pwc-archive/papers-with-abstracts huggingface dataset:
Context-Aware Synthesis and Placement of Object Instances
Authors: Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz
Date: 2018-12-06 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Object, Scene Parsing

Learning to insert an object instance into an image in a semantically
coherent manner is a challenging and interesting problem. Solving it requires
(a) determining a location to place an object in the scene and (b) determining
its appearance at the location. Such an object insertion model can potentially
facilitate numerous image editing and scene parsing applications. In this
paper, we propose an end-to-end trainable neural network for the task of
inserting an object instance mask of a specified class into the semantic label
map of an image. Our network consists of two generative modules where one
determines where the inserted object mask should be (i.e., location and scale)
and the other determines what the object mask shape (and pose) should look
like. The two modules are connected together via a spatial transformation
network and jointly trained. We devise a learning procedure that leverage both
supervised and unsupervised data and show our model can insert an object at
diverse locations with various appearances. We conduct extensive experimental
validations with comparisons to strong baselines to verify the effectiveness of
the proposed network.
Counterfactual Critic Multi-Agent Training for Scene Graph Generation
Authors: Long Chen, Hanwang Zhang, Jun Xiao, Xiangnan He, ShiLiang Pu, Shih-Fu Chang
Date: 2018-12-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: counterfactual, Graph Generation, Scene Graph Generation, Scene Understanding

Scene graphs -- objects as nodes and visual relationships as edges -- describe the whereabouts and interactions of the things and stuff in an image for comprehensive scene understanding. To generate coherent scene graphs, almost all existing methods exploit the fruitful visual context by modeling message passing among objects, fitting the dynamic nature of reasoning with visual context, eg, "person" on "bike" can help to determine the relationship "ride", which in turn contributes to the category confidence of the two objects. However, we argue that the scene dynamics is not properly learned by using the prevailing cross-entropy based supervised learning paradigm, which is not sensitive to graph inconsistency: errors at the hub or non-hub nodes are unfortunately penalized equally. To this end, we propose a Counterfactual critic Multi-Agent Training (CMAT) approach to resolve the mismatch. CMAT is a multi-agent policy gradient method that frames objects as cooperative agents, and then directly maximizes a graph-level metric as the reward. In particular, to assign the reward properly to each agent, CMAT uses a counterfactual baseline that disentangles the agent-specific reward by fixing the dynamics of other agents. Extensive validations on the challenging Visual Genome benchmark show that CMAT achieves a state-of-the-art by significant performance gains under various settings and metrics.
Arbitrary Style Transfer with Style-Attentional Networks
Authors: Dae Young Park, Kwang Hee Lee
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Style Transfer

Arbitrary style transfer aims to synthesize a content image with the style of an image to create a third image that has never been seen before. Recent arbitrary style transfer algorithms find it challenging to balance the content structure and the style patterns. Moreover, simultaneously maintaining the global and local style patterns is difficult due to the patch-based mechanism. In this paper, we introduce a novel style-attentional network (SANet) that efficiently and flexibly integrates the local style patterns according to the semantic spatial distribution of the content image. A new identity loss function and multi-level feature embeddings enable our SANet and decoder to preserve the content structure as much as possible while enriching the style patterns. Experimental results demonstrate that our algorithm synthesizes stylized images in real-time that are higher in quality than those produced by the state-of-the-art algorithms.
Quantifying Generalization in Reinforcement Learning
Authors: Karl Cobbe, Oleg Klimov, Chris Hesse, Tae-hoon Kim, John Schulman
Date: 2018-12-06 00:00:00
Tasks: Data Augmentation, Deep Reinforcement Learning, L2 Regularization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. This practice offers relatively little insight into an agent's ability to generalize. We address this issue by using procedurally generated environments to construct distinct training and test sets. Most notably, we introduce a new environment called CoinRun, designed as a benchmark for generalization in RL. Using CoinRun, we find that agents overfit to surprisingly large training sets. We then show that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.
Continual Learning Augmented Investment Decisions
Authors: Daniel Philps, Tillman Weyde, Artur d'Avila Garcez, Roy Batchelor
Date: 2018-12-06 00:00:00
Tasks: Continual Learning, Decision Making, Dynamic Time Warping

