Weis, Kishore Konda, Visvanathan Ramesh
Date: 2017-05-18 00:00:00
Tasks: Feature Importance

Successful training of convolutional neural networks is often associated with
sufficiently deep architectures composed of high amounts of features. These
networks typically rely on a variety of regularization and pruning techniques
to converge to less redundant states. We introduce a novel bottom-up approach
to expand representations in fixed-depth architectures. These architectures
start from just a single feature per layer and greedily increase width of
individual layers to attain effective representational capacities needed for a
specific task. While network growth can rely on a family of metrics, we propose
a computationally efficient version based on feature time evolution and
demonstrate its potency in determining feature importance and a networks'
effective capacity. We demonstrate how automatically expanded architectures
converge to similar topologies that benefit from lesser amount of parameters or
improved accuracy and exhibit systematic correspondence in representational
complexity with the specified task. In contrast to conventional design patterns
with a typical monotonic increase in the amount of features with increased
depth, we observe that CNNs perform better when there is more learnable
parameters in intermediate, with falloffs to earlier and later layers.
Orientation-boosted Voxel Nets for 3D Object Recognition
Authors: Nima Sedaghat, Mohammadreza Zolfaghari, Ehsan Amiri, Thomas Brox
Date: 2016-04-12 00:00:00
Tasks: 3D Object Recognition, Classification, General Classification, Object, Object Recognition

Recent work has shown good recognition results in 3D object recognition using
3D convolutional networks. In this paper, we show that the object orientation
plays an important role in 3D recognition. More specifically, we argue that
objects induce different features in the network under rotation. Thus, we
approach the category-level classification task as a multi-task problem, in
which the network is trained to predict the pose of the object in addition to
the class label as a parallel task. We show that this yields significant
improvements in the classification results. We test our suggested architecture
on several datasets representing various 3D data sources: LiDAR data, CAD
models, and RGB-D images. We report state-of-the-art results on classification
as well as significant improvements in precision and speed over the baseline on
3D detection.
Go game formal revealing by Ising model
Authors: Matías Alvarado, Arturo Yee, Carlos Villarreal
Date: 2017-10-19 00:00:00
Tasks: model

Go gaming is a struggle for territory control between rival, black and white,
stones on a board. We model the Go dynamics in a game by means of the Ising
model whose interaction coefficients reflect essential rules and tactics
employed in Go to build long-term strategies. At any step of the game, the
energy functional of the model provides the control degree (strength) of a
player over the board. A close fit between predictions of the model with actual
games is obtained.
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
Authors: Priyadarshini Panda, Narayan Srinivasa
Date: 2017-10-19 00:00:00
Tasks: Action Recognition, Temporal Action Localization

A fundamental challenge in machine learning today is to build a model that
can learn from few examples. Here, we describe a reservoir based spiking neural
model for learning to recognize actions with a limited number of labeled
videos. First, we propose a novel encoding, inspired by how microsaccades
influence visual perception, to extract spike information from raw video data
while preserving the temporal correlation across different frames. Using this
encoding, we show that the reservoir generalizes its rich dynamical activity
toward signature action/movements enabling it to learn from few training
examples. We evaluate our approach on the UCF-101 dataset. Our experiments
demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5
accuracy, respectively, on the 101-class data while requiring just 8 video
examples per class for training. Our results establish a new benchmark for
action recognition from limited video examples for spiking neural models while
yielding competetive accuracy with respect to state-of-the-art non-spiking
neural models.
Be Your Own Prada: Fashion Synthesis with Structural Coherence
Authors: Shizhan Zhu, Sanja Fidler, Raquel Urtasun, Dahua Lin, Chen Change Loy
Date: 2017-10-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Fashion Synthesis, Semantic Segmentation, Sentence

We present a novel and effective approach for generating new clothing on a
wearer through generative adversarial learning. Given an input image of a
person and a sentence describing a different outfit, our model "redresses" the
person as desired, while at the same time keeping the wearer and her/his pose
unchanged. Generating new outfits with precise regions conforming to a language
description while retaining wearer's body structure is a new challenging task.
Existing generative adversarial networks are not ideal in ensuring global
coherence of structure given both the input photograph and language description
as conditions. We address this challenge by decomposing the complex generative
process into two conditional stages. In the first stage, we generate a
plausible semantic segmentation map that obeys the wearer's pose as a latent
spatial arrangement. An effective spatial constraint is formulated to guide the
generation of this semantic segmentation map. In the second stage, a generative
model with a newly proposed compositional mapping layer is used to render the
final image with precise regions and textures conditioned on this map. We
extended the DeepFashion dataset [8] by collecting sentence descriptions for
79K images. We demonstrate the effectiveness of our approach through both
quantitative and qualitative evaluations. A user study is also conducted. The
codes and the data are available at http://mmlab.ie.cuhk.
edu.hk/projects/FashionGAN/.
Bayesian Decision Making in Groups is Hard
Authors: Jan Hązła, Ali Jadbabaie, Elchanan Mossel, M. Amin Rahimian
Date: 2017-05-12 00:00:00
Tasks: Decision Making

