pwc-archive/papers-with-abstracts huggingface dataset:
Context-Based Dynamic Pricing with Online Clustering
Authors: Sentao Miao, Xi Chen, Xiuli Chao, Jiaxi Liu, Yidong Zhang
Date: 2019-02-17 00:00:00
Tasks: Clustering, Online Clustering

We consider a context-based dynamic pricing problem of online products, which have low sales. Sales data from Alibaba, a major global online retailer, illustrate the prevalence of low-sale products. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples. To address this challenge, we propose pricing policies that concurrently perform clustering over product demand and set individual pricing decisions on the fly. By clustering data and identifying products that have similar demand patterns, we utilize sales data from products within the same cluster to improve demand estimation for better pricing decisions. We evaluate the algorithms using regret, and the result shows that when product demand functions come from multiple clusters, our algorithms significantly outperform traditional single-product pricing policies. Numerical experiments using a real dataset from Alibaba demonstrate that the proposed policies, compared with several benchmark policies, increase the revenue. The results show that online clustering is an effective approach to tackling dynamic pricing problems associated with low-sale products.
Online PCB Defect Detector On A New PCB Defect Dataset
Authors: Sanli Tang, Fan He, Xiaolin Huang, Jie Yang
Date: 2019-02-17 00:00:00
Tasks: Defect Detection

Previous works for PCB defect detection based on image difference and image
processing techniques have already achieved promising performance. However,
they sometimes fall short because of the unaccounted defect patterns or
over-sensitivity about some hyper-parameters. In this work, we design a deep
model that accurately detects PCB defects from an input pair of a detect-free
template and a defective tested image. A novel group pyramid pooling module is
proposed to efficiently extract features of a large range of resolutions, which
are merged by group to predict PCB defect of corresponding scales. To train the
deep model, a dataset is established, namely DeepPCB, which contains 1,500
image pairs with annotations including positions of 6 common types of PCB
defects. Experiment results validate the effectiveness and efficiency of the
proposed model by achieving $98.6\%$ mAP @ 62 FPS on DeepPCB dataset. This
dataset is now available at: https://github.com/tangsanli5201/DeepPCB.
An Automated Testing Framework for Conversational Agents
Authors: Soodeh Atefi, Mohammad Amin Alipour
Date: 2019-02-17 00:00:00

Conversational agents are systems with a conversational interface that afford
interaction in spoken language. These systems are becoming prevalent and are
preferred in various contexts and for many users. Despite their increasing
success, the automated testing infrastructure to support the effective and
efficient development of such systems compared to traditional software systems
is still limited. Automated testing framework for conversational systems can
improve the quality of these systems by assisting developers to write, execute,
and maintain test cases. In this paper, we introduce our work-in-progress
automated testing framework, and its realization in the Python programming
language. We discuss some research problems in the development of such an
automated testing framework for conversational agents. In particular, we point
out the problems of the specification of the expected behavior, known as test
oracles, and semantic comparison of utterances.
Structured Group Local Sparse Tracker
Authors: Mohammadreza Javanmardi, Xiaojun Qi
Date: 2019-02-17 00:00:00
Tasks: Visual Tracking

Sparse representation is considered as a viable solution to visual tracking.
In this paper, we propose a structured group local sparse tracker (SGLST),
which exploits local patches inside target candidates in the particle filter
framework. Unlike the conventional local sparse trackers, the proposed
optimization model in SGLST not only adopts local and spatial information of
the target candidates but also attains the spatial layout structure among them
by employing a group-sparsity regularization term. To solve the optimization
model, we propose an efficient numerical algorithm consisting of two
subproblems with the closed-form solutions. Both qualitative and quantitative
evaluations on the benchmarks of challenging image sequences demonstrate the
superior performance of the proposed tracker against several state-of-the-art
trackers.
Iterated Belief Base Revision: A Dynamic Epistemic Logic Approach
Authors: Marlo Souza, Álvaro Moreira, Renata Vieira
Date: 2019-02-17 00:00:00

