pwc-archive/papers-with-abstracts huggingface dataset:
Capturing Between-Tasks Covariance and Similarities Using Multivariate Linear Mixed Models
Authors: Aviv Navon, Saharon Rosset
Date: 2018-12-10 00:00:00
Tasks: regression

We consider the problem of predicting several response variables using the same set of explanatory variables. This setting naturally induces a group structure over the coefficient matrix, in which every explanatory variable corresponds to a set of related coefficients. Most of the existing methods that utilize this group formation assume that the similarities between related coefficients arise solely through a joint sparsity structure. In this paper, we propose a procedure for constructing an estimator of a multivariate regression coefficient matrix that directly models and captures the within-group similarities, by employing a multivariate linear mixed model formulation, with a joint estimation of covariance matrices for coefficients and errors via penalized likelihood. Our approach, which we term Multivariate random Regression with Covariance Estimation (MrRCE) encourages structured similarity in parameters, in which coefficients for the same variable in related tasks sharing the same sign and similar magnitude. We illustrate the benefits of our approach in synthetic and real examples, and show that the proposed method outperforms natural competitors and alternative estimators under several model settings.
Machine Learning-based Link Fault Identification and Localization in Complex Networks
Authors: Srinikethan Madapuzi Srinivasan, Tram Truong-Huu, Mohan Gurusamy
Date: 2018-12-10 00:00:00
Tasks: BIG-bench Machine Learning, Fault localization

With the proliferation of network devices and rapid development in
information technology, networks such as Internet of Things are increasing in
size and becoming more complex with heterogeneous wired and wireless links. In
such networks, link faults may result in a link disconnection without immediate
replacement or a link reconnection, e.g., a wireless node changes its access
point. Identifying whether a link disconnection or a link reconnection has
occurred and localizing the failed link become a challenging problem. An active
probing approach requires a long time to probe the network by sending signaling
messages on different paths, thus incurring significant communication delay and
overhead. In this paper, we adopt a passive approach and develop a three-stage
machine learning-based technique, namely ML-LFIL that identifies and localizes
link faults by analyzing the measurements captured from the normal traffic
flows, including aggregate flow rate, end-to-end delay and packet loss. ML-LFIL
learns the traffic behavior in normal working conditions and different link
fault scenarios. We train the learning model using support vector machine,
multi-layer perceptron and random forest. We implement ML-LFIL and carry out
extensive experiments using Mininet platform. Performance studies show that
ML-LFIL achieves high accuracy while requiring much lower fault localization
time compared to the active probing approach.
Deep Learning Power Allocation in Massive MIMO
Authors: Luca Sanguinetti, Alessio Zappone, Merouane Debbah
Date: 2018-12-10 00:00:00
Tasks: Deep Learning

This work advocates the use of deep learning to perform max-min and max-prod power allocation in the downlink of Massive MIMO networks. More precisely, a deep neural network is trained to learn the map between the positions of user equipments (UEs) and the optimal power allocation policies, and then used to predict the power allocation profiles for a new set of UEs' positions. The use of deep learning significantly improves the complexity-performance trade-off of power allocation, compared to traditional optimization-oriented methods. Particularly, the proposed approach does not require the computation of any statistical average, which would be instead necessary by using standard methods, and is able to guarantee near-optimal performance.
Statement networks: a power structure narrative as depicted by newspapers
Authors: Shoumik Sharar Chowdhury, Nazmus Saquib, Niamat Zawad, Manash Kumar Mandal, Syed Haque
Date: 2018-12-10 00:00:00
Tasks: Articles, Community Detection, Form, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We report a data mining pipeline and subsequent analysis to understand the
core periphery power structure created in three national newspapers in
Bangladesh, as depicted by statements made by people appearing in news.
Statements made by one actor about another actor can be considered a form of
public conversation. Named entity recognition techniques can be used to create
a temporal actor network from such conversations, which shows some unique
structure, and reveals much room for improvement in news reporting and also the
top actors' conversation preferences. Our results indicate there is a presence
of cliquishness between powerful political leaders when it comes to their
appearance in news. We also show how these cohesive cores form through the news
articles, and how, over a decade, news cycles change the actors belonging in
these groups.
Spatial Knowledge Distillation to aid Visual Reasoning
Authors: Somak Aditya, Rudra Saha, Yezhou Yang, Chitta Baral
Date: 2018-12-10 00:00:00
Tasks: Diagnostic, Knowledge Distillation, Question Answering, Relational Reasoning, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

