pwc-archive/papers-with-abstracts huggingface dataset:
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting
Authors: Srayanta Mukherjee, Devashish Shankar, Atin Ghosh, Nilam Tathawadekar, Pramod Kompalli, Sunita Sarawagi, Krishnendu Chaudhury
Date: 2018-03-10 00:00:00
Tasks: Demand Forecasting, Time Series, Time Series Analysis

Accurate demand forecasts can help on-line retail organizations better plan
their supply-chain processes. The challenge, however, is the large number of
associative factors that result in large, non-stationary shifts in demand,
which traditional time series and regression approaches fail to model. In this
paper, we propose a Neural Network architecture called AR-MDN, that
simultaneously models associative factors, time-series trends and the variance
in the demand. We first identify several causal features and use a combination
of feature embeddings, MLP and LSTM to represent them. We then model the output
density as a learned mixture of Gaussian distributions. The AR-MDN can be
trained end-to-end without the need for additional supervision. We experiment
on a dataset of an year's worth of data over tens-of-thousands of products from
Flipkart. The proposed architecture yields a significant improvement in
forecasting accuracy when compared with existing alternatives.
Can Autism be Catered with Artificial Intelligence-Assisted Intervention Technology? A Literature Review
Authors: Muhammad Shoaib Jaliawala, Rizwan Ahmed Khan
Date: 2018-03-14 00:00:00

This article presents an extensive literature review of technology based
intervention methodologies for individuals facing Autism Spectrum Disorder
(ASD). Reviewed methodologies include: contemporary Computer Aided Systems
(CAS), Computer Vision Assisted Technologies (CVAT) and Virtual Reality (VR) or
Artificial Intelligence (AI)-Assisted interventions. The research over the past
decade has provided enough demonstrations that individuals with ASD have a
strong interest in technology based interventions, which are useful in both,
clinical settings as well as at home and classrooms. Despite showing great
promise, research in developing an advanced technology based intervention that
is clinically quantitative for ASD is minimal. Moreover, the clinicians are
generally not convinced about the potential of the technology based
interventions due to non-empirical nature of published results. A major reason
behind this lack of acceptability is that a vast majority of studies on
distinct intervention methodologies do not follow any specific standard or
research design. We conclude from our findings that there remains a gap between
the research community of computer science, psychology and neuroscience to
develop an AI assisted intervention technology for individuals suffering from
ASD. Following the development of a standardized AI based intervention
technology, a database needs to be developed, to devise effective AI
algorithms.
Impacts of Dirty Data: and Experimental Evaluation
Authors: Zhixin Qi, Hongzhi Wang, Jianzhong Li, Hong Gao
Date: 2018-03-16 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, General Classification

Data quality issues have attracted widespread attention due to the negative impacts of dirty data on data mining and machine learning results. The relationship between data quality and the accuracy of results could be applied on the selection of the appropriate algorithm with the consideration of data quality and the determination of the data share to clean. However, rare research has focused on exploring such relationship. Motivated by this, this paper conducts an experimental comparison for the effects of missing, inconsistent and conflicting data on classification and clustering algorithms. Based on the experimental findings, we provide guidelines for algorithm selection and data cleaning.
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
Authors: Xenia Miscouridou, François Caron, Yee Whye Teh
Date: 2018-03-16 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Link Prediction, Point Processes

We propose a novel class of network models for temporal dyadic interaction
data. Our goal is to capture a number of important features often observed in
social interactions: sparsity, degree heterogeneity, community structure and
reciprocity. We propose a family of models based on self-exciting Hawkes point
processes in which events depend on the history of the process. The key
component is the conditional intensity function of the Hawkes Process, which
captures the fact that interactions may arise as a response to past
interactions (reciprocity), or due to shared interests between individuals
(community structure). In order to capture the sparsity and degree
heterogeneity, the base (non time dependent) part of the intensity function
builds on compound random measures following Todeschini et al. (2016). We
conduct experiments on a variety of real-world temporal interaction data and
show that the proposed model outperforms many competing approaches for link
prediction, and leads to interpretable parameters.
Distributed Data Vending on Blockchain
Authors: Jiayu Zhou, Fengyi Tang, He Zhu, Ning Nan, Ziheng Zhou
Date: 2018-03-15 00:00:00
Tasks: Retrieval

