pwc-archive/papers-with-abstracts huggingface dataset:
Antithetic and Monte Carlo kernel estimators for partial rankings
Authors: Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
Date: 2018-07-01 00:00:00
Tasks: Multi-Object Tracking, Object Tracking, Recommendation Systems

In the modern age, rankings data is ubiquitous and it is useful for a variety
of applications such as recommender systems, multi-object tracking and
preference learning. However, most rankings data encountered in the real world
is incomplete, which prevents the direct application of existing modelling
tools for complete rankings. Our contribution is a novel way to extend kernel
methods for complete rankings to partial rankings, via consistent Monte Carlo
estimators for Gram matrices: matrices of kernel values between pairs of
observations. We also present a novel variance reduction scheme based on an
antithetic variate construction between permutations to obtain an improved
estimator for the Mallows kernel. The corresponding antithetic kernel estimator
has lower variance and we demonstrate empirically that it has a better
performance in a variety of Machine Learning tasks. Both kernel estimators are
based on extending kernel mean embeddings to the embedding of a set of full
rankings consistent with an observed partial ranking. They form a
computationally tractable alternative to previous approaches for partial
rankings data. An overview of the existing kernels and metrics for permutations
is also provided.
cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data Processing
Authors: Konstantinos Zampogiannis, Cornelia Fermuller, Yiannis Aloimonos
Date: 2018-07-01 00:00:00
Tasks: Clustering, Point Cloud Segmentation, Spatial Reasoning

We introduce cilantro, an open-source C++ library for geometric and
general-purpose point cloud data processing. The library provides functionality
that covers low-level point cloud operations, spatial reasoning, various
methods for point cloud segmentation and generic data clustering, flexible
algorithms for robust or local geometric alignment, model fitting, as well as
powerful visualization tools. To accommodate all kinds of workflows, cilantro
is almost fully templated, and most of its generic algorithms operate in
arbitrary data dimension. At the same time, the library is easy to use and
highly expressive, promoting a clean and concise coding style. cilantro is
highly optimized, has a minimal set of external dependencies, and supports
rapid development of performant point cloud processing software in a wide
variety of contexts.
Gradient Reversal Against Discrimination
Authors: Edward Raff, Jared Sylvester
Date: 2018-07-01 00:00:00
Tasks: Attribute, Fairness

No methods currently exist for making arbitrary neural networks fair. In this
work we introduce GRAD, a new and simplified method to producing fair neural
networks that can be used for auto-encoding fair representations or directly
with predictive networks. It is easy to implement and add to existing
architectures, has only one (insensitive) hyper-parameter, and provides
improved individual and group fairness. We use the flexibility of GRAD to
demonstrate multi-attribute protection.
Model-based Exception Mining for Object-Relational Data
Authors: Fatemeh Riahi, Oliver Schulte
Date: 2018-07-01 00:00:00
Tasks: model, Object, Outlier Detection

This paper is based on a previous publication [29]. Our work extends
exception mining and outlier detection to the case of object-relational data.
Object-relational data represent a complex heterogeneous network [12], which
comprises objects of different types, links among these objects, also of
different types, and attributes of these links. This special structure
prohibits a direct vectorial data representation. We follow the
well-established Exceptional Model Mining framework, which leverages machine
learning models for exception mining: A object is exceptional to the extent
that a model learned for the object data differs from a model learned for the
general population. Exceptional objects can be viewed as outliers. We apply
state of-the-art probabilistic modelling techniques for object-relational data
that construct a graphical model (Bayesian network), which compactly represents
probabilistic associations in the data. A new metric, derived from the learned
object-relational model, quantifies the extent to which the individual
association pattern of a potential outlier deviates from that of the whole
population. The metric is based on the likelihood ratio of two parameter
vectors: One that represents the population associations, and another that
represents the individual associations. Our method is validated on synthetic
datasets and on real-world data sets about soccer matches and movies. Compared
to baseline methods, our novel transformed likelihood ratio achieved the best
detection accuracy on all datasets.
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
Authors: Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong, Richard Socher
Date: 2018-07-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Adaptation, speech-recognition, Speech Recognition, Unsupervised Domain Adaptation