We study the computations that Bayesian agents undertake when exchanging opinions over a network. The agents act repeatedly on their private information and take myopic actions that maximize their expected utility according to a fully rational posterior belief. We show that such computations are NP-hard for two natural utility functions: one with binary actions, and another where agents reveal their posterior beliefs. In fact, we show that distinguishing between posteriors that are concentrated on different states of the world is NP-hard. Therefore, even approximating the Bayesian posterior beliefs is hard. We also describe a natural search algorithm to compute agents' actions, which we call elimination of impossible signals, and show that if the network is transitive, the algorithm can be modified to run in polynomial time.
Saliency-guided Adaptive Seeding for Supervoxel Segmentation
Authors: Ge Gao, Mikko Lauri, Jianwei Zhang, Simone Frintrop
Date: 2017-04-13 00:00:00
Tasks: Segmentation

We propose a new saliency-guided method for generating supervoxels in 3D
space. Rather than using an evenly distributed spatial seeding procedure, our
method uses visual saliency to guide the process of supervoxel generation. This
results in densely distributed, small, and precise supervoxels in salient
regions which often contain objects, and larger supervoxels in less salient
regions that often correspond to background. Our approach largely improves the
quality of the resulting supervoxel segmentation in terms of boundary recall
and under-segmentation error on publicly available benchmarks.
Online Monotone Games
Authors: Ian Gemp, Sridhar Mahadevan
Date: 2017-10-19 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Algorithmic game theory (AGT) focuses on the design and analysis of
algorithms for interacting agents, with interactions rigorously formalized
within the framework of games. Results from AGT find applications in domains
such as online bidding auctions for web advertisements and network routing
protocols. Monotone games are games where agent strategies naturally converge
to an equilibrium state. Previous results in AGT have been obtained for convex,
socially-convex, or smooth games, but not monotone games. Our primary
theoretical contributions are defining the monotone game setting and its
extension to the online setting, a new notion of regret for this setting, and
accompanying algorithms that achieve sub-linear regret. We demonstrate the
utility of online monotone game theory on a variety of problem domains
including variational inequalities, reinforcement learning, and generative
adversarial networks.
Visual Integration of Data and Model Space in Ensemble Learning
Authors: Bruno Schneider, Dominik Jäckle, Florian Stoffel, Alexandra Diehl, Johannes Fuchs, Daniel Keim
Date: 2017-10-19 00:00:00
Tasks: Classification, Ensemble Learning, General Classification

Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classification outputs and where the errors come from. We propose a tight
visual integration of the data and the model space for exploring and combining
classifier models. We introduce a workflow that builds upon the visual
integration and enables the effective exploration of classification outputs and
models. We then present a use case in which we start with an ensemble
automatically selected by a standard ensemble selection algorithm, and show how
we can manipulate models and alternative combinations.
Sharpness-aware Low dose CT denoising using conditional generative adversarial network
Authors: Xin Yi, Paul Babyn
Date: 2017-08-22 00:00:00
Tasks: Denoising, Diagnostic, Generative Adversarial Network

Low Dose Computed Tomography (LDCT) has offered tremendous benefits in
radiation restricted applications, but the quantum noise as resulted by the
insufficient number of photons could potentially harm the diagnostic
performance. Current image-based denoising methods tend to produce a blur
effect on the final reconstructed results especially in high noise levels. In
this paper, a deep learning based approach was proposed to mitigate this
problem. An adversarially trained network and a sharpness detection network
were trained to guide the training process. Experiments on both simulated and
real dataset shows that the results of the proposed method have very small
resolution loss and achieves better performance relative to the-state-of-art
methods both quantitatively and visually.
Power Plant Performance Modeling with Concept Drift
Authors: Rui Xu, Yunwen Xu, Weizhong Yan
Date: 2017-10-19 00:00:00
Tasks: BIG-bench Machine Learning, regression

Power plant is a complex and nonstationary system for which the traditional
machine learning modeling approaches fall short of expectations. The
ensemble-based online learning methods provide an effective way to continuously
learn from the dynamic environment and autonomously update models to respond to
environmental changes. This paper proposes such an online ensemble regression
approach to model power plant performance, which is critically important for
operation optimization. The experimental results on both simulated and real
data show that the proposed method can achieve performance with less than 1%
mean average percentage error, which meets the general expectations in field
operations.