AGM's belief revision is one of the main paradigms in the study of belief
change operations. In this context, belief bases (prioritised bases) have been
largely used to specify the agent's belief state - whether representing the
agent's `explicit beliefs' or as a computational model for her belief state.
While the connection of iterated AGM-like operations and their encoding in
dynamic epistemic logics have been studied before, few works considered how
well-known postulates from iterated belief revision theory can be characterised
by means of belief bases and their counterpart in a dynamic epistemic logic.
This work investigates how priority graphs, a syntactic representation of
preference relations deeply connected to prioritised bases, can be used to
characterise belief change operators, focusing on well-known postulates of
Iterated Belief Change. We provide syntactic representations of belief change
operators in a dynamic context, as well as new negative results regarding the
possibility of representing an iterated belief revision operation using
transformations on priority graphs.
WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding
Authors: Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni, Stephen Roberts
Date: 2019-02-16 00:00:00

Variational Auto-encoders (VAEs) have been very successful as methods for
forming compressed latent representations of complex, often high-dimensional,
data. In this paper, we derive an alternative variational lower bound from the
one common in VAEs, which aims to minimize aggregate information loss. Using
our lower bound as the objective function for an auto-encoder enables us to
place a prior on the bulk statistics, corresponding to an aggregate posterior
for the entire dataset, as opposed to a single sample posterior as in the
original VAE. This alternative form of prior constraint allows individual
posteriors more flexibility to preserve necessary information for good
reconstruction quality. We further derive an analytic approximation to our
lower bound, leading to an efficient learning algorithm - WiSE-ALE. Through
various examples, we demonstrate that WiSE-ALE can reach excellent
reconstruction quality in comparison to other state-of-the-art VAE models,
while still retaining the ability to learn a smooth, compact representation.
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization
Authors: Feihu Huang, Bin Gu, Zhouyuan Huo, Songcan Chen, Heng Huang
Date: 2019-02-16 00:00:00
Tasks: BIG-bench Machine Learning

Proximal gradient method has been playing an important role to solve many
machine learning tasks, especially for the nonsmooth problems. However, in some
machine learning problems such as the bandit model and the black-box learning
problem, proximal gradient method could fail because the explicit gradients of
these problems are difficult or infeasible to obtain. The gradient-free
(zeroth-order) method can address these problems because only the objective
function values are required in the optimization. Recently, the first
zeroth-order proximal stochastic algorithm was proposed to solve the nonconvex
nonsmooth problems. However, its convergence rate is $O(\frac{1}{\sqrt{T}})$
for the nonconvex problems, which is significantly slower than the best
convergence rate $O(\frac{1}{T})$ of the zeroth-order stochastic algorithm,
where $T$ is the iteration number. To fill this gap, in the paper, we propose a
class of faster zeroth-order proximal stochastic methods with the variance
reduction techniques of SVRG and SAGA, which are denoted as ZO-ProxSVRG and
ZO-ProxSAGA, respectively. In theoretical analysis, we address the main
challenge that an unbiased estimate of the true gradient does not hold in the
zeroth-order case, which was required in previous theoretical analysis of both
SVRG and SAGA. Moreover, we prove that both ZO-ProxSVRG and ZO-ProxSAGA
algorithms have $O(\frac{1}{T})$ convergence rates. Finally, the experimental
results verify that our algorithms have a faster convergence rate than the
existing zeroth-order proximal stochastic algorithm.
A Little Is Enough: Circumventing Defenses For Distributed Learning
Authors: Moran Baruch, Gilad Baruch, Yoav Goldberg
Date: 2019-02-16 00:00:00
Proceeding: NeurIPS 2019 12

