pwc-archive/papers-with-abstracts huggingface dataset:
Homogeneity-Based Transmissive Process to Model True and False News in Social Networks
Authors: Jooyeon Kim, Dongkwan Kim, Alice Oh
Date: 2018-11-16 00:00:00

An overwhelming number of true and false news stories are posted and shared
in social networks, and users diffuse the stories based on multiple factors.
Diffusion of news stories from one user to another depends not only on the
stories' content and the genuineness but also on the alignment of the topical
interests between the users. In this paper, we propose a novel Bayesian
nonparametric model that incorporates homogeneity of news stories as the key
component that regulates the topical similarity between the posting and sharing
users' topical interests. Our model extends hierarchical Dirichlet process to
model the topics of the news stories and incorporates Bayesian Gaussian process
latent variable model to discover the homogeneity values. We train our model on
a real-world social network dataset and find homogeneity values of news stories
that strongly relate to their labels of genuineness and their contents.
Finally, we show that the supervised version of our model predicts the labels
of news stories better than the state-of-the-art neural network and Bayesian
models.
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Authors: Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, Amnon Shashua
Date: 2019-02-11 00:00:00
Tasks: Variational Monte Carlo

Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states. In practical applications, neural-network states inherit numerical schemes used in Variational Monte Carlo, most notably the use of Markov-Chain Monte-Carlo (MCMC) sampling to estimate quantum expectations. The local stochastic sampling in MCMC caps the potential advantages of neural networks in two ways: (i) Its intrinsic computational cost sets stringent practical limits on the width and depth of the networks, and therefore limits their expressive capacity; (ii) Its difficulty in generating precise and uncorrelated samples can result in estimations of observables that are very far from their true value. Inspired by the state-of-the-art generative models used in machine learning, we propose a specialized Neural Network architecture that supports efficient and exact sampling, completely circumventing the need for Markov Chain sampling. We demonstrate our approach for two-dimensional interacting spin models, showcasing the ability to obtain accurate results on larger system sizes than those currently accessible to neural-network quantum states.
Policy Learning for Fairness in Ranking
Authors: Ashudeep Singh, Thorsten Joachims
Date: 2019-02-11 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Fairness, Learning-To-Rank

Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g. online marketplaces, job placement, admissions). To address this need, we propose a general LTR framework that can optimize a wide range of utility metrics (e.g. NDCG) while satisfying fairness of exposure constraints with respect to the items. This framework expands the class of learnable ranking functions to stochastic ranking policies, which provides a language for rigorously expressing fairness specifications. Furthermore, we provide a new LTR algorithm called Fair-PG-Rank for directly searching the space of fair ranking policies via a policy-gradient approach. Beyond the theoretical evidence in deriving the framework and the algorithm, we provide empirical results on simulated and real-world datasets verifying the effectiveness of the approach in individual and group-fairness settings.
S-DOD-CNN: Doubly Injecting Spatially-Preserved Object Information for Event Recognition
Authors: Hyungtae Lee, Sungmin Eum, Heesung Kwon
Date: 2019-02-11 00:00:00
Tasks: Object, object-detection, Object Detection

We present a novel event recognition approach called Spatially-preserved Doubly-injected Object Detection CNN (S-DOD-CNN), which incorporates the spatially preserved object detection information in both a direct and an indirect way. Indirect injection is carried out by simply sharing the weights between the object detection modules and the event recognition module. Meanwhile, our novelty lies in the fact that we have preserved the spatial information for the direct injection. Once multiple regions-of-intereset (RoIs) are acquired, their feature maps are computed and then projected onto a spatially-preserving combined feature map using one of the four RoI Projection approaches we present. In our architecture, combined feature maps are generated for object detection which are directly injected to the event recognition module. Our method provides the state-of-the-art accuracy for malicious event recognition.
MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
Authors: Nabil Ibtehaz, M. Sohel Rahman
Date: 2019-02-11 00:00:00
Proceeding: ScienceDirect 2019 2
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