Investment decisions can benefit from incorporating an accumulated knowledge
of the past to drive future decision making. We introduce Continual Learning
Augmentation (CLA) which is based on an explicit memory structure and a feed
forward neural network (FFNN) base model and used to drive long term financial
investment decisions. We demonstrate that our approach improves accuracy in
investment decision making while memory is addressed in an explainable way. Our
approach introduces novel remember cues, consisting of empirically learned
change points in the absolute error series of the FFNN. Memory recall is also
novel, with contextual similarity assessed over time by sampling distances
using dynamic time warping (DTW). We demonstrate the benefits of our approach
by using it in an expected return forecasting task to drive investment
decisions. In an investment simulation in a broad international equity universe
between 2003-2017, our approach significantly outperforms FFNN base models. We
also illustrate how CLA's memory addressing works in practice, using a worked
example to demonstrate the explainability of our approach.
Comparative Document Summarisation via Classification
Authors: Umanga Bista, Alexander Mathews, Minjeong Shin, Aditya Krishna Menon, Lexing Xie
Date: 2018-12-06 00:00:00
Tasks: Binary Classification, Classification, General Classification, Interpretable Machine Learning

This paper considers extractive summarisation in a comparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13 months. We observe that gradient-based optimisation outperforms discrete and baseline approaches in 14 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.
Layer Flexible Adaptive Computational Time
Authors: Lida Zhang, Abdolghani Ebrahimi, Diego Klabjan
Date: 2018-12-06 00:00:00
Tasks: Language Modeling, Language Modelling

Deep recurrent neural networks perform well on sequence data and are the model of choice. However, it is a daunting task to decide the structure of the networks, i.e. the number of layers, especially considering different computational needs of a sequence. We propose a layer flexible recurrent neural network with adaptive computation time, and expand it to a sequence to sequence model. Different from the adaptive computation time model, our model has a dynamic number of transmission states which vary by step and sequence. We evaluate the model on a financial data set and Wikipedia language modeling. Experimental results show the performance improvement of 7\% to 12\% and indicate the model's ability to dynamically change the number of layers along with the computational steps.
Adpositional Supersenses for Mandarin Chinese
Authors: YIlun Zhu, Yang Liu, Siyao Peng, Austin Blodgett, Yushi Zhao, Nathan Schneider
Date: 2018-12-06 00:00:00
Tasks: Machine Translation, Translation

This study adapts Semantic Network of Adposition and Case Supersenses (SNACS)
annotation to Mandarin Chinese and demonstrates that the same supersense
categories are appropriate for Chinese adposition semantics. We annotated 15
chapters of The Little Prince, with high interannotator agreement. The parallel
corpus gives insight into differences in construal between the two languages'
adpositions, namely a number of construals that are frequent in Chinese but
rare or unattested in the English corpus. The annotated corpus can further
support automatic disambiguation of adpositions in Chinese, and the common
inventory of supersenses between the two languages can potentially serve
cross-linguistic tasks such as machine translation.
Skin Lesions Classification Using Convolutional Neural Networks in Clinical Images
Authors: Danilo Barros Mendes, Nilton Correia da Silva
Date: 2018-12-06 00:00:00
Tasks: General Classification

Skin lesions are conditions that appear on a patient due to many different
reasons. One of these can be because of an abnormal growth in skin tissue,
defined as cancer. This disease plagues more than 14.1 million patients and had
been the cause of more than 8.2 million deaths, worldwide. Therefore, the
construction of a classification model for 12 lesions, including Malignant
Melanoma and Basal Cell Carcinoma, is proposed. Furthermore, in this work, it
is used a ResNet-152 architecture, which was trained over 3,797 images, later
augmented by a factor of 29 times, using positional, scale, and lighting
transformations. Finally, the network was tested with 956 images and achieve an
area under the curve (AUC) of 0.96 for Melanoma and 0.91 for Basal Cell
Carcinoma.
Assigning a Grade: Accurate Measurement of Road Quality Using Satellite Imagery
Authors: Gabriel Cadamuro, Aggrey Muhebwa, Jay Taneja
Date: 2018-12-01 00:00:00
Tasks: Binary Classification, General Classification

Roads are critically important infrastructure to societal and economic
development, with huge investments made by governments every year. However,
methods for monitoring those investments tend to be time-consuming, laborious,
and expensive, placing them out of reach for many developing regions. In this
work, we develop a model for monitoring the quality of road infrastructure
using satellite imagery. For this task, we harness two trends: the increasing
availability of high-resolution, often-updated satellite imagery, and the
enormous improvement in speed and accuracy of convolutional neural
network-based methods for performing computer vision tasks. We employ a unique
dataset of road quality information on 7000km of roads in Kenya combined with
50cm resolution satellite imagery. We create models for a binary classification
task as well as a comprehensive 5-category classification task, with accuracy
scores of 88 and 73 percent respectively. We also provide evidence of the
robustness of our methods with challenging held-out scenarios, though we note
some improvement is still required for confident analysis of a never before
seen road. We believe these results are well-positioned to have substantial
impact on a broad set of transport applications.
RDEC: Integrating Regularization into Deep Embedded Clustering for Imbalanced Datasets
Authors: Yaling Tao, Kentaro Takagi, Kouta Nakata
Date: 2018-12-06 00:00:00
Tasks: Clustering