Recent advances in blockchain technologies have provided exciting
opportunities for decentralized applications. Specifically, blockchain-based
smart contracts enable credible transactions without authorized third parties.
The attractive properties of smart contracts facilitate distributed data
vending, allowing for proprietary data to be securely exchanged on a
blockchain. Distributed data vending can transform domains such as healthcare
by encouraging data distribution from owners and enabling large-scale data
aggregation. However, one key challenge in distributed data vending is the
trade-off dilemma between the effectiveness of data retrieval, and the leakage
risk from indexing the data. In this paper, we propose a framework for
distributed data vending through a combination of data embedding and similarity
learning. We illustrate our framework through a practical scenario of
distributing and aggregating electronic medical records on a blockchain.
Extensive empirical results demonstrate the effectiveness of our framework.
Dynamic-structured Semantic Propagation Network
Authors: Xiaodan Liang, Hongfei Zhou, Eric Xing
Date: 2018-03-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Segmentation, Semantic Segmentation, Universal Segmentation

Semantic concept hierarchy is still under-explored for semantic segmentation
due to the inefficiency and complicated optimization of incorporating
structural inference into dense prediction. This lack of modeling semantic
correlations also makes prior works must tune highly-specified models for each
task due to the label discrepancy across datasets. It severely limits the
generalization capability of segmentation models for open set concept
vocabulary and annotation utilization. In this paper, we propose a
Dynamic-Structured Semantic Propagation Network (DSSPN) that builds a semantic
neuron graph by explicitly incorporating the semantic concept hierarchy into
network construction. Each neuron represents the instantiated module for
recognizing a specific type of entity such as a super-class (e.g. food) or a
specific concept (e.g. pizza). During training, DSSPN performs the
dynamic-structured neuron computation graph by only activating a sub-graph of
neurons for each image in a principled way. A dense semantic-enhanced neural
block is proposed to propagate the learned knowledge of all ancestor neurons
into each fine-grained child neuron for feature evolving. Another merit of such
semantic explainable structure is the ability of learning a unified model
concurrently on diverse datasets by selectively activating different neuron
sub-graphs for each annotation at each step. Extensive experiments on four
public semantic segmentation datasets (i.e. ADE20K, COCO-Stuff, Cityscape and
Mapillary) demonstrate the superiority of our DSSPN over state-of-the-art
segmentation models. Moreoever, we demonstrate a universal segmentation model
that is jointly trained on diverse datasets can surpass the performance of the
common fine-tuning scheme for exploiting multiple domain knowledge.
A Meaning-based Statistical English Math Word Problem Solver
Authors: Chao-Chun Liang, Yu-Shiang Wong, Yi-Chung Lin, Keh-Yih Su
Date: 2018-03-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Math

We introduce MeSys, a meaning-based approach, for solving English math word
problems (MWPs) via understanding and reasoning in this paper. It first
analyzes the text, transforms both body and question parts into their
corresponding logic forms, and then performs inference on them. The associated
context of each quantity is represented with proposed role-tags (e.g., nsubj,
verb, etc.), which provides the flexibility for annotating an extracted math
quantity with its associated context information (i.e., the physical meaning of
this quantity). Statistical models are proposed to select the operator and
operands. A noisy dataset is designed to assess if a solver solves MWPs mainly
via understanding or mechanical pattern matching. Experimental results show
that our approach outperforms existing systems on both benchmark datasets and
the noisy dataset, which demonstrates that the proposed approach understands
the meaning of each quantity in the text more.
Heuristics for vehicle routing problems: Sequence or set optimization?
Authors: Túlio A. M. Toffolo, Thibaut Vidal, Tony Wauters
Date: 2018-03-16 00:00:00
Tasks: Computational Efficiency, Heuristic Search

We investigate a structural decomposition for the capacitated vehicle routing
problem (CVRP) based on vehicle-to-customer "assignment" and visits
"sequencing" decision variables. We show that an heuristic search focused on
assignment decisions with a systematic optimal choice of sequences (using
Concorde TSP solver) during each move evaluation is promising but requires a
prohibitive computational effort. We therefore introduce an intermediate search
space, based on the dynamic programming procedure of Balas & Simonetti, which
finds a good compromise between intensification and computational efficiency. A
variety of speed-up techniques are proposed for a fast exploration:
neighborhood reductions, dynamic move filters, memory structures, and
concatenation techniques. Finally, a tunneling strategy is designed to reshape
the search space as the algorithm progresses.
  The combination of these techniques within a classical local search, as well
as in the unified hybrid genetic search (UHGS) leads to significant
improvements of solution accuracy. New best solutions are found for
surprisingly small instances with as few as 256 customers. These solutions had
not been attained up to now with classic neighborhoods. Overall, this research
permits to better evaluate the respective impact of sequence and assignment
optimization, proposes new ways of combining the optimization of these two
decision sets, and opens promising research perspectives for the CVRP and its
variants.
Multi-Frame Quality Enhancement for Compressed Video
Authors: Ren Yang, Mai Xu, Zulin Wang, Tianyi Li
Date: 2018-03-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Motion Compensation, Video Enhancement

