pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Community Structure in Layered Neural Networks
Authors: Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino
Date: 2018-04-13 00:00:00

A layered neural network is now one of the most common choices for the
prediction of high-dimensional practical data sets, where the relationship
between input and output data is complex and cannot be represented well by
simple conventional models. Its effectiveness is shown in various tasks,
however, the lack of interpretability of the trained result by a layered neural
network has limited its application area.
  In our previous studies, we proposed methods for extracting a simplified
global structure of a trained layered neural network by classifying the units
into communities according to their connection patterns with adjacent layers.
These methods provided us with knowledge about the strength of the relationship
between communities from the existence of bundled connections, which are
determined by threshold processing of the connection ratio between pairs of
communities.
  However, it has been difficult to understand the role of each community
quantitatively by observing the modular structure. We could only know to which
sets of the input and output dimensions each community was mainly connected, by
tracing the bundled connections from the community to the input and output
layers. Another problem is that the finally obtained modular structure is
changed greatly depending on the setting of the threshold hyperparameter used
for determining bundled connections.
  In this paper, we propose a new method for interpreting quantitatively the
role of each community in inference, by defining the effect of each input
dimension on a community, and the effect of a community on each output
dimension. We show experimentally that our proposed method can reveal the role
of each part of a layered neural network by applying the neural networks to
three types of data sets, extracting communities from the trained network, and
applying the proposed method to the community structure.
RIPEx: Extracting malicious IP addresses from security forums using cross-forum learning
Authors: Joobin Gharibshah, Evangelos E. Papalexakis, Michalis Faloutsos
Date: 2018-04-13 00:00:00
Tasks: Transfer Learning

Is it possible to extract malicious IP addresses reported in security forums
in an automatic way? This is the question at the heart of our work. We focus on
security forums, where security professionals and hackers share knowledge and
information, and often report misbehaving IP addresses. So far, there have only
been a few efforts to extract information from such security forums. We propose
RIPEx, a systematic approach to identify and label IP addresses in security
forums by utilizing a cross-forum learning method. In more detail, the
challenge is twofold: (a) identifying IP addresses from other numerical
entities, such as software version numbers, and (b) classifying the IP address
as benign or malicious. We propose an integrated solution that tackles both
these problems. A novelty of our approach is that it does not require training
data for each new forum. Our approach does knowledge transfer across forums: we
use a classifier from our source forums to identify seed information for
training a classifier on the target forum. We evaluate our method using data
collected from five security forums with a total of 31K users and 542K posts.
First, RIPEx can distinguish IP address from other numeric expressions with 95%
precision and above 93% recall on average. Second, RIPEx identifies malicious
IP addresses with an average precision of 88% and over 78% recall, using our
cross-forum learning. Our work is a first step towards harnessing the wealth of
useful information that can be found in security forums.
MOVI: A Model-Free Approach to Dynamic Fleet Management
Authors: Takuma Oda, Carlee Joe-Wong
Date: 2018-04-13 00:00:00
Tasks: Management

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies,
can reduce passengers' waiting times by proactively dispatching vehicles to
locations where pickup requests are anticipated in the future. Yet it is
unclear how to best do this: optimal dispatching requires optimizing over
several sources of uncertainty, including vehicles' travel times to their
dispatched locations, as well as coordinating between vehicles so that they do
not attempt to pick up the same passenger. While prior works have developed
models for this uncertainty and used them to optimize dispatch policies, in
this work we introduce a model-free approach. Specifically, we propose MOVI, a
Deep Q-network (DQN)-based framework that directly learns the optimal vehicle
dispatch policy. Since DQNs scale poorly with a large number of possible
dispatches, we streamline our DQN training and suppose that each individual
vehicle independently learns its own optimal policy, ensuring scalability at
the cost of less coordination between vehicles. We then formulate a centralized
receding-horizon control (RHC) policy to compare with our DQN policies. To
compare these policies, we design and build MOVI as a large-scale realistic
simulator based on 15 million taxi trip records that simulates policy-agnostic
responses to dispatch decisions. We show that the DQN dispatch policy reduces
the number of unserviced requests by 76% compared to without dispatch and 20%
compared to the RHC approach, emphasizing the benefits of a model-free approach
and suggesting that there is limited value to coordinating vehicle actions.
This finding may help to explain the success of ridesharing platforms, for
which drivers make individual decisions.
Implicit Argument Prediction with Event Knowledge
Authors: Pengxiang Cheng, Katrin Erk
Date: 2018-02-20 00:00:00
Proceeding: NAACL 2018 6
Tasks: Prediction

