pwc-archive/papers-with-abstracts huggingface dataset:
Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval
Authors: Hao Wang, Cheng Deng, Xinxu Xu, Wei Liu, Xinbo Gao, DaCheng Tao
Date: 2019-04-03 00:00:00
Tasks: Decoder, Image Retrieval, Retrieval, Sketch-Based Image Retrieval, Transfer Learning

Zero-shot sketch-based image retrieval (ZS-SBIR) is a task of cross-domain image retrieval from a natural image gallery with free-hand sketch under a zero-shot scenario. Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding natural image. However, the intrinsic visual sparsity and large intra-class variance of the sketch make the learning of the conditional decoder more difficult and hence achieve unsatisfactory retrieval performance. In this paper, we propose a novel stacked semantic-guided network to address the unique characteristics of sketches in ZS-SBIR. Specifically, we devise multi-layer feature fusion networks that incorporate different intermediate feature representation information in a deep neural network to alleviate the intrinsic sparsity of sketches. In order to improve visual knowledge transfer from seen to unseen classes, we elaborate a coarse-to-fine conditional decoder that generates coarse-grained category-specific corresponding features first (taking auxiliary semantic information as conditional input) and then generates fine-grained instance-specific corresponding features (taking sketch representation as conditional input). Furthermore, regression loss and classification loss are utilized to preserve the semantic and discriminative information of the synthesized features respectively. Extensive experiments on the large-scale Sketchy dataset and TU-Berlin dataset demonstrate that our proposed approach outperforms state-of-the-art methods by more than 20\% in retrieval performance.
Rep the Set: Neural Networks for Learning Set Representations
Authors: Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, Michalis Vazirgiannis
Date: 2019-04-03 00:00:00
Tasks: General Classification, Graph Classification, Text Categorization

In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventional machine learning algorithms are unable to process this kind of representations, since sets may vary in cardinality and elements lack a meaningful ordering. In this paper, we present a new neural network architecture, called RepSet, that can handle examples that are represented as sets of vectors. The proposed model computes the correspondences between an input set and some hidden sets by solving a series of network flow problems. This representation is then fed to a standard neural network architecture to produce the output. The architecture allows end-to-end gradient-based learning. We demonstrate RepSet on classification tasks, including text categorization, and graph classification, and we show that the proposed neural network achieves performance better or comparable to state-of-the-art algorithms.
Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms
Authors: Nataliya Le Vine, Matthew Zeigenfuse, Mark Rowan
Date: 2019-04-03 00:00:00
Tasks: Generative Adversarial Network

Extracting information from tables in documents presents a significant
challenge in many industries and in academic research. Existing methods which
take a bottom-up approach of integrating lines into cells and rows or columns
neglect the available prior information relating to table structure. Our
proposed method takes a top-down approach, first using a generative adversarial
network to map a table image into a standardised `skeleton' table form denoting
the approximate row and column borders without table content, then fitting
renderings of candidate latent table structures to the skeleton structure using
a distance measure optimised by a genetic algorithm.
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
Authors: Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Common Sense Reasoning, Coreference Resolution, Natural Language Understanding, Sentence

Commonsense reasoning is fundamental to natural language understanding. While
traditional methods rely heavily on human-crafted features and knowledge bases,
we explore learning commonsense knowledge from a large amount of raw text via
unsupervised learning. We propose two neural network models based on the Deep
Structured Semantic Models (DSSM) framework to tackle two classic commonsense
reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation
(PDP). Evaluation shows that the proposed models effectively capture contextual
information in the sentence and co-reference information between pronouns and
nouns, and achieve significant improvement over previous state-of-the-art
approaches.
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
Authors: Chao Ning, Fengqi You
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Decision Making Under Uncertainty

This paper reviews recent advances in the field of optimization under
uncertainty via a modern data lens, highlights key research challenges and
promise of data-driven optimization that organically integrates machine
learning and mathematical programming for decision-making under uncertainty,
and identifies potential research opportunities. A brief review of classical
mathematical programming techniques for hedging against uncertainty is first
presented, along with their wide spectrum of applications in Process Systems
Engineering. A comprehensive review and classification of the relevant
publications on data-driven distributionally robust optimization, data-driven
chance constrained program, data-driven robust optimization, and data-driven
scenario-based optimization is then presented. This paper also identifies
fertile avenues for future research that focuses on a closed-loop data-driven
optimization framework, which allows the feedback from mathematical programming
to machine learning, as well as scenario-based optimization leveraging the
power of deep learning techniques. Perspectives on online learning-based
data-driven multistage optimization with a learning-while-optimizing scheme is
presented.
Personalized Bundle List Recommendation
Authors: Jinze Bai, Chang Zhou, Junshuai Song, Xiaoru Qu, Weiting An, Zhao Li, Jun Gao
Date: 2019-04-03 00:00:00
Tasks: Decoder, Diversity, Marketing, Point Processes, Structured Prediction

