pwc-archive/papers-with-abstracts huggingface dataset:
GDPP: Learning Diverse Generations Using Determinantal Point Process
Authors: Mohamed Elfeki, Camille Couprie, Morgane Riviere, Mohamed Elhoseiny
Date: 2018-11-30 00:00:00
Tasks: Diversity

Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images. An essential characteristic of generative models is their ability to produce multi-modal outputs. However, while training, they are often susceptible to mode collapse, that is models are limited in mapping input noise to only a few modes of the true data distribution. In this work, we draw inspiration from Determinantal Point Process (DPP) to propose an unsupervised penalty loss that alleviates mode collapse while producing higher quality samples. DPP is an elegant probabilistic measure used to model negative correlations within a subset and hence quantify its diversity. We use DPP kernel to model the diversity in real data as well as in synthetic data. Then, we devise an objective term that encourages generators to synthesize data with similar diversity to real data. In contrast to previous state-of-the-art generative models that tend to use additional trainable parameters or complex training paradigms, our method does not change the original training scheme. Embedded in an adversarial training and variational autoencoder, our Generative DPP approach shows a consistent resistance to mode-collapse on a wide variety of synthetic data and natural image datasets including MNIST, CIFAR10, and CelebA, while outperforming state-of-the-art methods for data-efficiency, generation quality, and convergence-time whereas being 5.8x faster than its closest competitor.
Corresponding Projections for Orphan Screening
Authors: Sven Giesselbach, Katrin Ullrich, Michael Kamp, Daniel Paurat, Thomas Gärtner
Date: 2018-11-30 00:00:00
Tasks: Drug Discovery, Transfer Learning

We propose a novel transfer learning approach for orphan screening called
corresponding projections. In orphan screening the learning task is to predict
the binding affinities of compounds to an orphan protein, i.e., one for which
no training data is available. The identification of compounds with high
affinity is a central concern in medicine since it can be used for drug
discovery and design. Given a set of prediction models for proteins with
labelled training data and a similarity between the proteins, corresponding
projections constructs a model for the orphan protein from them such that the
similarity between models resembles the one between proteins. Under the
assumption that the similarity resemblance holds, we derive an efficient
algorithm for kernel methods. We empirically show that the approach outperforms
the state-of-the-art in orphan screening.
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
Authors: Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier
Date: 2018-11-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder, Real-Time Strategy Games, Starcraft, State Estimation

We formulate the problem of defogging as state estimation and future state
prediction from previous, partial observations in the context of real-time
strategy games. We propose to employ encoder-decoder neural networks for this
task, and introduce proxy tasks and baselines for evaluation to assess their
ability of capturing basic game rules and high-level dynamics. By combining
convolutional neural networks and recurrent networks, we exploit spatial and
sequential correlations and train well-performing models on a large dataset of
human games of StarCraft: Brood War. Finally, we demonstrate the relevance of
our models to downstream tasks by applying them for enemy unit prediction in a
state-of-the-art, rule-based StarCraft bot. We observe improvements in win
rates against several strong community bots.
Learning Interpretable Rules for Multi-label Classification
Authors: Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier, Michael Rapp
Date: 2018-11-30 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification (MLC) is a supervised learning problem in which,
contrary to standard multiclass classification, an instance can be associated
with several class labels simultaneously. In this chapter, we advocate a
rule-based approach to multi-label classification. Rule learning algorithms are
often employed when one is not only interested in accurate predictions, but
also requires an interpretable theory that can be understood, analyzed, and
qualitatively evaluated by domain experts. Ideally, by revealing patterns and
regularities contained in the data, a rule-based theory yields new insights in
the application domain. Recently, several authors have started to investigate
how rule-based models can be used for modeling multi-label data. Discussing
this task in detail, we highlight some of the problems that make rule learning
considerably more challenging for MLC than for conventional classification.
While mainly focusing on our own previous work, we also provide a short
overview of related work in this area.
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL
Authors: Bilal Kartal, Pablo Hernandez-Leal, Matthew E. taylor
Date: 2018-11-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (DRL) has achieved great successes in recent
years with the help of novel methods and higher compute power. However, there
are still several challenges to be addressed such as convergence to locally
optimal policies and long training times. In this paper, firstly, we augment
Asynchronous Advantage Actor-Critic (A3C) method with a novel self-supervised
auxiliary task, i.e. \emph{Terminal Prediction}, measuring temporal closeness
to terminal states, namely A3C-TP. Secondly, we propose a new framework where
planning algorithms such as Monte Carlo tree search or other sources of
(simulated) demonstrators can be integrated to asynchronous distributed DRL
methods. Compared to vanilla A3C, our proposed methods both learn faster and
converge to better policies on a two-player mini version of the Pommerman game.
Adversarial Defense by Stratified Convolutional Sparse Coding
Authors: Bo Sun, Nian-hsuan Tsai, Fangchen Liu, Ronald Yu, Hao Su
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Adversarial Defense