Training a model to perform a task typically requires a large amount of data
from the domains in which the task will be applied. However, it is often the
case that data are abundant in some domains but scarce in others. Domain
adaptation deals with the challenge of adapting a model trained from a
data-rich source domain to perform well in a data-poor target domain. In
general, this requires learning plausible mappings between domains. CycleGAN is
a powerful framework that efficiently learns to map inputs from one domain to
another using adversarial training and a cycle-consistency constraint. However,
the conventional approach of enforcing cycle-consistency via reconstruction may
be overly restrictive in cases where one or more domains have limited training
data. In this paper, we propose an augmented cyclic adversarial learning model
that enforces the cycle-consistency constraint via an external task specific
model, which encourages the preservation of task-relevant content as opposed to
exact reconstruction. We explore digit classification in a low-resource setting
in supervised, semi and unsupervised situation, as well as high resource
unsupervised. In low-resource supervised setting, the results show that our
approach improves absolute performance by 14% and 4% when adapting SVHN to
MNIST and vice versa, respectively, which outperforms unsupervised domain
adaptation methods that require high-resource unlabeled target domain.
Moreover, using only few unsupervised target data, our approach can still
outperforms many high-resource unsupervised models. In speech domains, we
similarly adopt a speech recognition model from each domain as the task
specific model. Our approach improves absolute performance of speech
recognition by 2% for female speakers in the TIMIT dataset, where the majority
of training samples are from male voices.
New Heuristics for Parallel and Scalable Bayesian Optimization
Authors: Ran Rubin
Date: 2018-07-01 00:00:00
Tasks: Bayesian Optimization, global-optimization

Bayesian optimization has emerged as a strong candidate tool for global
optimization of functions with expensive evaluation costs. However, due to the
dynamic nature of research in Bayesian approaches, and the evolution of
computing technology, using Bayesian optimization in a parallel computing
environment remains a challenge for the non-expert. In this report, I review
the state-of-the-art in parallel and scalable Bayesian optimization methods. In
addition, I propose practical ways to avoid a few of the pitfalls of Bayesian
optimization, such as oversampling of edge parameters and over-exploitation of
high performance parameters. Finally, I provide relatively simple, heuristic
algorithms, along with their open source software implementations, that can be
immediately and easily deployed in any computing environment.
Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning
Authors: Baoxiang Wang, Tongfang Sun, Xianjun Sam Zheng
Date: 2018-07-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, reinforcement learning (RL) methods have been applied to
model gameplay with great success, achieving super-human performance in various
environments, such as Atari, Go, and Poker. However, those studies mostly focus
on winning the game and have largely ignored the rich and complex human
motivations, which are essential for understanding different players' diverse
behaviors. In this paper, we present a novel method called Multi-Motivation
Behavior Modeling (MMBM) that takes the multifaceted human motivations into
consideration and models the underlying value structure of the players using
inverse RL. Our approach does not require the access to the dynamic of the
system, making it feasible to model complex interactive environments such as
massively multiplayer online games. MMBM is tested on the World of Warcraft
Avatar History dataset, which recorded over 70,000 users' gameplay spanning
three years period. Our model reveals the significant difference of value
structures among different player groups. Using the results of motivation
modeling, we also predict and explain their diverse gameplay behaviors and
provide a quantitative assessment of how the redesign of the game environment
impacts players' behaviors.
Heuristic Framework for Multi-Scale Testing of the Multi-Manifold Hypothesis
Authors: F. Patricia Medina, Linda Ness, Melanie Weber, Karamatou Yacoubou Djima
Date: 2018-07-01 00:00:00

When analyzing empirical data, we often find that global linear models
overestimate the number of parameters required. In such cases, we may ask
whether the data lies on or near a manifold or a set of manifolds (a so-called
multi-manifold) of lower dimension than the ambient space. This question can be
phrased as a (multi-) manifold hypothesis. The identification of such intrinsic
multiscale features is a cornerstone of data analysis and representation and
has given rise to a large body of work on manifold learning. In this work, we
review key results on multi-scale data analysis and intrinsic dimension
followed by the introduction of a heuristic, multiscale framework for testing
the multi-manifold hypothesis. Our method implements a hypothesis test on a set
of spline-interpolated manifolds constructed from variance-based intrinsic
dimensions. The workflow is suitable for empirical data analysis as we
demonstrate on two use cases.
Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification
Authors: Xinhan Di, Pengqian Yu, Meng Tian
Date: 2018-07-01 00:00:00
Tasks: General Classification

It has been demonstrated that deep neural networks are prone to noisy
examples particular adversarial samples during inference process. The gap
between robust deep learning systems in real world applications and vulnerable
neural networks is still large. Current adversarial training strategies improve
the robustness against adversarial samples. However, these methods lead to
accuracy reduction when the input examples are clean thus hinders the
practicability. In this paper, we investigate an approach that protects the
neural network classification from the adversarial samples and improves its
accuracy when the input examples are clean. We demonstrate the versatility and
effectiveness of our proposed approach on a variety of different networks and
datasets.
Data-driven satisficing measure and ranking
Authors: Wenjie Huang
Date: 2018-07-01 00:00:00
Tasks: Stochastic Optimization