For tasks involving language and vision, the current state-of-the-art methods
tend not to leverage any additional information that might be present to gather
relevant (commonsense) knowledge. A representative task is Visual Question
Answering where large diagnostic datasets have been proposed to test a system's
capability of answering questions about images. The training data is often
accompanied by annotations of individual object properties and spatial
locations. In this work, we take a step towards integrating this additional
privileged information in the form of spatial knowledge to aid in visual
reasoning. We propose a framework that combines recent advances in knowledge
distillation (teacher-student framework), relational reasoning and
probabilistic logical languages to incorporate such knowledge in existing
neural networks for the task of Visual Question Answering. Specifically, for a
question posed against an image, we use a probabilistic logical language to
encode the spatial knowledge and the spatial understanding about the question
in the form of a mask that is directly provided to the teacher network. The
student network learns from the ground-truth information as well as the
teachers prediction via distillation. We also demonstrate the impact of
predicting such a mask inside the teachers network using attention.
Empirically, we show that both the methods improve the test accuracy over a
state-of-the-art approach on a publicly available dataset.
EDF: Ensemble, Distill, and Fuse for Easy Video Labeling
Authors: Giulio Zhou, Subramanya Dulloor, David G. Andersen, Michael Kaminsky
Date: 2018-12-10 00:00:00
Tasks: Data Augmentation, Object, object-detection, Object Detection

We present a way to rapidly bootstrap object detection on unseen videos using
minimal human annotations. We accomplish this by combining two complementary
sources of knowledge (one generic and the other specific) using bounding box
merging and model distillation. The first (generic) knowledge source is
obtained from ensembling pre-trained object detectors using a novel bounding
box merging and confidence reweighting scheme. We make the observation that
model distillation with data augmentation can train a specialized detector that
outperforms the noisy labels it was trained on, and train a Student Network on
the ensemble detections that obtains higher mAP than the ensemble itself. The
second (specialized) knowledge source comes from training a detector (which we
call the Supervised Labeler) on a labeled subset of the video to generate
detections on the unlabeled portion. We demonstrate on two popular vehicular
datasets that these techniques work to emit bounding boxes for all vehicles in
the frame with higher mean average precision (mAP) than any of the reference
networks used, and that the combination of ensembled and human-labeled data
produces object detections that outperform either alone.
A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks
Authors: Shaohua Wang, Song Gao, Xin Feng, Alan T. Murray, Yuan Zeng
Date: 2018-12-10 00:00:00
Tasks: Management

Given different types of constraints on human life, people must make
decisions that satisfy social activity needs. Minimizing costs (i.e., distance,
time, or money) associated with travel plays an important role in perceived and
realized social quality of life. Identifying optimal interaction locations on
road networks when there are multiple moving objects (MMO) with space-time
constraints remains a challenge. In this research, we formalize the problem of
finding dynamic ideal interaction locations for MMO as a spatial optimization
model and introduce a context-based geoprocessing heuristic framework to
address this problem. As a proof of concept, a case study involving
identification of a meetup location for multiple people under traffic
conditions is used to validate the proposed geoprocessing framework. Five
heuristic methods with regard to efficient shortest-path search space have been
tested. We find that the R* tree-based algorithm performs the best with high
quality solutions and low computation time. This framework is implemented in a
GIS environment to facilitate integration with external geographic contextual
information, e.g., temporary road barriers, points of interest (POI), and
real-time traffic information, when dynamically searching for ideal meetup
sites. The proposed method can be applied in trip planning, carpooling
services, collaborative interaction, and logistics management.
3D Scene Parsing via Class-Wise Adaptation
Authors: Daichi Ono, Hiroyuki Yabe, Tsutomu Horikawa
Date: 2018-12-10 00:00:00
Tasks: Scene Parsing, Segmentation, Semantic Segmentation, Simultaneous Localization and Mapping