Clustering is a fundamental machine learning task and can be used in many
applications. With the development of deep neural networks (DNNs), combining
techniques from DNNs with clustering has become a new research direction and
achieved some success. However, few studies have focused on the imbalanced-data
problem which commonly occurs in real-world applications. In this paper, we
propose a clustering method, regularized deep embedding clustering (RDEC), that
integrates virtual adversarial training (VAT), a network regularization
technique, with a clustering method called deep embedding clustering (DEC). DEC
optimizes cluster assignments by pushing data more densely around centroids in
latent space, but it is sometimes sensitive to the initial location of
centroids, especially in the case of imbalanced data, where the minor class has
less chance to be assigned a good centroid. RDEC introduces regularization
using VAT to ensure the model's robustness to local perturbations of data. VAT
pushes data that are similar in the original space closer together in the
latent space, bunching together data from minor classes and thereby
facilitating cluster identification by RDEC. Combining the advantages of DEC
and VAT, RDEC attains state-of-the-art performance on both balanced and
imbalanced benchmark/real-world datasets. For example, accuracies are as high
as 98.41% on MNIST dataset and 85.45% on a highly imbalanced dataset derived
from the MNIST, which is nearly 8% higher than the current best result.
Learning Dynamic Embeddings from Temporal Interactions
Authors: Srijan Kumar, Xikun Zhang, Jure Leskovec
Date: 2018-12-06 00:00:00
Tasks: Representation Learning

Modeling a sequence of interactions between users and items (e.g., products,
posts, or courses) is crucial in domains such as e-commerce, social networking,
and education to predict future interactions. Representation learning presents
an attractive solution to model the dynamic evolution of user and item
properties, where each user/item can be embedded in a euclidean space and its
evolution can be modeled by dynamic changes in embedding. However, existing
embedding methods either generate static embeddings, treat users and items
independently, or are not scalable.
  Here we present JODIE, a coupled recurrent model to jointly learn the dynamic
embeddings of users and items from a sequence of user-item interactions. JODIE
has three components. First, the update component updates the user and item
embedding from each interaction using their previous embeddings with the two
mutually-recursive Recurrent Neural Networks. Second, a novel projection
component is trained to forecast the embedding of users at any future time.
Finally, the prediction component directly predicts the embedding of the item
in a future interaction. For models that learn from a sequence of interactions,
traditional training data batching cannot be done due to complex user-user
dependencies. Therefore, we present a novel batching algorithm called t-Batch
that generates time-consistent batches of training data that can run in
parallel, giving massive speed-up.
  We conduct six experiments on two prediction tasks---future interaction
prediction and state change prediction---using four real-world datasets. We
show that JODIE outperforms six state-of-the-art algorithms in these tasks by
up to 22.4%. Moreover, we show that JODIE is highly scalable and up to 9.2x
faster than comparable models. As an additional experiment, we illustrate that
JODIE can predict student drop-out from courses five interactions in advance.
Adversarially Learned Anomaly Detection
Authors: Houssam Zenati, Manon Romain, Chuan Sheng Foo, Bruno Lecouat, Vijay Ramaseshan Chandrasekhar
Date: 2018-12-06 00:00:00
Tasks: Anomaly Detection

Anomaly detection is a significant and hence well-studied problem. However,
developing effective anomaly detection methods for complex and high-dimensional
data remains a challenge. As Generative Adversarial Networks (GANs) are able to
model the complex high-dimensional distributions of real-world data, they offer
a promising approach to address this challenge. In this work, we propose an
anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based
on bi-directional GANs, that derives adversarially learned features for the
anomaly detection task. ALAD then uses reconstruction errors based on these
adversarially learned features to determine if a data sample is anomalous. ALAD
builds on recent advances to ensure data-space and latent-space
cycle-consistencies and stabilize GAN training, which results in significantly
improved anomaly detection performance. ALAD achieves state-of-the-art
performance on a range of image and tabular datasets while being several
hundred-fold faster at test time than the only published GAN-based method.
Generalizability of predictive models for intensive care unit patients
Authors: Alistair E. W. Johnson, Tom J. Pollard, Tristan Naumann
Date: 2018-12-06 00:00:00

