pwc-archive/papers-with-abstracts huggingface dataset:
Forecasting Player Behavioral Data and Simulating in-Game Events
Authors: Anna Guitart, Pei Pei Chen, Paul Bertens, África Periáñez
Date: 2017-10-05 00:00:00
Tasks: Time Series, Time Series Analysis

Understanding player behavior is fundamental in game data science. Video
games evolve as players interact with the game, so being able to foresee player
experience would help to ensure a successful game development. In particular,
game developers need to evaluate beforehand the impact of in-game events.
Simulation optimization of these events is crucial to increase player
engagement and maximize monetization. We present an experimental analysis of
several methods to forecast game-related variables, with two main aims: to
obtain accurate predictions of in-app purchases and playtime in an operational
production environment, and to perform simulations of in-game events in order
to maximize sales and playtime. Our ultimate purpose is to take a step towards
the data-driven development of games. The results suggest that, even though the
performance of traditional approaches such as ARIMA is still better, the
outcomes of state-of-the-art techniques like deep learning are promising. Deep
learning comes up as a well-suited general model that could be used to forecast
a variety of time series with different dynamic behaviors.
Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics
Authors: Esben Jannik Bjerrum, Mads Glahder, Thomas Skov
Date: 2017-10-05 00:00:00
Tasks: Data Augmentation, Deep Learning

Deep learning methods are used on spectroscopic data to predict drug content
in tablets from near infrared (NIR) spectra. Using convolutional neural
networks (CNNs), features are ex- tracted from the spectroscopic data. Extended
multiplicative scatter correction (EMSC) and a novel spectral data augmentation
method are benchmarked as preprocessing steps. The learned models perform
better or on par with hypothetical optimal partial least squares (PLS) models
for all combinations of preprocessing. Data augmentation with subsequent EMSC
in combination gave the best results. The deep learning model CNNs also
outperform the PLS models in an extrapolation chal- lenge created using data
from a second instrument and from an analyte concentration not covered by the
training data. Qualitative investigations of the CNNs kernel activations show
their resemblance to wellknown data processing methods such as smoothing,
slope/derivative, thresholds and spectral region selection.
B-CNN: Branch Convolutional Neural Network for Hierarchical Classification
Authors: Xinqi Zhu, Michael Bain
Date: 2017-09-28 00:00:00
Tasks: Classification, General Classification

Convolutional Neural Network (CNN) image classifiers are traditionally
designed to have sequential convolutional layers with a single output layer.
This is based on the assumption that all target classes should be treated
equally and exclusively. However, some classes can be more difficult to
distinguish than others, and classes may be organized in a hierarchy of
categories. At the same time, a CNN is designed to learn internal
representations that abstract from the input data based on its hierarchical
layered structure. So it is natural to ask if an inverse of this idea can be
applied to learn a model that can predict over a classification hierarchy using
multiple output layers in decreasing order of class abstraction. In this paper,
we introduce a variant of the traditional CNN model named the Branch
Convolutional Neural Network (B-CNN). A B-CNN model outputs multiple
predictions ordered from coarse to fine along the concatenated convolutional
layers corresponding to the hierarchical structure of the target classes, which
can be regarded as a form of prior knowledge on the output. To learn with
B-CNNs a novel training strategy, named the Branch Training strategy
(BT-strategy), is introduced which balances the strictness of the prior with
the freedom to adjust parameters on the output layers to minimize the loss. In
this way we show that CNN based models can be forced to learn successively
coarse to fine concepts in the internal layers at the output stage, and that
hierarchical prior knowledge can be adopted to boost CNN models' classification
performance. Our models are evaluated to show that the B-CNN extensions improve
over the corresponding baseline CNN on the benchmark datasets MNIST, CIFAR-10
and CIFAR-100.
Real-Time Illegal Parking Detection System Based on Deep Learning
Authors: Xuemei Xie, Chenye Wang, Shu Chen, Guangming Shi, Zhifu Zhao
Date: 2017-10-05 00:00:00
Tasks: Deep Learning