In recent years Deep Learning has brought about a breakthrough in Medical
Image Segmentation. U-Net is the most prominent deep network in this regard,
which has been the most popular architecture in the medical imaging community.
Despite outstanding overall performance in segmenting multimodal medical
images, from extensive experimentations on challenging datasets, we found out
that the classical U-Net architecture seems to be lacking in certain aspects.
Therefore, we propose some modifications to improve upon the already
state-of-the-art U-Net model. Hence, following the modifications we develop a
novel architecture MultiResUNet as the potential successor to the successful
U-Net architecture. We have compared our proposed architecture MultiResUNet
with the classical U-Net on a vast repertoire of multimodal medical images.
Albeit slight improvements in the cases of ideal images, a remarkable gain in
performance has been attained for challenging images. We have evaluated our
model on five different datasets, each with their own unique challenges, and
have obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%,
1.41%, and 0.62% respectively.
The StarCraft Multi-Agent Challenge
Authors: Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, Shimon Whiteson
Date: 2019-02-11 00:00:00
Tasks: Benchmarking, MuJoCo, Multi-agent Reinforcement Learning, Real-Time Strategy Games, Reinforcement Learning, Reinforcement Learning (RL), SMAC, SMAC+, Starcraft, Starcraft II

In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only on their private observations. This is an attractive research area since such problems are relevant to a large number of real-world systems and are also more amenable to evaluation than general-sum problems. Standardised environments such as the ALE and MuJoCo have allowed single-agent RL to move beyond toy domains, such as grid worlds. However, there is no comparable benchmark for cooperative multi-agent RL. As a result, most papers in this field use one-off toy problems, making it difficult to measure real progress. In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap. SMAC is based on the popular real-time strategy game StarCraft II and focuses on micromanagement challenges where each unit is controlled by an independent agent that must act based on local observations. We offer a diverse set of challenge maps and recommendations for best practices in benchmarking and evaluations. We also open-source a deep multi-agent RL learning framework including state-of-the-art algorithms. We believe that SMAC can provide a standard benchmark environment for years to come. Videos of our best agents for several SMAC scenarios are available at: https://youtu.be/VZ7zmQ_obZ0.
Registration-free Face-SSD: Single shot analysis of smiles, facial attributes, and affect in the wild
Authors: Youngkyoon Jang, Hatice Gunes, Ioannis Patras
Date: 2019-02-11 00:00:00
Tasks: Arousal Estimation, Attribute, Face Detection, Smile Recognition

In this paper, we present a novel single shot face-related task analysis
method, called Face-SSD, for detecting faces and for performing various
face-related (classification/regression) tasks including smile recognition,
face attribute prediction and valence-arousal estimation in the wild. Face-SSD
uses a Fully Convolutional Neural Network (FCNN) to detect multiple faces of
different sizes and recognise/regress one or more face-related classes.
Face-SSD has two parallel branches that share the same low-level filters, one
branch dealing with face detection and the other one with face analysis tasks.
The outputs of both branches are spatially aligned heatmaps that are produced
in parallel - therefore Face-SSD does not require that face detection, facial
region extraction, size normalisation, and facial region processing are
performed in subsequent steps. Our contributions are threefold: 1) Face-SSD is
the first network to perform face analysis without relying on pre-processing
such as face detection and registration in advance - Face-SSD is a simple and a
single FCNN architecture simultaneously performing face detection and
face-related task analysis - those are conventionally treated as separate
consecutive tasks; 2) Face-SSD is a generalised architecture that is applicable
for various face analysis tasks without modifying the network structure - this
is in contrast to designing task-specific architectures; and 3) Face-SSD
achieves real-time performance (21 FPS) even when detecting multiple faces and
recognising multiple classes in a given image. Experimental results show that
Face-SSD achieves state-of-the-art performance in various face analysis tasks
by reaching a recognition accuracy of 95.76% for smile detection, 90.29% for
attribute prediction, and Root Mean Square (RMS) error of 0.44 and 0.39 for
valence and arousal estimation.
Deep Learning Methods for Event Verification and Image Repurposing Detection
Authors: M. Goebel, A. Flenner, L. Nataraj, B. S. Manjunath
Date: 2019-02-11 00:00:00
Tasks: Deep Learning, General Classification