Distributed learning is central for large-scale training of deep-learning
models. However, they are exposed to a security threat in which Byzantine
participants can interrupt or control the learning process. Previous attack
models and their corresponding defenses assume that the rogue participants are
(a) omniscient (know the data of all other participants), and (b) introduce
large change to the parameters. We show that small but well-crafted changes are
sufficient, leading to a novel non-omniscient attack on distributed learning
that go undetected by all existing defenses. We demonstrate our attack method
works not only for preventing convergence but also for repurposing of the model
behavior (backdooring). We show that 20% of corrupt workers are sufficient to
degrade a CIFAR10 model accuracy by 50%, as well as to introduce backdoors into
MNIST and CIFAR10 models without hurting their accuracy
Deep Generalized Convolutional Sum-Product Networks
Authors: Jos van de Wolfshaar, Andrzej Pronobis
Date: 2019-02-16 00:00:00
Tasks: GPU, Image Classification, Image Inpainting

Sum-Product Networks (SPNs) are hierarchical, graphical models that combine benefits of deep learning and probabilistic modeling. SPNs offer unique advantages to applications demanding exact probabilistic inference over high-dimensional, noisy inputs. Yet, compared to convolutional neural nets, they struggle with capturing complex spatial relationships in image data. To alleviate this issue, we introduce Deep Generalized Convolutional Sum-Product Networks (DGC-SPNs), which encode spatial features in a way similar to CNNs, while preserving the validity of the probabilistic SPN model. As opposed to existing SPN-based image representations, DGC-SPNs allow for overlapping convolution patches through a novel parameterization of dilations and strides, resulting in significantly improved feature coverage and feature resolution. DGC-SPNs substantially outperform other SPN architectures across several visual datasets and for both generative and discriminative tasks, including image inpainting and classification. These contributions are reinforced by the first simple, scalable, and GPU-optimized implementation of SPNs, integrated with the widely used Keras/TensorFlow framework. The resulting model is fully probabilistic and versatile, yet efficient and straightforward to apply in practical applications in place of traditional deep nets.
LISA: a MATLAB package for Longitudinal Image Sequence Analysis
Authors: Jang Ik Cho, Xiao-Feng Wang, Yifan Xu, Jiayang Sun
Date: 2019-02-16 00:00:00

Large sequences of images (or movies) can now be obtained on an unprecedented
scale, which poses fundamental challenges to the existing image analysis
techniques. The challenges include heterogeneity, (automatic) alignment,
multiple comparisons, potential artifacts, and hidden noises. This paper
introduces our MATLAB package, Longitudinal Image Sequence Analysis (LISA), as
a one-stop ensemble of image processing and analysis tool for comparing a
general class of images from either different times, sessions, or subjects.
Given two contrasting sequences of images, the image processing in LISA starts
with selecting a region of interest in two representative images, followed by
automatic or manual segmentation and registration. Automatic segmentation
de-noises an image using a mixture of Gaussian distributions of the pixel
intensity values, while manual segmentation applies a user-chosen intensity
cut-off value to filter out noises. Automatic registration aligns the
contrasting images based on a mid-line regression whereas manual registration
lines up the images along a reference line formed by two user-selected points.
The processed images are then rendered for simultaneous statistical comparisons
to generate D, S, T, and P-maps. The D map represents a curated difference of
contrasting images, the S map is the non-parametrically smoothed differences,
the T map presents the variance-adjusted, smoothed differences, and the P-map
provides multiplicity-controlled p-values. These maps reveal the regions with
significant differences due to either longitudinal, subject-specific, or
treatment changes. A user can skip the image processing step to dive directly
into the statistical analysis step if the images have already been processed.
Hence, LISA offers flexibility in applying other image pre-processing tools.
LISA also has a parallel computing option for high definition images.
Atlas-based automated detection of swim bladder in Medaka embryo
Authors: Diane Genest, Marc Léonard, Jean Cousty, Noémie De Crozé, Hugues Talbot
Date: 2019-02-16 00:00:00
Tasks: Bladder Segmentation, Segmentation