The increasing illegal parking has become more and more serious. Nowadays the
methods of detecting illegally parked vehicles are based on background
segmentation. However, this method is weakly robust and sensitive to
environment. Benefitting from deep learning, this paper proposes a novel
illegal vehicle parking detection system. Illegal vehicles captured by camera
are firstly located and classified by the famous Single Shot MultiBox Detector
(SSD) algorithm. To improve the performance, we propose to optimize SSD by
adjusting the aspect ratio of default box to accommodate with our dataset
better. After that, a tracking and analysis of movement is adopted to judge the
illegal vehicles in the region of interest (ROI). Experiments show that the
system can achieve a 99% accuracy and real-time (25FPS) detection with strong
robustness in complex environments.
Single-pixel imaging with Morlet wavelet correlated random patterns
Authors: Krzysztof M. Czajkowski, Anna Pastuszczak, Rafał Kotyński
Date: 2017-09-22 00:00:00

Single-pixel imaging is an indirect imaging technique which utilizes
simplified optical hardware and advanced computational methods. It offers novel
solutions for hyper-spectral imaging, polarimetric imaging, three-dimensional
imaging, holographic imaging, optical encryption and imaging through scattering
media. The main limitations for its use come from relatively high measurement
and reconstruction times. In this paper we propose to reduce the required
signal acquisition time by using a novel sampling scheme based on a random
selection of Morlet wavelets convolved with white noise. While such functions
exhibit random properties, they are locally determined by Morlet wavelet
parameters. The proposed method is equivalent to random sampling of the
properly selected part of the feature space, which maps the measured images
accurately both in the spatial and spatial frequency domains. We compare both
numerically and experimentally the image quality obtained with our sampling
protocol against widely-used sampling with Walsh-Hadamard or noiselet
functions. The results show considerable improvement over the former methods,
enabling single-pixel imaging at low compression rates on the order of a few
percent.
Multi-Level Discovery of Deep Options
Authors: Roy Fox, Sanjay Krishnan, Ion Stoica, Ken Goldberg
Date: 2017-03-24 00:00:00
Tasks: Reinforcement Learning

Augmenting an agent's control with useful higher-level behaviors called
options can greatly reduce the sample complexity of reinforcement learning, but
manually designing options is infeasible in high-dimensional and abstract state
spaces. While recent work has proposed several techniques for automated option
discovery, they do not scale to multi-level hierarchies and to expressive
representations such as deep networks. We present Discovery of Deep Options
(DDO), a policy-gradient algorithm that discovers parametrized options from a
set of demonstration trajectories, and can be used recursively to discover
additional levels of the hierarchy. The scalability of our approach to
multi-level hierarchies stems from the decoupling of low-level option discovery
from high-level meta-control policy learning, facilitated by
under-parametrization of the high level. We demonstrate that using the
discovered options to augment the action space of Deep Q-Network agents can
accelerate learning by guiding exploration in tasks where random actions are
unlikely to reach valuable states. We show that DDO is effective in adding
options that accelerate learning in 4 out of 5 Atari RAM environments chosen in
our experiments. We also show that DDO can discover structure in robot-assisted
surgical videos and kinematics that match expert annotation with 72% accuracy.
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Authors: Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-yan Yeung, Wai-kin Wong, Wang-chun Woo
Date: 2017-06-12 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Optical Flow Estimation, Video Prediction

With the goal of making high-resolution forecasts of regional rainfall,
precipitation nowcasting has become an important and fundamental technology
underlying various public services ranging from rainstorm warnings to flight
safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to
outperform traditional optical flow based methods for precipitation nowcasting,
suggesting that deep learning models have a huge potential for solving the
problem. However, the convolutional recurrence structure in ConvLSTM-based
models is location-invariant while natural motion and transformation (e.g.,
rotation) are location-variant in general. Furthermore, since
deep-learning-based precipitation nowcasting is a newly emerging area, clear
evaluation protocols have not yet been established. To address these problems,
we propose both a new model and a benchmark for precipitation nowcasting.
Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU)
model that can actively learn the location-variant structure for recurrent
connections. Besides, we provide a benchmark that includes a real-world
large-scale dataset from the Hong Kong Observatory, a new training loss, and a
comprehensive evaluation protocol to facilitate future research and gauge the
state of the art.
Revisiting Spectral Graph Clustering with Generative Community Models
Authors: Pin-Yu Chen, Lingfei Wu
Date: 2017-09-14 00:00:00
Tasks: Clustering, Community Detection, Graph Clustering, Spectral Graph Clustering