Product bundling, offering a combination of items to customers, is one of the
marketing strategies commonly used in online e-commerce and offline retailers.
A high-quality bundle generalizes frequent items of interest, and diversity
across bundles boosts the user-experience and eventually increases transaction
volume. In this paper, we formalize the personalized bundle list recommendation
as a structured prediction problem and propose a bundle generation network
(BGN), which decomposes the problem into quality/diversity parts by the
determinantal point processes (DPPs). BGN uses a typical encoder-decoder
framework with a proposed feature-aware softmax to alleviate the inadequate
representation of traditional softmax, and integrates the masked beam search
and DPP selection to produce high-quality and diversified bundle list with an
appropriate bundle size. We conduct extensive experiments on three public
datasets and one industrial dataset, including two generated from co-purchase
records and the other two extracted from real-world online bundle services. BGN
significantly outperforms the state-of-the-art methods in terms of quality,
diversity and response time over all datasets. In particular, BGN improves the
precision of the best competitors by 16\% on average while maintaining the
highest diversity on four datasets, and yields a 3.85x improvement of response
time over the best competitors in the bundle list recommendation problem.
ICface: Interpretable and Controllable Face Reenactment Using GANs
Authors: Soumya Tripathy, Juho Kannala, Esa Rahtu
Date: 2019-04-03 00:00:00
Tasks: Face Reenactment, Video Editing

This paper presents a generic face animator that is able to control the pose and expressions of a given face image. The animation is driven by human interpretable control signals consisting of head pose angles and the Action Unit (AU) values. The control information can be obtained from multiple sources including external driving videos and manual controls. Due to the interpretable nature of the driving signal, one can easily mix the information between multiple sources (e.g. pose from one image and expression from another) and apply selective post-production editing. The proposed face animator is implemented as a two-stage neural network model that is learned in a self-supervised manner using a large video collection. The proposed Interpretable and Controllable face reenactment network (ICface) is compared to the state-of-the-art neural network-based face animation techniques in multiple tasks. The results indicate that ICface produces better visual quality while being more versatile than most of the comparison methods. The introduced model could provide a lightweight and easy to use tool for a multitude of advanced image and video editing tasks.
Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP
Authors: Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, Pierre Boulet
Date: 2019-04-03 00:00:00
Tasks: General Classification

Spiking neural networks (SNNs) are good candidates to produce
ultra-energy-efficient hardware. However, the performance of these models is
currently behind traditional methods. Introducing multi-layered SNNs is a
promising way to reduce this gap. We propose in this paper a new threshold
adaptation system which uses a timestamp objective at which neurons should
fire. We show that our method leads to state-of-the-art classification rates on
the MNIST dataset (98.60%) and the Faces/Motorbikes dataset (99.46%) with an
unsupervised SNN followed by a linear SVM. We also investigate the sparsity
level of the network by testing different inhibition policies and STDP rules.
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
Authors: Jeonghun Baek, Geewook Kim, Junyeop Lee, Sungrae Park, Dongyoon Han, Sangdoo Yun, Seong Joon Oh, Hwalsuk Lee
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Matching, model, Scene Text Recognition

Many new proposals for scene text recognition (STR) models have been introduced in recent years. While each claim to have pushed the boundary of the technology, a holistic and fair comparison has been largely missing in the field due to the inconsistent choices of training and evaluation datasets. This paper addresses this difficulty with three major contributions. First, we examine the inconsistencies of training and evaluation datasets, and the performance gap results from inconsistencies. Second, we introduce a unified four-stage STR framework that most existing STR models fit into. Using this framework allows for the extensive evaluation of previously proposed STR modules and the discovery of previously unexplored module combinations. Third, we analyze the module-wise contributions to performance in terms of accuracy, speed, and memory demand, under one consistent set of training and evaluation datasets. Such analyses clean up the hindrance on the current comparisons to understand the performance gain of the existing modules.
Semantic Bilinear Pooling for Fine-Grained Recognition
Authors: Xinjie Li, Chun Yang, Songlu Chen, Chao Zhu, Xu-Cheng Yin
Date: 2019-04-03 00:00:00
Tasks: General Classification, Multi-Label Learning

Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified than coarse categories. However, most of the recent deep learning based methods neglect the semantic structure of fine-grained objects and do not take advantage of the traditional fine-grained recognition techniques (e.g. coarse-to-fine classification). In this paper, we propose a novel framework with a two-branch network (coarse branch and fine branch), i.e., semantic bilinear pooling, for fine-grained recognition with a hierarchical label tree. This framework can adaptively learn the semantic information from the hierarchical levels. Specifically, we design a generalized cross-entropy loss for the training of the proposed framework to fully exploit the semantic priors via considering the relevance between adjacent levels and enlarge the distance between samples of different coarse classes. Furthermore, our method leverages only the fine branch when testing so that it adds no overhead to the testing time. Experimental results show that our proposed method achieves state-of-the-art performance on four public datasets.
DADA: Depth-aware Domain Adaptation in Semantic Segmentation
Authors: Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) is important for applications where large scale annotation of representative data is challenging. For semantic segmentation in particular, it helps deploy on real "target domain" data models that are trained on annotated images from a different "source domain", notably a virtual environment. To this end, most previous works consider semantic segmentation as the only mode of supervision for source domain data, while ignoring other, possibly available, information like depth. In this work, we aim at exploiting at best such a privileged information while training the UDA model. We propose a unified depth-aware UDA framework that leverages in several complementary ways the knowledge of dense depth in the source domain. As a result, the performance of the trained semantic segmentation model on the target domain is boosted. Our novel approach indeed achieves state-of-the-art performance on different challenging synthetic-2-real benchmarks.
Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
Authors: Ivan Bravi, Simon Lucas, Diego Perez-Liebana, Jialin Liu
Date: 2019-04-03 00:00:00

Game-based benchmarks have been playing an essential role in the development
of Artificial Intelligence (AI) techniques. Providing diverse challenges is
crucial to push research toward innovation and understanding in modern
techniques. Rinascimento provides a parameterised partially-observable
multiplayer card-based board game, these parameters can easily modify the
rules, objectives and items in the game. We describe the framework in all its
features and the game-playing challenge providing baseline game-playing AIs and
analysis of their skills. We reserve to agents' hyper-parameter tuning a
central role in the experiments highlighting how it can heavily influence the
performance. The base-line agents contain several additional contribution to
Statistical Forward Planning algorithms.
Modeling Vocabulary for Big Code Machine Learning
Authors: Hlib Babii, Andrea Janes, Romain Robbes
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning

When building machine learning models that operate on source code, several
decisions have to be made to model source-code vocabulary. These decisions can
have a large impact: some can lead to not being able to train models at all,
others significantly affect performance, particularly for Neural Language
Models. Yet, these decisions are not often fully described. This paper lists
important modeling choices for source code vocabulary, and explores their
impact on the resulting vocabulary on a large-scale corpus of 14,436 projects.
We show that a subset of decisions have decisive characteristics, allowing to
train accurate Neural Language Models quickly on a large corpus of 10,106
projects.
A Comprehensive Overhaul of Feature Distillation
Authors: Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, Jin Young Choi
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, image-classification, Image Classification, Knowledge Distillation, object-detection, Object Detection, Position, Semantic Segmentation

We investigate the design aspects of feature distillation methods achieving network compression and propose a novel feature distillation method in which the distillation loss is designed to make a synergy among various aspects: teacher transform, student transform, distillation feature position and distance function. Our proposed distillation loss includes a feature transform with a newly designed margin ReLU, a new distillation feature position, and a partial L2 distance function to skip redundant information giving adverse effects to the compression of student. In ImageNet, our proposed method achieves 21.65% of top-1 error with ResNet50, which outperforms the performance of the teacher network, ResNet152. Our proposed method is evaluated on various tasks such as image classification, object detection and semantic segmentation and achieves a significant performance improvement in all tasks. The code is available at https://sites.google.com/view/byeongho-heo/overhaul
Identifying Patient Groups based on Frequent Patterns of Patient Samples
Authors: Seyed Amin Tabatabaei, Xixi Lu, Mark Hoogendoorn, Hajo A. Reijers
Date: 2019-04-03 00:00:00
Tasks: Diagnostic