We propose the method that uses only computer graphics datasets to parse the
real world 3D scenes. 3D scene parsing based on semantic segmentation is
required to implement the categorical interaction in the virtual world.
Convolutional Neural Networks (CNNs) have recently shown state-of-theart
performance on computer vision tasks including semantic segmentation. However,
collecting and annotating a huge amount of data are needed to train CNNs.
Especially in the case of semantic segmentation, annotating pixel by pixel
takes a significant amount of time and often makes mistakes. In contrast,
computer graphics can generate a lot of accurate annotated data and easily
scale up by changing camera positions, textures and lights. Despite these
advantages, models trained on computer graphics datasets cannot perform well on
real data, which is known as the domain shift. To address this issue, we first
present that depth modal and synthetic noise are effective to reduce the domain
shift. Then, we develop the class-wise adaptation which obtains domain
invariant features of CNNs. To reduce the domain shift, we create computer
graphics rooms with a lot of props, and provide photo-realistic rendered
images.We also demonstrate the application which is combined semantic
segmentation with Simultaneous Localization and Mapping (SLAM). Our application
performs accurate 3D scene parsing in real-time on an actual room.
Learning Non-Uniform Hypergraph for Multi-Object Tracking
Authors: Longyin Wen, Dawei Du, Shengkun Li, Xiao Bian, Siwei Lyu
Date: 2018-12-10 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking

The majority of Multi-Object Tracking (MOT) algorithms based on the
tracking-by-detection scheme do not use higher order dependencies among objects
or tracklets, which makes them less effective in handling complex scenarios. In
this work, we present a new near-online MOT algorithm based on non-uniform
hypergraph, which can model different degrees of dependencies among tracklets
in a unified objective. The nodes in the hypergraph correspond to the tracklets
and the hyperedges with different degrees encode various kinds of dependencies
among them. Specifically, instead of setting the weights of hyperedges with
different degrees empirically, they are learned automatically using the
structural support vector machine algorithm (SSVM). Several experiments are
carried out on various challenging datasets (i.e., PETS09, ParkingLot sequence,
SubwayFace, and MOT16 benchmark), to demonstrate that our method achieves
favorable performance against the state-of-the-art MOT methods.
Reliable Identification of Redundant Kernels for Convolutional Neural Network Compression
Authors: Wei Wang, Liqiang Zhu
Date: 2018-12-10 00:00:00
Tasks: Neural Network Compression

To compress deep convolutional neural networks (CNNs) with large memory
footprint and long inference time, this paper proposes a novel pruning
criterion using layer-wised Ln-norm of feature maps. Different from existing
pruning criteria, which are mainly based on L1-norm of convolution kernels, the
proposed method utilizes Ln-norm of output feature maps after non-linear
activations, where n is a variable, increasing from 1 at the first convolution
layer to inf at the last convolution layer. With the ability of accurately
identifying unimportant convolution kernels, the proposed method achieves a
good balance between model size and inference accuracy. The experiments on
ImageNet and the successful application in railway surveillance system show
that the proposed method outperforms existing kernel-norm-based methods and is
generally applicable to any deep neural network with convolution operations.
Fast convergence rates of deep neural networks for classification
Authors: Yongdai Kim, Ilsang Ohn, Dongha Kim
Date: 2018-12-10 00:00:00
Tasks: Classification, General Classification