The methodology of community detection can be divided into two principles:
imposing a network model on a given graph, or optimizing a designed objective
function. The former provides guarantees on theoretical detectability but falls
short when the graph is inconsistent with the underlying model. The latter is
model-free but fails to provide quality assurance for the detected communities.
In this paper, we propose a novel unified framework to combine the advantages
of these two principles. The presented method, SGC-GEN, not only considers the
detection error caused by the corresponding model mismatch to a given graph,
but also yields a theoretical guarantee on community detectability by analyzing
Spectral Graph Clustering (SGC) under GENerative community models (GCMs).
SGC-GEN incorporates the predictability on correct community detection with a
measure of community fitness to GCMs. It resembles the formulation of
supervised learning problems by enabling various community detection loss
functions and model mismatch metrics. We further establish a theoretical
condition for correct community detection using the normalized graph Laplacian
matrix under a GCM, which provides a novel data-driven loss function for
SGC-GEN. In addition, we present an effective algorithm to implement SGC-GEN,
and show that the computational complexity of SGC-GEN is comparable to the
baseline methods. Our experiments on 18 real-world datasets demonstrate that
SGC-GEN possesses superior and robust performance compared to 6 baseline
methods under 7 representative clustering metrics.
Learning Scalable Deep Kernels with Recurrent Structure
Authors: Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing
Date: 2016-10-27 00:00:00
Tasks: Autonomous Driving, Gaussian Processes, Smart Grid Prediction

Many applications in speech, robotics, finance, and biology deal with
sequential data, where ordering matters and recurrent structures are common.
However, this structure cannot be easily captured by standard kernel functions.
To model such structure, we propose expressive closed-form kernel functions for
Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the
inductive biases of long short-term memory (LSTM) recurrent networks, while
retaining the non-parametric probabilistic advantages of Gaussian processes. We
learn the properties of the proposed kernels by optimizing the Gaussian process
marginal likelihood using a new provably convergent semi-stochastic gradient
procedure and exploit the structure of these kernels for scalable training and
prediction. This approach provides a practical representation for Bayesian
LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and
thoroughly investigate a consequential autonomous driving application, where
the predictive uncertainties provided by GP-LSTM are uniquely valuable.
Automatic Taxonomy Generation - A Use-Case in the Legal Domain
Authors: Cécile Robin, James O'Neill, Paul Buitelaar
Date: 2017-10-04 00:00:00

A key challenge in the legal domain is the adaptation and representation of
the legal knowledge expressed through texts, in order for legal practitioners
and researchers to access this information easier and faster to help with
compliance related issues. One way to approach this goal is in the form of a
taxonomy of legal concepts. While this task usually requires a manual
construction of terms and their relations by domain experts, this paper
describes a methodology to automatically generate a taxonomy of legal noun
concepts. We apply and compare two approaches on a corpus consisting of
statutory instruments for UK, Wales, Scotland and Northern Ireland laws.
Energy-Based Spherical Sparse Coding
Authors: Bailey Kong, Charless C. Fowlkes
Date: 2017-10-04 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper, we explore an efficient variant of convolutional sparse coding
with unit norm code vectors where reconstruction quality is evaluated using an
inner product (cosine distance). To use these codes for discriminative
classification, we describe a model we term Energy-Based Spherical Sparse
Coding (EB-SSC) in which the hypothesized class label introduces a learned
linear bias into the coding step. We evaluate and visualize performance of
stacking this encoder to make a deep layered model for image classification.
Machine Learning in Appearance-based Robot Self-localization
Authors: Alexander Kuleshov, Alexander Bernstein, Evgeny Burnaev, Yury Yanovich
Date: 2017-06-17 00:00:00
Tasks: BIG-bench Machine Learning