We propose an adversarial defense method that achieves state-of-the-art performance among attack-agnostic adversarial defense methods while also maintaining robustness to input resolution, scale of adversarial perturbation, and scale of dataset size. Based on convolutional sparse coding, we construct a stratified low-dimensional quasi-natural image space that faithfully approximates the natural image space while also removing adversarial perturbations. We introduce a novel Sparse Transformation Layer (STL) in between the input image and the first layer of the neural network to efficiently project images into our quasi-natural image space. Our experiments show state-of-the-art performance of our method compared to other attack-agnostic adversarial defense methods in various adversarial settings.
Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition
Authors: Tong Yu, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Date: 2018-11-30 00:00:00
Tasks: Online surgical phase recognition, Surgical phase recognition

Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior. In laparoscopic procedures one particular algorithm needed for such systems is the identification of surgical phases, for which the current state of the art is a model based on a CNN-LSTM. A number of previous works using models of this kind have trained them in a fully supervised manner, requiring a fully annotated dataset. Instead, our work confronts the problem of learning surgical phase recognition in scenarios presenting scarce amounts of annotated data (under 25% of all available video recordings). We propose a teacher/student type of approach, where a strong predictor called the teacher, trained beforehand on a small dataset of ground truth-annotated videos, generates synthetic annotations for a larger dataset, which another model - the student - learns from. In our case, the teacher features a novel CNN-biLSTM-CRF architecture, designed for offline inference only. The student, on the other hand, is a CNN-LSTM capable of making real-time predictions. Results for various amounts of manually annotated videos demonstrate the superiority of the new CNN-biLSTM-CRF predictor as well as improved performance from the CNN-LSTM trained using synthetic labels generated for unannotated videos. For both offline and online surgical phase recognition with very few annotated recordings available, this new teacher/student strategy provides a valuable performance improvement by efficiently leveraging the unannotated data.
Unsupervised learning with GLRM feature selection reveals novel traumatic brain injury phenotypes
Authors: Aaron J. Masino, Kaitlin A. Folweiler
Date: 2018-11-30 00:00:00
Tasks: Clustering, feature selection

Baseline injury categorization is important to traumatic brain injury (TBI)
research and treatment. Current categorization is dominated by symptom-based
scores that insufficiently capture injury heterogeneity. In this work, we apply
unsupervised clustering to identify novel TBI phenotypes. Our approach uses a
generalized low-rank model (GLRM) model for feature selection in a procedure
analogous to wrapper methods. The resulting clusters reveal four novel TBI
phenotypes with distinct feature profiles and that correlate to 90-day
functional and cognitive status.
Learning Interpretable Characteristic Kernels via Decision Forests
Authors: Sambit Panda, Cencheng Shen, Joshua T. Vogelstein
Date: 2018-11-30 00:00:00
Tasks: Feature Importance, General Classification