Implicit arguments are not syntactically connected to their predicates, and
are therefore hard to extract. Previous work has used models with large numbers
of features, evaluated on very small datasets. We propose to train models for
implicit argument prediction on a simple cloze task, for which data can be
generated automatically at scale. This allows us to use a neural model, which
draws on narrative coherence and entity salience for predictions. We show that
our model has superior performance on both synthetic and natural data.
Frustum PointNets for 3D Object Detection from RGB-D Data
Authors: Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas
Date: 2017-11-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Object Detection, Object, Object Detection, Object Detection In Indoor Scenes, Object Localization, Region Proposal

In this work, we study 3D object detection from RGB-D data in both indoor and
outdoor scenes. While previous methods focus on images or 3D voxels, often
obscuring natural 3D patterns and invariances of 3D data, we directly operate
on raw point clouds by popping up RGB-D scans. However, a key challenge of this
approach is how to efficiently localize objects in point clouds of large-scale
scenes (region proposal). Instead of solely relying on 3D proposals, our method
leverages both mature 2D object detectors and advanced 3D deep learning for
object localization, achieving efficiency as well as high recall for even small
objects. Benefited from learning directly in raw point clouds, our method is
also able to precisely estimate 3D bounding boxes even under strong occlusion
or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection
benchmarks, our method outperforms the state of the art by remarkable margins
while having real-time capability.
Predicting Good Configurations for GitHub and Stack Overflow Topic Models
Authors: Christoph Treude, Markus Wagner
Date: 2018-04-13 00:00:00
Tasks: Feature Importance, Topic Models

Software repositories contain large amounts of textual data, ranging from
source code comments and issue descriptions to questions, answers, and comments
on Stack Overflow. To make sense of this textual data, topic modelling is
frequently used as a text-mining tool for the discovery of hidden semantic
structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used
topic model that aims to explain the structure of a corpus by grouping texts.
LDA requires multiple parameters to work well, and there are only rough and
sometimes conflicting guidelines available on how these parameters should be
set. In this paper, we contribute (i) a broad study of parameters to arrive at
good local optima for GitHub and Stack Overflow text corpora, (ii) an
a-posteriori characterisation of text corpora related to eight programming
languages, and (iii) an analysis of corpus feature importance via per-corpus
LDA configuration. We find that (1) popular rules of thumb for topic modelling
parameter configuration are not applicable to the corpora used in our
experiments, (2) corpora sampled from GitHub and Stack Overflow have different
characteristics and require different configurations to achieve good model fit,
and (3) we can predict good configurations for unseen corpora reliably. These
findings support researchers and practitioners in efficiently determining
suitable configurations for topic modelling when analysing textual data
contained in software repositories.
SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis
Authors: Wengling Chen, James Hays
Date: 2018-01-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Data Augmentation, Generative Adversarial Network, Image Generation, Retrieval, Translation

Synthesizing realistic images from human drawn sketches is a challenging
problem in computer graphics and vision. Existing approaches either need exact
edge maps, or rely on retrieval of existing photographs. In this work, we
propose a novel Generative Adversarial Network (GAN) approach that synthesizes
plausible images from 50 categories including motorcycles, horses and couches.
We demonstrate a data augmentation technique for sketches which is fully
automatic, and we show that the augmented data is helpful to our task. We
introduce a new network building block suitable for both the generator and
discriminator which improves the information flow by injecting the input image
at multiple scales. Compared to state-of-the-art image translation methods, our
approach generates more realistic images and achieves significantly higher
Inception Scores.
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
Authors: Matthew Tesfaldet, Marcus A. Brubaker, Konstantinos G. Derpanis
Date: 2017-06-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object Recognition, Optical Flow Estimation, Style Transfer, Texture Synthesis, Vocal Bursts Valence Prediction

We introduce a two-stream model for dynamic texture synthesis. Our model is
based on pre-trained convolutional networks (ConvNets) that target two
independent tasks: (i) object recognition, and (ii) optical flow prediction.
Given an input dynamic texture, statistics of filter responses from the object
recognition ConvNet encapsulate the per-frame appearance of the input texture,
while statistics of filter responses from the optical flow ConvNet model its
dynamics. To generate a novel texture, a randomly initialized input sequence is
optimized to match the feature statistics from each stream of an example
texture. Inspired by recent work on image style transfer and enabled by the
two-stream model, we also apply the synthesis approach to combine the texture
appearance from one texture with the dynamics of another to generate entirely
novel dynamic textures. We show that our approach generates novel, high quality
samples that match both the framewise appearance and temporal evolution of
input texture. Finally, we quantitatively evaluate our texture synthesis
approach with a thorough user study.
Multimodal Unsupervised Image-to-Image Translation
Authors: Xun Huang, Ming-Yu Liu, Serge Belongie, Jan Kautz
Date: 2018-04-12 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Translation, Unsupervised Image-To-Image Translation