We derive the fast convergence rates of a deep neural network (DNN) classifier with the rectified linear unit (ReLU) activation function learned using the hinge loss. We consider three cases for a true model: (1) a smooth decision boundary, (2) smooth conditional class probability, and (3) the margin condition (i.e., the probability of inputs near the decision boundary is small). We show that the DNN classifier learned using the hinge loss achieves fast rate convergences for all three cases provided that the architecture (i.e., the number of layers, number of nodes and sparsity). is carefully selected. An important implication is that DNN architectures are very flexible for use in various cases without much modification. In addition, we consider a DNN classifier learned by minimizing the cross-entropy, and show that the DNN classifier achieves a fast convergence rate under the condition that the conditional class probabilities of most data are sufficiently close to either 1 or zero. This assumption is not unusual for image recognition because human beings are extremely good at recognizing most images. To confirm our theoretical explanation, we present the results of a small numerical study conducted to compare the hinge loss and cross-entropy.
Task-Free Continual Learning
Authors: Rahaf Aljundi, Klaas Kelchtermans, Tinne Tuytelaars
Date: 2018-12-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Continual Learning, Face Recognition, Self-Supervised Learning

Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at all times. This setup, however, is rarely encountered in practical applications. Therefore we investigate how to transform continual learning to an online setup. We develop a system that keeps on learning over time in a streaming fashion, with data distributions gradually changing and without the notion of separate tasks. To this end, we build on the work on Memory Aware Synapses, and show how this method can be made online by providing a protocol to decide i) when to update the importance weights, ii) which data to use to update them, and iii) how to accumulate the importance weights at each update step. Experimental results show the validity of the approach in the context of two applications: (self-)supervised learning of a face recognition model by watching soap series and learning a robot to avoid collisions.
PoseFix: Model-agnostic General Human Pose Refinement Network
Authors: Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee
Date: 2018-12-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: 2D Human Pose Estimation, Keypoint Detection, model, Multi-Person Pose Estimation, Pose Estimation

Multi-person pose estimation from a 2D image is an essential technique for
human behavior understanding. In this paper, we propose a human pose refinement
network that estimates a refined pose from a tuple of an input image and input
pose. The pose refinement was performed mainly through an end-to-end trainable
multi-stage architecture in previous methods. However, they are highly
dependent on pose estimation models and require careful model design. By
contrast, we propose a model-agnostic pose refinement method. According to a
recent study, state-of-the-art 2D human pose estimation methods have similar
error distributions. We use this error statistics as prior information to
generate synthetic poses and use the synthesized poses to train our model. In
the testing stage, pose estimation results of any other methods can be input to
the proposed method. Moreover, the proposed model does not require code or
knowledge about other methods, which allows it to be easily used in the
post-processing step. We show that the proposed approach achieves better
performance than the conventional multi-stage refinement models and
consistently improves the performance of various state-of-the-art pose
estimation methods on the commonly used benchmark. The code is available in
this https URL\footnote{\url{https://github.com/mks0601/PoseFix_RELEASE}}.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Authors: Chenguang Zhu, Michael Zeng, Xuedong Huang
Date: 2018-12-10 00:00:00
Tasks: Conversational Question Answering, Coreference Resolution, Machine Reading Comprehension, Question Answering, Reading Comprehension, Spoken Language Understanding

Conversational question answering (CQA) is a novel QA task that requires
understanding of dialogue context. Different from traditional single-turn
machine reading comprehension (MRC) tasks, CQA includes passage comprehension,
coreference resolution, and contextual understanding. In this paper, we propose
an innovated contextualized attention-based deep neural network, SDNet, to fuse
context into traditional MRC models. Our model leverages both inter-attention
and self-attention to comprehend conversation context and extract relevant
information from passage. Furthermore, we demonstrated a novel method to
integrate the latest BERT contextual model. Empirical results show the
effectiveness of our model, which sets the new state of the art result in CoQA
leaderboard, outperforming the previous best model by 1.6% F1. Our ensemble
model further improves the result by 2.7% F1.
q-means: A quantum algorithm for unsupervised machine learning
Authors: Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash
Date: 2018-12-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: BIG-bench Machine Learning, Clustering, Quantum Machine Learning