Decision forests are widely used for classification and regression tasks. A lesser known property of tree-based methods is that one can construct a proximity matrix from the tree(s), and these proximity matrices are induced kernels. While there has been extensive research on the applications and properties of kernels, there is relatively little research on kernels induced by decision forests. We construct Kernel Mean Embedding Random Forests (KMERF), which induce kernels from random trees and/or forests using leaf-node proximity. We introduce the notion of an asymptotically characteristic kernel, and prove that KMERF kernels are asymptotically characteristic for both discrete and continuous data. Because KMERF is data-adaptive, we suspected it would outperform kernels selected a priori on finite sample data. We illustrate that KMERF nearly dominates current state-of-the-art kernel-based tests across a diverse range of high-dimensional two-sample and independence testing settings. Furthermore, our forest-based approach is interpretable, and provides feature importance metrics that readily distinguish important dimensions, unlike other high-dimensional non-parametric testing procedures. Hence, this work demonstrates the decision forest-based kernel can be more powerful and more interpretable than existing methods, flying in the face of conventional wisdom of the trade-off between the two.
Modulated Policy Hierarchies
Authors: Alexander Pashevich, Danijar Hafner, James Davidson, Rahul Sukthankar, Cordelia Schmid
Date: 2018-11-30 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Solving tasks with sparse rewards is a main challenge in reinforcement
learning. While hierarchical controllers are an intuitive approach to this
problem, current methods often require manual reward shaping, alternating
training phases, or manually defined sub tasks. We introduce modulated policy
hierarchies (MPH), that can learn end-to-end to solve tasks from sparse
rewards. To achieve this, we study different modulation signals and exploration
for hierarchical controllers. Specifically, we find that communicating via
bit-vectors is more efficient than selecting one out of multiple skills, as it
enables mixing between them. To facilitate exploration, MPH uses its different
time scales for temporally extended intrinsic motivation at each level of the
hierarchy. We evaluate MPH on the robotics tasks of pushing and sparse block
stacking, where it outperforms recent baselines.
Modeling natural language emergence with integral transform theory and reinforcement learning
Authors: Bohdan Khomtchouk, Shyam Sudhakaran
Date: 2018-11-30 00:00:00
Tasks: image-classification, Image Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Zipf's law predicts a power-law relationship between word rank and frequency
in language communication systems and has been widely reported in a variety of
natural language processing applications. However, the emergence of natural
language is often modeled as a function of bias between speaker and listener
interests, which lacks a direct way of relating information-theoretic bias to
Zipfian rank. A function of bias also serves as an unintuitive interpretation
of the communicative effort exchanged between a speaker and a listener. We
counter these shortcomings by proposing a novel integral transform and kernel
for mapping communicative bias functions to corresponding word frequency-rank
representations at any arbitrary phase transition point, resulting in a direct
way to link communicative effort (modeled by speaker/listener bias) to specific
vocabulary used (represented by word rank). We demonstrate the practical
utility of our integral transform by showing how a change from bias to rank
results in greater accuracy and performance at an image classification task for
assigning word labels to images randomly subsampled from CIFAR10. We model this
task as a reinforcement learning game between a speaker and listener and
compare the relative impact of bias and Zipfian word rank on communicative
performance (and accuracy) between the two agents.
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
Authors: Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Nießner, Leonidas Guibas
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Semantic Segmentation, Semantic Segmentation

We introduce, TextureNet, a neural network architecture designed to extract
features from high-resolution signals associated with 3D surface meshes (e.g.,
color texture maps). The key idea is to utilize a 4-rotational symmetric
(4-RoSy) field to define a domain for convolution on a surface. Though 4-RoSy
fields have several properties favorable for convolution on surfaces (low
distortion, few singularities, consistent parameterization, etc.), orientations
are ambiguous up to 4-fold rotation at any sample point. So, we introduce a new
convolutional operator invariant to the 4-RoSy ambiguity and use it in a
network to extract features from high-resolution signals on geodesic
neighborhoods of a surface. In comparison to alternatives, such as PointNet
based methods which lack a notion of orientation, the coherent structure given
by these neighborhoods results in significantly stronger features. As an
example application, we demonstrate the benefits of our architecture for 3D
semantic segmentation of textured 3D meshes. The results show that our method
outperforms all existing methods on the basis of mean IoU by a significant
margin in both geometry-only (6.4%) and RGB+Geometry (6.9-8.2%) settings.
Improving Traffic Safety Through Video Analysis in Jakarta, Indonesia
Authors: João Caldeira, Alex Fout, Aniket Kesari, Raesetje Sefala, Joseph Walsh, Katy Dupre, Muhammad Rizal Khaefi, Setiaji, George Hodge, Zakiya Aryana Pramestri, Muhammad Adib Imtiyazi
Date: 2018-11-30 00:00:00

This project presents the results of a partnership between the Data Science
for Social Good fellowship, Jakarta Smart City and Pulse Lab Jakarta to create
a video analysis pipeline for the purpose of improving traffic safety in
Jakarta. The pipeline transforms raw traffic video footage into databases that
are ready to be used for traffic analysis. By analyzing these patterns, the
city of Jakarta will better understand how human behavior and built
infrastructure contribute to traffic challenges and safety risks. The results
of this work should also be broadly applicable to smart city initiatives around
the globe as they improve urban planning and sustainability through data
science approaches.
Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks
Authors: Saúl Alonso-Monsalve, Leigh H. Whitehead
Date: 2018-11-30 00:00:00
Tasks: Generative Adversarial Network, Image Generation, model