Grouping patients meaningfully can give insights about the different types of
patients, their needs, and the priorities. Finding groups that are meaningful
is however very challenging as background knowledge is often required to
determine what a useful grouping is. In this paper we propose an approach that
is able to find groups of patients based on a small sample of positive examples
given by a domain expert. Because of that, the approach relies on very limited
efforts by the domain experts. The approach groups based on the activities and
diagnostic/billing codes within health pathways of patients. To define such a
grouping based on the sample of patients efficiently, frequent patterns of
activities are discovered and used to measure the similarity between the care
pathways of other patients to the patients in the sample group. This approach
results in an insightful definition of the group. The proposed approach is
evaluated using several datasets obtained from a large university medical
center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury
and around 0.6 for diabetes.
A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality
Authors: Navid Azizan, Babak Hassibi
Date: 2019-04-03 00:00:00

Stochastic mirror descent (SMD) is a fairly new family of algorithms that has
recently found a wide range of applications in optimization, machine learning,
and control. It can be considered a generalization of the classical stochastic
gradient algorithm (SGD), where instead of updating the weight vector along the
negative direction of the stochastic gradient, the update is performed in a
"mirror domain" defined by the gradient of a (strictly convex) potential
function. This potential function, and the mirror domain it yields, provides
considerable flexibility in the algorithm compared to SGD. While many
properties of SMD have already been obtained in the literature, in this paper
we exhibit a new interpretation of SMD, namely that it is a risk-sensitive
optimal estimator when the unknown weight vector and additive noise are
non-Gaussian and belong to the exponential family of distributions. The
analysis also suggests a modified version of SMD, which we refer to as
symmetric SMD (SSMD). The proofs rely on some simple properties of Bregman
divergence, which allow us to extend results from quadratics and Gaussians to
certain convex functions and exponential families in a rather seamless way.
Recommending Scientific Videos based on Metadata Enrichment using Linked Open Data
Authors: Justyna Medrek, Christian Otto, Ralph Ewerth
Date: 2018-06-19 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), speech-recognition, Speech Recognition

The amount of available videos in the Web has significantly increased not
only for entertainment etc., but also to convey educational or scientific
information in an effective way. There are several web portals that offer
access to the latter kind of video material. One of them is the TIB AV-Portal
of the Leibniz Information Centre for Science and Technology (TIB), which hosts
scientific and educational video content. In contrast to other video portals,
automatic audiovisual analysis (visual concept classification, optical
character recognition, speech recognition) is utilized to enhance metadata
information and semantic search. In this paper, we propose to further exploit
and enrich this automatically generated information by linking it to the
Integrated Authority File (GND) of the German National Library. This
information is used to derive a measure to compare the similarity of two videos
which serves as a basis for recommending semantically similar videos. A user
study demonstrates the feasibility of the proposed approach.
Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints
Authors: Shaofeng Cai, Gang Chen, Beng Chin Ooi, Jinyang Gao
Date: 2019-04-03 00:00:00
Tasks: Model Compression

Deep learning models have been used to support analytics beyond simple aggregation, where deeper and wider models have been shown to yield great results. These models consume a huge amount of memory and computational operations. However, most of the large-scale industrial applications are often computational budget constrained. In practice, the peak workload of inference service could be 10x higher than the average cases, with the presence of unpredictable extreme cases. Lots of computational resources could be wasted during off-peak hours and the system may crash when the workload exceeds system capacity. How to support deep learning services with a dynamic workload cost-efficiently remains a challenging problem. In this paper, we address the challenge with a general and novel training scheme called model slicing, which enables deep learning models to provide predictions within the prescribed computational resource budget dynamically. Model slicing could be viewed as an elastic computation solution without requiring more computational resources. Succinctly, each layer in the model is divided into groups of a contiguous block of basic components (i.e. neurons in dense layers and channels in convolutional layers), and then partially ordered relation is introduced to these groups by enforcing that groups participated in each forward pass always starts from the first group to the dynamically-determined rightmost group. Trained by dynamically indexing the rightmost group with a single parameter slice rate, the network is engendered to build up group-wise and residual representation. Then during inference, a sub-model with fewer groups can be readily deployed for efficiency whose computation is roughly quadratic to the width controlled by the slice rate. Extensive experiments show that models trained with model slicing can effectively support on-demand workload with elastic inference cost.