Quantum machine learning is one of the most promising applications of a
full-scale quantum computer. Over the past few years, many quantum machine
learning algorithms have been proposed that can potentially offer considerable
speedups over the corresponding classical algorithms. In this paper, we
introduce q-means, a new quantum algorithm for clustering which is a canonical
problem in unsupervised machine learning. The $q$-means algorithm has
convergence and precision guarantees similar to $k$-means, and it outputs with
high probability a good approximation of the $k$ cluster centroids like the
classical algorithm. Given a dataset of $N$ $d$-dimensional vectors $v_i$ (seen
as a matrix $V \in \mathbb{R}^{N \times d})$ stored in QRAM, the running time
of q-means is $\widetilde{O}\left( k d \frac{\eta}{\delta^2}\kappa(V)(\mu(V) +
k \frac{\eta}{\delta}) + k^2 \frac{\eta^{1.5}}{\delta^2} \kappa(V)\mu(V)
\right)$ per iteration, where $\kappa(V)$ is the condition number, $\mu(V)$ is
a parameter that appears in quantum linear algebra procedures and $\eta =
\max_{i} ||v_{i}||^{2}$. For a natural notion of well-clusterable datasets, the
running time becomes $\widetilde{O}\left( k^2 d \frac{\eta^{2.5}}{\delta^3} +
k^{2.5} \frac{\eta^2}{\delta^3} \right)$ per iteration, which is linear in the
number of features $d$, and polynomial in the rank $k$, the maximum square norm
$\eta$ and the error parameter $\delta$. Both running times are only
polylogarithmic in the number of datapoints $N$. Our algorithm provides
substantial savings compared to the classical $k$-means algorithm that runs in
time $O(kdN)$ per iteration, particularly for the case of large datasets.
Closed-form Inference and Prediction in Gaussian Process State-Space Models
Authors: Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
Date: 2018-12-10 00:00:00
Tasks: Form, State Space Models, Time Series, Time Series Analysis, Variational Inference

We examine an analytic variational inference scheme for the Gaussian Process
State Space Model (GPSSM) - a probabilistic model for system identification and
time-series modelling. Our approach performs variational inference over both
the system states and the transition function. We exploit Markov structure in
the true posterior, as well as an inducing point approximation to achieve
linear time complexity in the length of the time series. Contrary to previous
approaches, no Monte Carlo sampling is required: inference is cast as a
deterministic optimisation problem. In a number of experiments, we demonstrate
the ability to model non-linear dynamics in the presence of both process and
observation noise as well as to impute missing information (e.g. velocities
from raw positions through time), to de-noise, and to estimate the underlying
dimensionality of the system. Finally, we also introduce a closed-form method
for multi-step prediction, and a novel criterion for assessing the quality of
our approximate posterior.
Learning Style Compatibility for Furniture
Authors: Divyansh Aggarwal, Elchin Valiyev, Fadime Sener, Angela Yao
Date: 2018-12-09 00:00:00
Tasks: Attribute

When judging style, a key question that often arises is whether or not a pair
of objects are compatible with each other. In this paper we investigate how
Siamese networks can be used efficiently for assessing the style compatibility
between images of furniture items. We show that the middle layers of pretrained
CNNs can capture essential information about furniture style, which allows for
efficient applications of such networks for this task. We also use a joint
image-text embedding method that allows for the querying of stylistically
compatible furniture items, along with additional attribute constraints based
on text. To evaluate our methods, we collect and present a large scale dataset
of images of furniture of different style categories accompanied by text
attributes.