We propose and demonstrate the use of a model-assisted generative adversarial network (GAN) to produce fake images that accurately match true images through the variation of the parameters of the model that describes the features of the images. The generator learns the model parameter values that produce fake images that best match the true images. Two case studies show excellent agreement between the generated best match parameters and the true parameters. The best match model parameter values can be used to retune the default simulation to minimize any bias when applying image recognition techniques to fake and true images. In the case of a real-world experiment, the true images are experimental data with unknown true model parameter values, and the fake images are produced by a simulation that takes the model parameters as input. The model-assisted GAN uses a convolutional neural network to emulate the simulation for all parameter values that, when trained, can be used as a conditional generator for fast fake-image production.
Anomaly Detection Models for IoT Time Series Data
Authors: Federico Giannoni, Marco Mancini, Federico Marinelli
Date: 2018-11-30 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Insitu sensors and Wireless Sensor Networks (WSNs) have become more and more
popular in the last decade, due to their potential to be used in various
applications of many different fields. As of today, WSNs are pretty much used
by any monitoring system: from those that are health care related, to those
that are used for environmental forecasting or surveillance purposes. All
applications that make use of insitu sensors, strongly rely on their correct
operation, which however, is quite difficult to guarantee. These sensors in
fact, are typically cheap and prone to malfunction. Additionally, for many
tasks (e.g. environmental forecasting), sensors are also deployed under
potentially harsh weather condition, making their breakage even more likely.
The high probability of erroneous readings or data corruption during
transmission, brings up the problem of ensuring quality of the data collected
by sensors. Since WSNs have to operate continuously and therefore generate very
large volumes of data every day, the quality control process has to be
automated, scalable and fast enough to be applicable to streaming data. The
most common approach to ensure the quality of sensors data, consists in
automated detection of erroneous readings or anomalous behaviours of sensors.
In the literature, this strategy is known as anomaly detection and can be
pursued in many different ways.
Mapping Informal Settlements in Developing Countries with Multi-resolution, Multi-spectral Data
Authors: Patrick Helber, Bradley Gram-Hansen, Indhu Varatharajan, Faiza Azam, Alejandro Coca-Castro, Veronika Kopackova, Piotr Bilinski
Date: 2018-11-30 00:00:00

Detecting and mapping informal settlements encompasses several of the United
Nations sustainable development goals. This is because informal settlements are
home to the most socially and economically vulnerable people on the planet.
Thus, understanding where these settlements are is of paramount importance to
both government and non-government organizations (NGOs), such as the United
Nations Children's Fund (UNICEF), who can use this information to deliver
effective social and economic aid. We propose two effective methods for
detecting and mapping the locations of informal settlements. One uses only
low-resolution (LR), freely available, Sentinel-2 multispectral satellite
imagery with noisy annotations, whilst the other is a deep learning approach
that uses only costly very-high-resolution (VHR) satellite imagery. To our
knowledge, we are the first to map informal settlements successfully with
low-resolution satellite imagery. We extensively evaluate and compare the
proposed methods. Please find additional material at
https://frontierdevelopmentlab.github.io/informal-settlements/.
Generating Material Maps to Map Informal Settlements
Authors: Patrick Helber, Bradley Gram-Hansen, Indhu Varatharajan, Faiza Azam, Alejandro Coca-Castro, Veronika Kopackova, Piotr Bilinski
Date: 2018-11-30 00:00:00

Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both government and non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), who can use this information to deliver effective social and economic aid. We propose a method that detects and maps the locations of informal settlements using only freely available, Sentinel-2 low-resolution satellite spectral data and socio-economic data. This is in contrast to previous studies that only use costly very-high resolution (VHR) satellite and aerial imagery. We show how we can detect informal settlements by combining both domain knowledge and machine learning techniques, to build a classifier that looks for known roofing materials used in informal settlements. Please find additional material at https://frontierdevelopmentlab.github.io/informal-settlements/.
The Graph-Based Behavior-Aware Recommendation for Interactive News
Authors: Mingyuan Ma, Sen Na, Hongyu Wang, Congzhou Chen, Jin Xu
Date: 2018-11-30 00:00:00
Tasks: Diversity, News Recommendation, Representation Learning

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user's preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user's demand on the news diversity. We have three main steps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user's interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.