The past few years have witnessed great success in applying deep learning to
enhance the quality of compressed image/video. The existing approaches mainly
focus on enhancing the quality of a single frame, ignoring the similarity
between consecutive frames. In this paper, we investigate that heavy quality
fluctuation exists across compressed video frames, and thus low quality frames
can be enhanced using the neighboring high quality frames, seen as Multi-Frame
Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach
for compressed video, as a first attempt in this direction. In our approach, we
firstly develop a Support Vector Machine (SVM) based detector to locate Peak
Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame
Convolutional Neural Network (MF-CNN) is designed to enhance the quality of
compressed video, in which the non-PQF and its nearest two PQFs are as the
input. The MF-CNN compensates motion between the non-PQF and PQFs through the
Motion Compensation subnet (MC-subnet). Subsequently, the Quality Enhancement
subnet (QE-subnet) reduces compression artifacts of the non-PQF with the help
of its nearest PQFs. Finally, the experiments validate the effectiveness and
generality of our MFQE approach in advancing the state-of-the-art quality
enhancement of compressed video. The code of our MFQE approach is available at
https://github.com/ryangBUAA/MFQE.git
Deeply supervised salient object detection with short connections
Authors: Qibin Hou, Ming-Ming Cheng, Xiao-Wei Hu, Ali Borji, Zhuowen Tu, Philip Torr
Date: 2016-11-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: Boundary Detection, Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Semantic Segmentation

Recent progress on saliency detection is substantial, benefiting mostly from
the explosive development of Convolutional Neural Networks (CNNs). Semantic
segmentation and saliency detection algorithms developed lately have been
mostly based on Fully Convolutional Neural Networks (FCNs). There is still a
large room for improvement over the generic FCN models that do not explicitly
deal with the scale-space problem. Holistically-Nested Edge Detector (HED)
provides a skip-layer structure with deep supervision for edge and boundary
detection, but the performance gain of HED on salience detection is not
obvious. In this paper, we propose a new method for saliency detection by
introducing short connections to the skip-layer structures within the HED
architecture. Our framework provides rich multi-scale feature maps at each
layer, a property that is critically needed to perform segment detection. Our
method produces state-of-the-art results on 5 widely tested salient object
detection benchmarks, with advantages in terms of efficiency (0.15 seconds per
image), effectiveness, and simplicity over the existing algorithms.
Deep Multiple Instance Learning for Zero-shot Image Tagging
Authors: Shafin Rahman, Salman Khan
Date: 2018-03-16 00:00:00
Tasks: Multiple Instance Learning, Zero-Shot Learning

In-line with the success of deep learning on traditional recognition problem,
several end-to-end deep models for zero-shot recognition have been proposed in
the literature. These models are successful to predict a single unseen label
given an input image, but does not scale to cases where multiple unseen objects
are present. In this paper, we model this problem within the framework of
Multiple Instance Learning (MIL). To the best of our knowledge, we propose the
first end-to-end trainable deep MIL framework for the multi-label zero-shot
tagging problem. Due to its novel design, the proposed framework has several
interesting features: (1) Unlike previous deep MIL models, it does not use any
off-line procedure (e.g., Selective Search or EdgeBoxes) for bag generation.
(2) During test time, it can process any number of unseen labels given their
semantic embedding vectors. (3) Using only seen labels per image as weak
annotation, it can produce a bounding box for each predicted labels. We
experiment with the NUS-WIDE dataset and achieve superior performance across
conventional, zero-shot and generalized zero-shot tagging tasks.
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
Authors: Shafin Rahman, Salman Khan, Fatih Porikli
Date: 2018-03-16 00:00:00
Tasks: Clustering, Novel Concepts, object-detection, Object Detection, Zero-Shot Learning, Zero-Shot Object Detection

Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of
a single dominant unseen object category in a test image. We hypothesize that
this setting is ill-suited for real-world applications where unseen objects
appear only as a part of a complex scene, warranting both the `recognition' and
`localization' of an unseen category. To address this limitation, we introduce
a new \emph{`Zero-Shot Detection'} (ZSD) problem setting, which aims at
simultaneously recognizing and locating object instances belonging to novel
categories without any training examples. We also propose a new experimental
protocol for ZSD based on the highly challenging ILSVRC dataset, adhering to
practical issues, e.g., the rarity of unseen objects. To the best of our
knowledge, this is the first end-to-end deep network for ZSD that jointly
models the interplay between visual and semantic domain information. To
overcome the noise in the automatically derived semantic descriptions, we
utilize the concept of meta-classes to design an original loss function that
achieves synergy between max-margin class separation and semantic space
clustering. Furthermore, we present a baseline approach extended from
recognition to detection setting. Our extensive experiments show significant
performance boost over the baseline on the imperative yet difficult ZSD
problem.
Transferable Pedestrian Motion Prediction Models at Intersections
Authors: Macheng Shen, Golnaz Habibi, Jonathan P. How
Date: 2018-03-15 00:00:00
Tasks: feature selection, motion prediction, Prediction, Reinforcement Learning, Trajectory Planning, Transfer Learning