Fish embryo models are increasingly being used both for the assessment of
chemicals efficacy and potential toxicity. This article proposes a methodology
to automatically detect the swim bladder on 2D images of Medaka fish embryos
seen either in dorsal view or in lateral view. After embryo segmentation and
for each studied orientation, the method builds an atlas of a healthy embryo.
This atlas is then used to define the region of interest and to guide the swim
bladder segmentation with a discrete globally optimal active contour.
Descriptors are subsequently designed from this segmentation. An automated
random forest clas-sifier is built from these descriptors in order to classify
embryos with and without a swim bladder. The proposed method is assessed on a
dataset of 261 images, containing 202 embryos with a swim bladder (where 196
are in dorsal view and 6 are in lateral view) and 59 without (where 43 are in
dorsal view and 16 are in lateral view). We obtain an average precision rate of
95% in the total dataset following 5-fold cross-validation.
Making Convex Loss Functions Robust to Outliers using $e$-Exponentiated Transformation
Authors: Suvadeep Hajra
Date: 2019-02-16 00:00:00

In this paper, we propose a novel {\em $e$-exponentiated} transformation, $0
\le e<1$, for loss functions. When the transformation is applied to a convex
loss function, the transformed loss function become more robust to outliers.
Using a novel generalization error bound, we have theoretically shown that the
transformed loss function has a tighter bound for datasets corrupted by
outliers. Our empirical observation shows that the accuracy obtained using the
transformed loss function can be significantly better than the same obtained
using the original loss function and comparable to that obtained by some other
state of the art methods in the presence of label noise.
Screening Rules for Lasso with Non-Convex Sparse Regularizers
Authors: Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon
Date: 2019-02-16 00:00:00

Leveraging on the convexity of the Lasso problem , screening rules help in
accelerating solvers by discarding irrelevant variables, during the
optimization process. However, because they provide better theoretical
guarantees in identifying relevant variables, several non-convex regularizers
for the Lasso have been proposed in the literature. This work is the first that
introduces a screening rule strategy into a non-convex Lasso solver. The
approach we propose is based on a iterative majorization-minimization (MM)
strategy that includes a screening rule in the inner solver and a condition for
propagating screened variables between iterations of MM. In addition to improve
efficiency of solvers, we also provide guarantees that the inner solver is able
to identify the zeros components of its critical point in finite time. Our
experimental analysis illustrates the significant computational gain brought by
the new screening rule compared to classical coordinate-descent or proximal
gradient descent methods.
Timeline-based planning: Expressiveness and Complexity
Authors: Nicola Gigante
Date: 2019-02-16 00:00:00
Tasks: Scheduling

Timeline-based planning is an approach originally developed in the context of space mission planning and scheduling, where problem domains are modelled as systems made of a number of independent but interacting components, whose behaviour over time, the timelines, is governed by a set of temporal constraints. This approach is different from the action-based perspective of common PDDL-like planning languages. Timeline-based systems have been successfully deployed in a number of space missions and other domains. However, despite this practical success, a thorough theoretical understanding of the paradigm was missing. This thesis fills this gap, providing the first detailed account of formal and computational properties of the timeline-based approach to planning. In particular, we show that a particularly restricted variant of the formalism is already expressive enough to compactly capture action-based temporal planning problems. Then, finding a solution plan for a timeline-based planning problem is proved to be EXPSPACE-complete. Then, we study the problem of timeline-based planning with uncertainty, that include external components whose behaviour is not under the control of the planned system. We identify a few issues in the state-of-the-art approach based on flexible plans, proposing timeline-based games, a more general game-theoretic formulation of the problem, that addresses those issues. We show that winning strategies for such games can be found in doubly-exponential time. Then, we study the expressiveness of the formalism from a logic point of view, showing that (most of) timeline-based planning problems can be captured by Bounded TPTL with Past, a fragment of TPTL+P that, unlike the latter, keeps an EXPSPACE satisfiability problem. The logic is introduced and its satisfiabilty problem is solved by extending a recent one-pass tree-shaped tableau method for LTL.
Semi-supervised Learning on Graph with an Alternating Diffusion Process
Authors: Qilin Li, Senjian An, Ling Li, Wanquan Liu
Date: 2019-02-16 00:00:00
Tasks: graph construction