An appearance-based robot self-localization problem is considered in the
machine learning framework. The appearance space is composed of all possible
images, which can be captured by a robot's visual system under all robot
localizations. Using recent manifold learning and deep learning techniques, we
propose a new geometrically motivated solution based on training data
consisting of a finite set of images captured in known locations of the robot.
The solution includes estimation of the robot localization mapping from the
appearance space to the robot localization space, as well as estimation of the
inverse mapping for modeling visual image features. The latter allows solving
the robot localization problem as the Kalman filtering problem.
Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion Lexicon
Authors: Giannis Haralabopoulos, Elena Simperl
Date: 2017-10-04 00:00:00
Tasks: Diversity, Sentiment Analysis

Sentiment analysis aims to uncover emotions conveyed through information. In
its simplest form, it is performed on a polarity basis, where the goal is to
classify information with positive or negative emotion. Recent research has
explored more nuanced ways to capture emotions that go beyond polarity. For
these methods to work, they require a critical resource: a lexicon that is
appropriate for the task at hand, in terms of the range of emotions it captures
diversity. In the past, sentiment analysis lexicons have been created by
experts, such as linguists and behavioural scientists, with strict rules.
Lexicon evaluation was also performed by experts or gold standards. In our
paper, we propose a crowdsourcing method for lexicon acquisition, which is
scalable, cost-effective, and doesn't require experts or gold standards. We
also compare crowd and expert evaluations of the lexicon, to assess the overall
lexicon quality, and the evaluation capabilities of the crowd.
Syntactic and Semantic Features For Code-Switching Factored Language Models
Authors: Heike Adel, Ngoc Thang Vu, Katrin Kirchhoff, Dominic Telaar, Tanja Schultz
Date: 2017-10-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Word Embeddings

This paper presents our latest investigations on different features for
factored language models for Code-Switching speech and their effect on
automatic speech recognition (ASR) performance. We focus on syntactic and
semantic features which can be extracted from Code-Switching text data and
integrate them into factored language models. Different possible factors, such
as words, part-of-speech tags, Brown word clusters, open class words and
clusters of open class word embeddings are explored. The experimental results
reveal that Brown word clusters, part-of-speech tags and open-class words are
the most effective at reducing the perplexity of factored language models on
the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model
containing Brown word clusters and part-of-speech tags and the model also
including clusters of open class word embeddings yield the best mixed error
rate results. In summary, the best language model can significantly reduce the
perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed
error rate by up to 3.4% relative.
Dense RGB-D semantic mapping with Pixel-Voxel neural network
Authors: Cheng Zhao, Li Sun, Pulak Purkait, Rustam Stolkin
Date: 2017-09-30 00:00:00
Tasks: 3D Reconstruction, GPU, Scene Understanding, Semantic Segmentation

For intelligent robotics applications, extending 3D mapping to 3D semantic
mapping enables robots to, not only localize themselves with respect to the
scene's geometrical features but also simultaneously understand the higher
level meaning of the scene contexts. Most previous methods focus on geometric
3D reconstruction and scene understanding independently notwithstanding the
fact that joint estimation can boost the accuracy of the semantic mapping. In
this paper, a dense RGB-D semantic mapping system with a Pixel-Voxel network is
proposed, which can perform dense 3D mapping while simultaneously recognizing
and semantically labelling each point in the 3D map. The proposed Pixel-Voxel
network obtains global context information by using PixelNet to exploit the RGB
image and meanwhile, preserves accurate local shape information by using
VoxelNet to exploit the corresponding 3D point cloud. Unlike the existing
architecture that fuses score maps from different models with equal weights, we
proposed a Softmax weighted fusion stack that adaptively learns the varying
contributions of PixelNet and VoxelNet, and fuses the score maps of the two
models according to their respective confidence levels. The proposed
Pixel-Voxel network achieves the state-of-the-art semantic segmentation
performance on the SUN RGB-D benchmark dataset. The runtime of the proposed
system can be boosted to 11-12Hz, enabling near to real-time performance using
an i7 8-cores PC with Titan X GPU.
Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
Authors: Anoop Kunchukuttan, Maulik Shah, Pradyot Prakash, Pushpak Bhattacharyya
Date: 2017-02-23 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Translation