A large volume of research has considered the creation of predictive models
for clinical data; however, much existing literature reports results using only
a single source of data. In this work, we evaluate the performance of models
trained on the publicly-available eICU Collaborative Research Database. We show
that cross-validation using many distinct centers provides a reasonable
estimate of model performance in new centers. We further show that a single
model trained across centers transfers well to distinct hospitals, even
compared to a model retrained using hospital-specific data. Our results
motivate the use of multi-center datasets for model development and highlight
the need for data sharing among hospitals to maximize model performance.
Differentially Private Data Generative Models
Authors: Qingrong Chen, Chong Xiang, Minhui Xue, Bo Li, Nikita Borisov, Dali Kaarfar, Haojin Zhu
Date: 2018-12-06 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning, Inference Attack, Membership Inference Attack

Deep neural networks (DNNs) have recently been widely adopted in various
applications, and such success is largely due to a combination of algorithmic
breakthroughs, computation resource improvements, and access to a large amount
of data. However, the large-scale data collections required for deep learning
often contain sensitive information, therefore raising many privacy concerns.
Prior research has shown several successful attacks in inferring sensitive
training data information, such as model inversion, membership inference, and
generative adversarial networks (GAN) based leakage attacks against
collaborative deep learning. In this paper, to enable learning efficiency as
well as to generate data with privacy guarantees and high utility, we propose a
differentially private autoencoder-based generative model (DP-AuGM) and a
differentially private variational autoencoder-based generative model
(DP-VaeGM). We evaluate the robustness of two proposed models. We show that
DP-AuGM can effectively defend against the model inversion, membership
inference, and GAN-based attacks. We also show that DP-VaeGM is robust against
the membership inference attack. We conjecture that the key to defend against
the model inversion and GAN-based attacks is not due to differential privacy
but the perturbation of training data. Finally, we demonstrate that both
DP-AuGM and DP-VaeGM can be easily integrated with real-world machine learning
applications, such as machine learning as a service and federated learning,
which are otherwise threatened by the membership inference attack and the
GAN-based attack, respectively.
Teacher-Student Compression with Generative Adversarial Networks
Authors: Ruishan Liu, Nicolo Fusi, Lester Mackey
Date: 2018-12-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: CPU, Diversity, Generative Adversarial Network, image-classification, Image Classification, Model Compression

More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices. Teacher-student compression (TSC), also known as distillation, alleviates this burden by training a less expensive student model to mimic the expensive teacher model while maintaining most of the original accuracy. However, when fresh data is unavailable for the compression task, the teacher's training data is typically reused, leading to suboptimal compression. In this work, we propose to augment the compression dataset with synthetic data from a generative adversarial network (GAN) designed to approximate the training data distribution. Our GAN-assisted TSC (GAN-TSC) significantly improves student accuracy for expensive models such as large random forests and deep neural networks on both tabular and image datasets. Building on these results, we propose a comprehensive metric---the TSC Score---to evaluate the quality of synthetic datasets based on their induced TSC performance. The TSC Score captures both data diversity and class affinity, and we illustrate its benefits over the popular Inception Score in the context of image classification.
GADGET SVM: A Gossip-bAseD sub-GradiEnT Solver for Linear SVMs
Authors: Haimonti Dutta, Nitin Nataraj
Date: 2018-12-05 00:00:00
Tasks: Binary Classification, General Classification

In the era of big data, an important weapon in a machine learning
researcher's arsenal is a scalable Support Vector Machine (SVM) algorithm. SVMs
are extensively used for solving classification problems. Traditional
algorithms for learning SVMs often scale super linearly with training set size
which becomes infeasible very quickly for large data sets. In recent years,
scalable algorithms have been designed which study the primal or dual
formulations of the problem. This often suggests a way to decompose the problem
and facilitate development of distributed algorithms. In this paper, we present
a distributed algorithm for learning linear Support Vector Machines in the
primal form for binary classification called Gossip-bAseD sub-GradiEnT (GADGET)
SVM. The algorithm is designed such that it can be executed locally on nodes of
a distributed system. Each node processes its local homogeneously partitioned
data and learns a primal SVM model. It then gossips with random neighbors about
the classifier learnt and uses this information to update the model. Extensive
theoretical and empirical results suggest that this anytime algorithm has
performance comparable to its centralized and online counterparts.