The authenticity of images posted on social media is an issue of growing
concern. Many algorithms have been developed to detect manipulated images, but
few have investigated the ability of deep neural network based approaches to
verify the authenticity of image labels, such as event names. In this paper, we
propose several novel methods to predict if an image was captured at one of
several noteworthy events. We use a set of images from several recorded events
such as storms, marathons, protests, and other large public gatherings. Two
strategies of applying pre-trained Imagenet network for event verification are
presented, with two modifications for each strategy. The first method uses the
features from the last convolutional layer of a pre-trained network as input to
a classifier. We also consider the effects of tuning the convolutional weights
of the pre-trained network to improve classification. The second method
combines many features extracted from smaller scales and uses the output of a
pre-trained network as the input to a second classifier. For both methods, we
investigated several different classifiers and tested many different
pre-trained networks. Our experiments demonstrate both these approaches are
effective for event verification and image re-purposing detection. The
classification at the global scale tends to marginally outperform our tested
local methods and fine tuning the network further improves the results.
KTBoost: Combined Kernel and Tree Boosting
Authors: Fabio Sigrist
Date: 2019-02-11 00:00:00
Tasks: regression

We introduce a novel boosting algorithm called `KTBoost' which combines kernel boosting and tree boosting. In each boosting iteration, the algorithm adds either a regression tree or reproducing kernel Hilbert space (RKHS) regression function to the ensemble of base learners. Intuitively, the idea is that discontinuous trees and continuous RKHS regression functions complement each other, and that this combination allows for better learning of functions that have parts with varying degrees of regularity such as discontinuities and smooth parts. We empirically show that KTBoost significantly outperforms both tree and kernel boosting in terms of predictive accuracy in a comparison on a wide array of data sets.
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
Authors: Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger
Date: 2019-02-11 00:00:00
Tasks: Memorization

One of the central goals of Recurrent Neural Networks (RNNs) is to learn long-term dependencies in sequential data. Nevertheless, the most popular training method, Truncated Backpropagation through Time (TBPTT), categorically forbids learning dependencies beyond the truncation horizon. In contrast, the online training algorithm Real Time Recurrent Learning (RTRL) provides untruncated gradients, with the disadvantage of impractically large computational costs. Recently published approaches reduce these costs by providing noisy approximations of RTRL. We present a new approximation algorithm of RTRL, Optimal Kronecker-Sum Approximation (OK). We prove that OK is optimal for a class of approximations of RTRL, which includes all approaches published so far. Additionally, we show that OK has empirically negligible noise: Unlike previous algorithms it matches TBPTT in a real world task (character-level Penn TreeBank) and can exploit online parameter updates to outperform TBPTT in a synthetic string memorization task. Code availiable on github.
Whole-Chain Recommendations
Authors: Xiangyu Zhao, Long Xia, Linxin Zou, Hui Liu, Dawei Yin, Jiliang Tang
Date: 2019-02-11 00:00:00
Tasks: Multi-agent Reinforcement Learning, Recommendation Systems, Reinforcement Learning, Reinforcement Learning (RL)

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.
Improving Generalization and Stability of Generative Adversarial Networks
Authors: Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh
Date: 2019-02-11 00:00:00
Proceeding: ICLR 2019 5

Generative Adversarial Networks (GANs) are one of the most popular tools for
learning complex high dimensional distributions. However, generalization
properties of GANs have not been well understood. In this paper, we analyze the
generalization of GANs in practical settings. We show that discriminators
trained on discrete datasets with the original GAN loss have poor
generalization capability and do not approximate the theoretically optimal
discriminator. We propose a zero-centered gradient penalty for improving the
generalization of the discriminator by pushing it toward the optimal
discriminator. The penalty guarantees the generalization and convergence of
GANs. Experiments on synthetic and large scale datasets verify our theoretical
analysis.
Interaction-Transformation Evolutionary Algorithm for Symbolic Regression
Authors: Fabricio Olivetti de Franca, Guilherme Seidyo Imai Aldeia
Date: 2019-02-11 00:00:00
Tasks: regression, Symbolic Regression

The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Genetic Programming. This paper introduces an Evolutionary Algorithm capable of evolving a population of IT expressions supported only by the mutation operator. The results show that this representation is capable of finding better approximations to real-world data sets when compared to traditional approaches and a state-of-the-art Genetic Programming algorithm.
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
Authors: Constantin Grigo, Phaedon-Stelios Koutsourelakis
Date: 2019-02-11 00:00:00
Tasks: Small Data Image Classification, Uncertainty Quantification, Variational Inference