We propose an computational framework for real-time risk assessment and
prioritizing for random outcomes without prior information on probability
distributions. The basic model is built based on satisficing measure (SM) which
yields a single index for risk comparison. Since SM is a dual representation
for a family of risk measures, we consider problems constrained by general
convex risk measures and specifically by Conditional value-at-risk. Starting
from offline optimization, we apply sample average approximation technique and
argue the convergence rate and validation of optimal solutions. In online
stochastic optimization case, we develop primal-dual stochastic approximation
algorithms respectively for general risk constrained problems, and derive their
regret bounds. For both offline and online cases, we illustrate the
relationship between risk ranking accuracy with sample size (or iterations).
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
Authors: Yanru Qu, Bohui Fang, Wei-Nan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, Xiuqiang He
Date: 2018-07-01 00:00:00
Tasks: Click-Through Rate Prediction, Feature Engineering, Information Retrieval, Prediction, Recommendation Systems, Retrieval

User response prediction is a crucial component for personalized information
retrieval and filtering scenarios, such as recommender system and web search.
The data in user response prediction is mostly in a multi-field categorical
format and transformed into sparse representations via one-hot encoding. Due to
the sparsity problems in representation and optimization, most research focuses
on feature engineering and shallow modeling. Recently, deep neural networks
have attracted research attention on such a problem for their high capacity and
end-to-end training scheme. In this paper, we study user response prediction in
the scenario of click prediction. We first analyze a coupled gradient issue in
latent vector-based models and propose kernel product to learn field-aware
feature interactions. Then we discuss an insensitive gradient issue in
DNN-based models and propose Product-based Neural Network (PNN) which adopts a
feature extractor to explore feature interactions. Generalizing the kernel
product to a net-in-net architecture, we further propose Product-network In
Network (PIN) which can generalize previous models. Extensive experiments on 4
industrial datasets and 1 contest dataset demonstrate that our models
consistently outperform 8 baselines on both AUC and log loss. Besides, PIN
makes great CTR improvement (relatively 34.67%) in online A/B test.
Modeling, comprehending and summarizing textual content by graphs
Authors: Vinicius Woloszyn, Guilherme Medeiros Machado, Leandro Krug Wives, José Palazzo Moreira de Oliveira
Date: 2018-07-01 00:00:00
Tasks: Text Summarization

Automatic Text Summarization strategies have been successfully employed to
digest text collections and extract its essential content. Usually, summaries
are generated using textual corpora that belongs to the same domain area where
the summary will be used. Nonetheless, there are special cases where it is not
found enough textual sources, and one possible alternative is to generate a
summary from a different domain. One manner to summarize texts consists of
using a graph model. This model allows giving more importance to words
corresponding to the main concepts from the target domain found in the
summarized text. This gives the reader an overview of the main text concepts as
well as their relationships. However, this kind of summarization presents a
significant number of repeated terms when compared to human-generated
summaries. In this paper, we present an approach to produce graph-model
extractive summaries of texts, meeting the target domain exigences and treating
the terms repetition problem. To evaluate the proposition, we performed a
series of experiments showing that the proposed approach statistically improves
the performance of a model based on Graph Centrality, achieving better
coverage, accuracy, and recall.
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks
Authors: Julian Faraone, Nicholas Fraser, Michaela Blott, Philip H. W. Leong
Date: 2018-07-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Quantization

Inference for state-of-the-art deep neural networks is computationally
expensive, making them difficult to deploy on constrained hardware
environments. An efficient way to reduce this complexity is to quantize the
weight parameters and/or activations during training by approximating their
distributions with a limited entry codebook. For very low-precisions, such as
binary or ternary networks with 1-8-bit activations, the information loss from
quantization leads to significant accuracy degradation due to large gradient
mismatches between the forward and backward functions. In this paper, we
introduce a quantization method to reduce this loss by learning a symmetric
codebook for particular weight subgroups. These subgroups are determined based
on their locality in the weight matrix, such that the hardware simplicity of
the low-precision representations is preserved. Empirically, we show that
symmetric quantization can substantially improve accuracy for networks with
extremely low-precision weights and activations. We also demonstrate that this
representation imposes minimal or no hardware implications to more
coarse-grained approaches. Source code is available at
https://www.github.com/julianfaraone/SYQ.
Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control
Authors: JunPing Wang, Wensheng Zhang, Ian Thomas, ShiHui Duan, YouKang Shi
Date: 2018-07-01 00:00:00
Tasks: Decision Making, Sequential Decision Making