One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection. We first discussed the feature selection for transferable pedestrian motion models in general. Following this discussion, we developed one transferable pedestrian motion prediction algorithm based on Inverse Reinforcement Learning (IRL) that infers pedestrian intentions and predicts future trajectories based on observed trajectory. We evaluated our algorithm on a dataset collected at two intersections, trained at one intersection and tested at the other intersection. We used the accuracy of augmented semi-nonnegative sparse coding (ASNSC), trained and tested at the same intersection as a baseline. The result shows that the proposed algorithm improves the baseline accuracy by 40% in the non-transfer task, and 16% in the transfer task.
Optimal Bipartite Network Clustering
Authors: Zhixin Zhou, Arash A. Amini
Date: 2018-03-15 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

We study bipartite community detection in networks, or more generally the
network biclustering problem. We present a fast two-stage procedure based on
spectral initialization followed by the application of a pseudo-likelihood
classifier twice. Under mild regularity conditions, we establish the weak
consistency of the procedure (i.e., the convergence of the misclassification
rate to zero) under a general bipartite stochastic block model. We show that
the procedure is optimal in the sense that it achieves the optimal convergence
rate that is achievable by a biclustering oracle, adaptively over the whole
class, up to constants. This is further formalized by deriving a minimax lower
bound over a class of biclustering problems. The optimal rate we obtain
sharpens some of the existing results and generalizes others to a wide regime
of average degree growth, from sparse networks with average degrees growing
arbitrarily slowly to fairly dense networks with average degrees of order
$\sqrt{n}$. As a special case, we recover the known exact recovery threshold in
the $\log n$ regime of sparsity. To obtain the consistency result, as part of
the provable version of the algorithm, we introduce a sub-block partitioning
scheme that is also computationally attractive, allowing for distributed
implementation of the algorithm without sacrificing optimality. The provable
algorithm is derived from a general class of pseudo-likelihood biclustering
algorithms that employ simple EM type updates. We show the effectiveness of
this general class by numerical simulations.
Deep Learning Reconstruction of Ultra-Short Pulses
Authors: Tom Zahavy, Alex Dikopoltsev, Oren Cohen, Shie Mannor, Mordechai Segev
Date: 2018-03-15 00:00:00
Tasks: Deep Learning

Ultra-short laser pulses with femtosecond to attosecond pulse duration are
the shortest systematic events humans can create. Characterization (amplitude
and phase) of these pulses is a key ingredient in ultrafast science, e.g.,
exploring chemical reactions and electronic phase transitions. Here, we propose
and demonstrate, numerically and experimentally, the first deep neural network
technique to reconstruct ultra-short optical pulses. We anticipate that this
approach will extend the range of ultrashort laser pulses that can be
characterized, e.g., enabling to diagnose very weak attosecond pulses.
One-shot Face Recognition by Promoting Underrepresented Classes
Authors: Yandong Guo, Lei Zhang
Date: 2017-07-18 00:00:00
Tasks: Face Identification, Face Recognition, regression

In this paper, we study the problem of training large-scale face
identification model with imbalanced training data. This problem naturally
exists in many real scenarios including large-scale celebrity recognition,
movie actor annotation, etc. Our solution contains two components. First, we
build a face feature extraction model, and improve its performance, especially
for the persons with very limited training samples, by introducing a
regularizer to the cross entropy loss for the multi-nomial logistic regression
(MLR) learning. This regularizer encourages the directions of the face features
from the same class to be close to the direction of their corresponding
classification weight vector in the logistic regression. Second, we build a
multi-class classifier using MLR on top of the learned face feature extraction
model. Since the standard MLR has poor generalization capability for the
one-shot classes even if these classes have been oversampled, we propose a
novel supervision signal called underrepresented-classes promotion loss, which
aligns the norms of the weight vectors of the one-shot classes (a.k.a.
underrepresented-classes) to those of the normal classes. In addition to the
original cross entropy loss, this new loss term effectively promotes the
underrepresented classes in the learned model and leads to a remarkable
improvement in face recognition performance.
  We test our solution on the MS-Celeb-1M low-shot learning benchmark task. Our
solution recognizes 94.89% of the test images at the precision of 99\% for the
one-shot classes. To the best of our knowledge, this is the best performance
among all the published methods using this benchmark task with the same setup,
including all the participants in the recent MS-Celeb-1M challenge at ICCV
2017.