Unsupervised image-to-image translation is an important and challenging
problem in computer vision. Given an image in the source domain, the goal is to
learn the conditional distribution of corresponding images in the target
domain, without seeing any pairs of corresponding images. While this
conditional distribution is inherently multimodal, existing approaches make an
overly simplified assumption, modeling it as a deterministic one-to-one
mapping. As a result, they fail to generate diverse outputs from a given source
domain image. To address this limitation, we propose a Multimodal Unsupervised
Image-to-image Translation (MUNIT) framework. We assume that the image
representation can be decomposed into a content code that is domain-invariant,
and a style code that captures domain-specific properties. To translate an
image to another domain, we recombine its content code with a random style code
sampled from the style space of the target domain. We analyze the proposed
framework and establish several theoretical results. Extensive experiments with
comparisons to the state-of-the-art approaches further demonstrates the
advantage of the proposed framework. Moreover, our framework allows users to
control the style of translation outputs by providing an example style image.
Code and pretrained models are available at https://github.com/nvlabs/MUNIT
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Authors: Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
Date: 2017-11-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Fairness

The most prevalent notions of fairness in machine learning are statistical
definitions: they fix a small collection of pre-defined groups, and then ask
for parity of some statistic of the classifier across these groups. Constraints
of this form are susceptible to intentional or inadvertent "fairness
gerrymandering", in which a classifier appears to be fair on each individual
group, but badly violates the fairness constraint on one or more structured
subgroups defined over the protected attributes. We propose instead to demand
statistical notions of fairness across exponentially (or infinitely) many
subgroups, defined by a structured class of functions over the protected
attributes. This interpolates between statistical definitions of fairness and
recently proposed individual notions of fairness, but raises several
computational challenges. It is no longer clear how to audit a fixed classifier
to see if it satisfies such a strong definition of fairness. We prove that the
computational problem of auditing subgroup fairness for both equality of false
positive rates and statistical parity is equivalent to the problem of weak
agnostic learning, which means it is computationally hard in the worst case,
even for simple structured subclasses.
  We then derive two algorithms that provably converge to the best fair
classifier, given access to oracles which can solve the agnostic learning
problem. The algorithms are based on a formulation of subgroup fairness as a
two-player zero-sum game between a Learner and an Auditor. Our first algorithm
provably converges in a polynomial number of steps. Our second algorithm enjoys
only provably asymptotic convergence, but has the merit of simplicity and
faster per-step computation. We implement the simpler algorithm using linear
regression as a heuristic oracle, and show that we can effectively both audit
and learn fair classifiers on real datasets.
An Universal Image Attractiveness Ranking Framework
Authors: Ning Ma, Alexey Volkov, Aleksandr Livshits, Pawel Pietrusinski, Houdong Hu, Mark Bolin
Date: 2018-04-12 00:00:00
Tasks: Attribute

We propose a new framework to rank image attractiveness using a novel
pairwise deep network trained with a large set of side-by-side multi-labeled
image pairs from a web image index. The judges only provide relative ranking
between two images without the need to directly assign an absolute score, or
rate any predefined image attribute, thus making the rating more intuitive and
accurate. We investigate a deep attractiveness rank net (DARN), a combination
of deep convolutional neural network and rank net, to directly learn an
attractiveness score mean and variance for each image and the underlying
criteria the judges use to label each pair. The extension of this model
(DARN-V2) is able to adapt to individual judge's personal preference. We also
show the attractiveness of search results are significantly improved by using
this attractiveness information in a real commercial search engine. We evaluate
our model against other state-of-the-art models on our side-by-side web test
data and another public aesthetic data set. With much less judgments (1M vs
50M), our model outperforms on side-by-side labeled data, and is comparable on
data labeled by absolute score.
Network-based protein structural classification
Authors: Khalique Newaz, Mahboobeh Ghalehnovi, Arash Rahnama, Panos J. Antsaklis, Tijana Milenkovic
Date: 2018-04-12 00:00:00
Tasks: Classification, General Classification

Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct 3-dimensional (3D) structure-based protein features. In contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of ~9,400 CATH and ~12,800 SCOP protein domains (spanning 36 PSN sets), our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running time.
BlockDrop: Dynamic Inference Paths in Residual Networks
Authors: Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris
Date: 2017-11-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Reinforcement Learning

Very deep convolutional neural networks offer excellent recognition results,
yet their computational expense limits their impact for many real-world
applications. We introduce BlockDrop, an approach that learns to dynamically
choose which layers of a deep network to execute during inference so as to best
reduce total computation without degrading prediction accuracy. Exploiting the
robustness of Residual Networks (ResNets) to layer dropping, our framework
selects on-the-fly which residual blocks to evaluate for a given novel image.
In particular, given a pretrained ResNet, we train a policy network in an
associative reinforcement learning setting for the dual reward of utilizing a
minimal number of blocks while preserving recognition accuracy. We conduct
extensive experiments on CIFAR and ImageNet. The results provide strong
quantitative and qualitative evidence that these learned policies not only
accelerate inference but also encode meaningful visual information. Built upon
a ResNet-101 model, our method achieves a speedup of 20\% on average, going as
high as 36\% for some images, while maintaining the same 76.4\% top-1 accuracy
on ImageNet.
Balanced Random Survival Forests for Extremely Unbalanced, Right Censored Data
Authors: Kahkashan Afrin, Gurudev Illangovan, Sanjay S. Srivatsa, Satish T. S. Bukkapatnam
Date: 2018-03-24 00:00:00
Tasks: Benchmarking, Prediction

Accuracies of survival models for life expectancy prediction as well as
critical-care applications are significantly compromised due to the sparsity of
samples and extreme imbalance between the survival (usually, the majority) and
mortality class sizes. While a recent random survival forest (RSF) model
overcomes the limitations of the proportional hazard assumption, an imbalance
in the data results in an underestimation (overestimation) of the hazard of the
mortality (survival) classes. A balanced random survival forests (BRSF) model,
based on training the RSF model with data generated from a synthetic minority
sampling scheme is presented to address this gap. Theoretical results on the
effect of balancing on prediction accuracies in BRSF are reported. Benchmarking
studies were conducted using five datasets with different levels of class
imbalance from public repositories and an imbalanced dataset of 267 acute
cardiac patients, collected at the Heart, Artery, and Vein Center of Fresno,
CA. Investigations suggest that BRSF provides an improved discriminatory
strength between the survival and the mortality classes. It outperformed both
optimized Cox (without and with balancing) and RSF with an average reduction of
55\% in the prediction error over the next best alternative.
Efficient Model Identification for Tensegrity Locomotion
Authors: Shaojun Zhu, David Surovik, Kostas E. Bekris, Abdeslam Boularias
Date: 2018-04-12 00:00:00
Tasks: Bayesian Optimization, model

This paper aims to identify in a practical manner unknown physical
parameters, such as mechanical models of actuated robot links, which are
critical in dynamical robotic tasks. Key features include the use of an
off-the-shelf physics engine and the Bayesian optimization framework. The task
being considered is locomotion with a high-dimensional, compliant Tensegrity
robot. A key insight, in this case, is the need to project the model
identification challenge into an appropriate lower dimensional space for
efficiency. Comparisons with alternatives indicate that the proposed method can
identify the parameters more accurately within the given time budget, which
also results in more precise locomotion control.
Neural Sketch Learning for Conditional Program Generation
Authors: Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
Date: 2017-03-16 00:00:00
Proceeding: ICLR 2018 1

We study the problem of generating source code in a strongly typed, Java-like
programming language, given a label (for example a set of API calls or types)
carrying a small amount of information about the code that is desired. The
generated programs are expected to respect a "realistic" relationship between
programs and labels, as exemplified by a corpus of labeled programs available
during training.
  Two challenges in such conditional program generation are that the generated
programs must satisfy a rich set of syntactic and semantic constraints, and
that source code contains many low-level features that impede learning. We
address these problems by training a neural generator not on code but on
program sketches, or models of program syntax that abstract out names and
operations that do not generalize across programs. During generation, we infer
a posterior distribution over sketches, then concretize samples from this
distribution into type-safe programs using combinatorial techniques. We
implement our ideas in a system for generating API-heavy Java code, and show
that it can often predict the entire body of a method given just a few API
calls or data types that appear in the method.