Graph-based semi-supervised learning usually involves two separate stages,
constructing an affinity graph and then propagating labels for transductive
inference on the graph. It is suboptimal to solve them independently, as the
correlation between the affinity graph and labels are not fully exploited. In
this paper, we integrate the two stages into one unified framework by
formulating the graph construction as a regularized function estimation problem
similar to label propagation. We propose an alternating diffusion process to
solve the two problems simultaneously, which allows us to learn the graph and
unknown labels in an iterative fashion. With the proposed framework, we are
able to adequately leverage both the given labels and estimated labels to
construct a better graph, and effectively propagate labels on such a dynamic
graph updated simultaneously with the newly obtained labels. Extensive
experiments on various real-world datasets have demonstrated the superiority of
the proposed method compared to other state-of-the-art methods.
Local Differential Privacy in Decentralized Optimization
Authors: Hanshen Xiao, Yu Ye, Srinivas Devadas
Date: 2019-02-16 00:00:00

Privacy concerns with sensitive data are receiving increasing attention. In this paper, we study local differential privacy (LDP) in interactive decentralized optimization. By constructing random local aggregators, we propose a framework to amplify LDP by a constant. We take Alternating Direction Method of Multipliers (ADMM), and decentralized gradient descent as two concrete examples, where experiments support our theory. In an asymptotic view, we address the following question: Under LDP, is it possible to design a distributed private minimizer for arbitrary closed convex constraints with utility loss not explicitly dependent on dimensionality? As an affiliated result, we also show that with merely linear secret sharing, information theoretic privacy is achievable for bounded colluding agents.
Differentiable reservoir computing
Authors: Lyudmila Grigoryeva, Juan-Pablo Ortega
Date: 2019-02-16 00:00:00

Much effort has been devoted in the last two decades to characterize the
situations in which a reservoir computing system exhibits the so-called echo
state (ESP) and fading memory (FMP) properties. These important features
amount, in mathematical terms, to the existence and continuity of global
reservoir system solutions. That research is complemented in this paper with
the characterization of the differentiability of reservoir filters for very
general classes of discrete-time deterministic inputs. This constitutes a novel
strong contribution to the long line of research on the ESP and the FMP and, in
particular, links to existing research on the input-dependence of the ESP.
Differentiability has been shown in the literature to be a key feature in the
learning of attractors of chaotic dynamical systems. A Volterra-type series
representation for reservoir filters with semi-infinite discrete-time inputs is
constructed in the analytic case using Taylor's theorem and corresponding
approximation bounds are provided. Finally, it is shown as a corollary of these
results that any fading memory filter can be uniformly approximated by a finite
Volterra series with finite memory.
Exploring Language Similarities with Dimensionality Reduction Technique
Authors: Sangarshanan Veeraraghavan
Date: 2019-02-16 00:00:00
Tasks: Dimensionality Reduction, Translation

In recent years several novel models were developed to process natural
language, development of accurate language translation systems have helped us
overcome geographical barriers and communicate ideas effectively. These models
are developed mostly for a few languages that are widely used while other
languages are ignored. Most of the languages that are spoken share lexical,
syntactic and sematic similarity with several other languages and knowing this
can help us leverage the existing model to build more specific and accurate
models that can be used for other languages, so here I have explored the idea
of representing several known popular languages in a lower dimension such that
their similarities can be visualized using simple 2 dimensional plots. This can
even help us understand newly discovered languages that may not share its
vocabulary with any of the existing languages.