We investigate pivot-based translation between related languages in a low
resource, phrase-based SMT setting. We show that a subword-level pivot-based
SMT model using a related pivot language is substantially better than word and
morpheme-level pivot models. It is also highly competitive with the best direct
translation model, which is encouraging as no direct source-target training
corpus is used. We also show that combining multiple related language pivot
models can rival a direct translation model. Thus, the use of subwords as
translation units coupled with multiple related pivot languages can compensate
for the lack of a direct parallel corpus.
Counterfactual Language Model Adaptation for Suggesting Phrases
Authors: Kenneth C. Arnold, Kai-Wei Chang, Adam T. Kalai
Date: 2017-10-04 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: counterfactual, Language Modeling, Language Modelling, model

Mobile devices use language models to suggest words and phrases for use in
text entry. Traditional language models are based on contextual word frequency
in a static corpus of text. However, certain types of phrases, when offered to
writers as suggestions, may be systematically chosen more often than their
frequency would predict. In this paper, we propose the task of generating
suggestions that writers accept, a related but distinct task to making accurate
predictions. Although this task is fundamentally interactive, we propose a
counterfactual setting that permits offline training and evaluation. We find
that even a simple language model can capture text characteristics that improve
acceptability.
Enhanced Neural Machine Translation by Learning from Draft
Authors: Aodong Li, Shiyue Zhang, Dong Wang, Thomas Fang Zheng
Date: 2017-10-04 00:00:00
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) has recently achieved impressive results. A
potential problem of the existing NMT algorithm, however, is that the decoding
is conducted from left to right, without considering the right context. This
paper proposes an two-stage approach to solve the problem. In the first stage,
a conventional attention-based NMT system is used to produce a draft
translation, and in the second stage, a novel double-attention NMT system is
used to refine the translation, by looking at the original input as well as the
draft translation. This drafting-and-refinement can obtain the right-context
information from the draft, hence producing more consistent translations. We
evaluated this approach using two Chinese-English translation tasks, one with
44k pairs and 1M pairs respectively. The experiments showed that our approach
achieved positive improvements over the conventional NMT system: the
improvements are 2.4 and 0.9 BLEU points on the small-scale and large-scale
tasks, respectively.
Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Authors: Ming Yu, Addie M. Thompson, Karthikeyan Natesan Ramamurthy, Eunho Yang, Aurélie C. Lozano
Date: 2017-10-04 00:00:00
Tasks: Clustering, Multi-Task Learning

Inferring predictive maps between multiple input and multiple output
variables or tasks has innumerable applications in data science. Multi-task
learning attempts to learn the maps to several output tasks simultaneously with
information sharing between them. We propose a novel multi-task learning
framework for sparse linear regression, where a full task hierarchy is
automatically inferred from the data, with the assumption that the task
parameters follow a hierarchical tree structure. The leaves of the tree are the
parameters for individual tasks, and the root is the global model that
approximates all the tasks. We apply the proposed approach to develop and
evaluate: (a) predictive models of plant traits using large-scale and automated
remote sensing data, and (b) GWAS methodologies mapping such derived phenotypes
in lieu of hand-measured traits. We demonstrate the superior performance of our
approach compared to other methods, as well as the usefulness of discovering
hierarchical groupings between tasks. Our results suggest that richer genetic
mapping can indeed be obtained from the remote sensing data. In addition, our
discovered groupings reveal interesting insights from a plant science
perspective.