Generating sequential decision process from huge amounts of measured process
data is a future research direction for collaborative factory automation,
making full use of those online or offline process data to directly design
flexible make decisions policy, and evaluate performance. The key challenges
for the sequential decision process is to online generate sequential
decision-making policy directly, and transferring knowledge across tasks
domain. Most multi-task policy generating algorithms often suffer from
insufficient generating cross-task sharing structure at discrete-time nonlinear
systems with applications. This paper proposes the multi-task generative
adversarial nets with shared memory for cross-domain coordination control,
which can generate sequential decision policy directly from raw sensory input
of all of tasks, and online evaluate performance of system actions in
discrete-time nonlinear systems. Experiments have been undertaken using a
professional flexible manufacturing testbed deployed within a smart factory of
Weichai Power in China. Results on three groups of discrete-time nonlinear
control tasks show that our proposed model can availably improve the
performance of task with the help of other related tasks.
Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions
Authors: Weinan E, Qingcan Wang
Date: 2018-07-01 00:00:00

We prove that for analytic functions in low dimension, the convergence rate
of the deep neural network approximation is exponential.
Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages
Authors: Manuel Mager, Elisabeth Mager, Alfonso Medina-Urrea, Ivan Meza, Katharina Kann
Date: 2018-07-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Translation

Machine translation from polysynthetic to fusional languages is a challenging
task, which gets further complicated by the limited amount of parallel text
available. Thus, translation performance is far from the state of the art for
high-resource and more intensively studied language pairs. To shed light on the
phenomena which hamper automatic translation to and from polysynthetic
languages, we study translations from three low-resource, polysynthetic
languages (Nahuatl, Wixarika and Yorem Nokki) into Spanish and vice versa.
Doing so, we find that in a morpheme-to-morpheme alignment an important amount
of information contained in polysynthetic morphemes has no Spanish counterpart,
and its translation is often omitted. We further conduct a qualitative analysis
and, thus, identify morpheme types that are commonly hard to align or ignored
in the translation process.
Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification
Authors: Benteng Ma, Yong Xia
Date: 2018-07-01 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification

Recent years have witnessed the breakthrough success of deep convolutional
neural networks (DCNNs) in image classification and other vision applications.
Although freeing users from the troublesome handcrafted feature extraction by
providing a uniform feature extraction-classification framework, DCNNs still
require a handcrafted design of their architectures. In this paper, we propose
the genetic DCNN designer, an autonomous learning algorithm can generate a DCNN
architecture automatically based on the data available for a specific image
classification problem. We first partition a DCNN into multiple stacked meta
convolutional blocks and fully connected blocks, each containing the operations
of convolution, pooling, fully connection, batch normalization, activation and
drop out, and thus convert the architecture into an integer vector. Then, we
use refined evolutionary operations, including selection, mutation and
crossover to evolve a population of DCNN architectures. Our results on the
MNIST, Fashion-MNIST, EMNISTDigit, EMNIST-Letter, CIFAR10 and CIFAR100 datasets
suggest that the proposed genetic DCNN designer is able to produce
automatically DCNN architectures, whose performance is comparable to, if not
better than, that of stateof- the-art DCNN models
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
Authors: Fangchang Ma, Guilherme Venturelli Cavalheiro, Sertac Karaman
Date: 2018-07-01 00:00:00
Tasks: Autonomous Driving, Depth Completion

Depth completion, the technique of estimating a dense depth image from sparse
depth measurements, has a variety of applications in robotics and autonomous
driving. However, depth completion faces 3 main challenges: the irregularly
spaced pattern in the sparse depth input, the difficulty in handling multiple
sensor modalities (when color images are available), as well as the lack of
dense, pixel-level ground truth depth labels. In this work, we address all
these challenges. Specifically, we develop a deep regression model to learn a
direct mapping from sparse depth (and color images) to dense depth. We also
propose a self-supervised training framework that requires only sequences of
color and sparse depth images, without the need for dense depth labels. Our
experiments demonstrate that our network, when trained with semi-dense
annotations, attains state-of-the- art accuracy and is the winning approach on
the KITTI depth completion benchmark at the time of submission. Furthermore,
the self-supervised framework outperforms a number of existing solutions
trained with semi- dense annotations.
Photorealistic Style Transfer for Videos
Authors: Michael Honke, Rahul Iyer, Dishant Mittal
Date: 2018-07-01 00:00:00
Tasks: Deep Learning, Style Transfer

Photorealistic style transfer is a technique which transfers colour from one
reference domain to another domain by using deep learning and optimization
techniques. Here, we present a technique which we use to transfer style and
colour from a reference image to a video.