The automated construction of coarse-grained models represents a pivotal component in computer simulation of physical systems and is a key enabler in various analysis and design tasks related to uncertainty quantification. Pertinent methods are severely inhibited by the high-dimension of the parametric input and the limited number of training input/output pairs that can be generated when computationally demanding forward models are considered. Such cases are frequently encountered in the modeling of random heterogeneous media where the scale of the microstructure necessitates the use of high-dimensional random vectors and very fine discretizations of the governing equations. The present paper proposes a probabilistic Machine Learning framework that is capable of operating in the presence of Small Data by exploiting aspects of the physical structure of the problem as well as contextual knowledge. As a result, it can perform comparably well under extrapolative conditions. It unifies the tasks of dimensionality and model-order reduction through an encoder-decoder scheme that simultaneously identifies a sparse set of salient lower-dimensional microstructural features and calibrates an inexpensive, coarse-grained model which is predictive of the output. Information loss is accounted for and quantified in the form of probabilistic predictive estimates. The learning engine is based on Stochastic Variational Inference. We demonstrate how the variational objectives can be used not only to train the coarse-grained model, but also to suggest refinements that lead to improved predictions.
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification
Authors: Blaž Škrlj, Jan Kralj, Janez Konc, Marko Robnik-Šikonja, Nada Lavrač
Date: 2019-02-11 00:00:00
Tasks: Classification, General Classification, Network Embedding, Node Classification, Structural Node Embedding

Network node embedding is an active research subfield of complex network analysis. This paper contributes a novel approach to learning network node embeddings and direct node classification using a node ranking scheme coupled with an autoencoder-based neural network architecture. The main advantages of the proposed Deep Node Ranking (DNR) algorithm are competitive or better classification performance, significantly higher learning speed and lower space requirements when compared to state-of-the-art approaches on 15 real-life node classification benchmarks. Furthermore, it enables exploration of the relationship between symbolic and the derived sub-symbolic node representations, offering insights into the learned node space structure. To avoid the space complexity bottleneck in a direct node classification setting, DNR computes stationary distributions of personalized random walks from given nodes in mini-batches, scaling seamlessly to larger networks. The scaling laws associated with DNR were also investigated on 1488 synthetic Erd\H{o}s-R\'enyi networks, demonstrating its scalability to tens of millions of links.
Color Image and Multispectral Image Denoising Using Block Diagonal Representation
Authors: Zhaoming Kong, Xiaowei Yang
Date: 2019-02-11 00:00:00
Tasks: Denoising, Image Denoising

Filtering images of more than one channel is challenging in terms of both
efficiency and effectiveness. By grouping similar patches to utilize the
self-similarity and sparse linear approximation of natural images, recent
nonlocal and transform-domain methods have been widely used in color and
multispectral image (MSI) denoising. Many related methods focus on the modeling
of group level correlation to enhance sparsity, which often resorts to a
recursive strategy with a large number of similar patches. The importance of
the patch level representation is understated. In this paper, we mainly
investigate the influence and potential of representation at patch level by
considering a general formulation with block diagonal matrix. We further show
that by training a proper global patch basis, along with a local principal
component analysis transform in the grouping dimension, a simple
transform-threshold-inverse method could produce very competitive results. Fast
implementation is also developed to reduce computational complexity. Extensive
experiments on both simulated and real datasets demonstrate its robustness,
effectiveness and efficiency.
MISO: Mutual Information Loss with Stochastic Style Representations for Multimodal Image-to-Image Translation
Authors: Sanghyeon Na, Seungjoo Yoo, Jaegul Choo
Date: 2019-02-11 00:00:00
Tasks: Image Reconstruction, Image-to-Image Translation, Translation

Unpaired multimodal image-to-image translation is a task of translating a
given image in a source domain into diverse images in the target domain,
overcoming the limitation of one-to-one mapping. Existing multimodal
translation models are mainly based on the disentangled representations with an
image reconstruction loss. We propose two approaches to improve multimodal
translation quality. First, we use a content representation from the source
domain conditioned on a style representation from the target domain. Second,
rather than using a typical image reconstruction loss, we design MILO (Mutual
Information LOss), a new stochastically-defined loss function based on
information theory. This loss function directly reflects the interpretation of
latent variables as a random variable. We show that our proposed model Mutual
Information with StOchastic Style Representation(MISO) achieves
state-of-the-art performance through extensive experiments on various
real-world datasets.