pwc-archive/papers-with-abstracts huggingface dataset:
Neuromorphic Hardware learns to learn
Authors: Thomas Bohnstingl, Franz Scherr, Christian Pehle, Karlheinz Meier, Wolfgang Maass
Date: 2019-03-15 00:00:00

Hyperparameters and learning algorithms for neuromorphic hardware are usually
chosen by hand. In contrast, the hyperparameters and learning algorithms of
networks of neurons in the brain, which they aim to emulate, have been
optimized through extensive evolutionary and developmental processes for
specific ranges of computing and learning tasks. Occasionally this process has
been emulated through genetic algorithms, but these require themselves
hand-design of their details and tend to provide a limited range of
improvements. We employ instead other powerful gradient-free optimization
tools, such as cross-entropy methods and evolutionary strategies, in order to
port the function of biological optimization processes to neuromorphic
hardware. As an example, we show that this method produces neuromorphic agents
that learn very efficiently from rewards. In particular, meta-plasticity, i.e.,
the optimization of the learning rule which they use, substantially enhances
reward-based learning capability of the hardware. In addition, we demonstrate
for the first time Learning-to-Learn benefits from such hardware, in
particular, the capability to extract abstract knowledge from prior learning
experiences that speeds up the learning of new but related tasks.
Learning-to-Learn is especially suited for accelerated neuromorphic hardware,
since it makes it feasible to carry out the required very large number of
network computations.
Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets
Authors: Sang Il Lee, Seong Joon Yoo
Date: 2019-03-15 00:00:00
Tasks: Multimodal Deep Learning, Prediction, Stock Prediction

In today's increasingly international economy, return and volatility spillover effects across international equity markets are major macroeconomic drivers of stock dynamics. Thus, information regarding foreign markets is one of the most important factors in forecasting domestic stock prices. However, the cross-correlation between domestic and foreign markets is highly complex. Hence, it is extremely difficult to explicitly express this cross-correlation with a dynamical equation. In this study, we develop stock return prediction models that can jointly consider international markets, using multimodal deep learning. Our contributions are three-fold: (1) we visualize the transfer information between South Korea and US stock markets by using scatter plots; (2) we incorporate the information into the stock prediction models with the help of multimodal deep learning; (3) we conclusively demonstrate that the early and intermediate fusion models achieve a significant performance boost in comparison with the late fusion and single modality models. Our study indicates that jointly considering international stock markets can improve the prediction accuracy and deep neural networks are highly effective for such tasks.
DeepHuman: 3D Human Reconstruction from a Single Image
Authors: Zerong Zheng, Tao Yu, Yixuan Wei, Qionghai Dai, Yebin Liu
Date: 2019-03-15 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Human Reconstruction, Diversity, Pose Estimation, Translation

We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D
human reconstruction from a single RGB image. To reduce the ambiguities
associated with the surface geometry reconstruction, even for the
reconstruction of invisible areas, we propose and leverage a dense semantic
representation generated from SMPL model as an additional input. One key
feature of our network is that it fuses different scales of image features into
the 3D space through volumetric feature transformation, which helps to recover
accurate surface geometry. The visible surface details are further refined
through a normal refinement network, which can be concatenated with the volume
generation network using our proposed volumetric normal projection layer. We
also contribute THuman, a 3D real-world human model dataset containing about
7000 models. The network is trained using training data generated from the
dataset. Overall, due to the specific design of our network and the diversity
in our dataset, our method enables 3D human model estimation given only a
single image and outperforms state-of-the-art approaches.
A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks
Authors: Chanwoo Jeong, Sion Jang, Hyuna Shin, Eunjeong Park, Sungchul Choi
Date: 2019-03-15 00:00:00
Tasks: Benchmarking, Citation Recommendation, Sentence, TAG

With the tremendous growth in the number of scientific papers being
published, searching for references while writing a scientific paper is a
time-consuming process. A technique that could add a reference citation at the
appropriate place in a sentence will be beneficial. In this perspective,
context-aware citation recommendation has been researched upon for around two
decades. Many researchers have utilized the text data called the context
sentence, which surrounds the citation tag, and the metadata of the target
paper to find the appropriate cited research. However, the lack of
well-organized benchmarking datasets and no model that can attain high
performance has made the research difficult.
  In this paper, we propose a deep learning based model and well-organized
dataset for context-aware paper citation recommendation. Our model comprises a
document encoder and a context encoder, which uses Graph Convolutional Networks
(GCN) layer and Bidirectional Encoder Representations from Transformers (BERT),
which is a pre-trained model of textual data. By modifying the related PeerRead
dataset, we propose a new dataset called FullTextPeerRead containing context
sentences to cited references and paper metadata. To the best of our knowledge,
This dataset is the first well-organized dataset for context-aware paper
recommendation. The results indicate that the proposed model with the proposed
datasets can attain state-of-the-art performance and achieve a more than 28%
improvement in mean average precision (MAP) and recall@k.
Applying Probabilistic Programming to Affective Computing
Authors: Desmond C. Ong, Harold Soh, Jamil Zaki, Noah D. Goodman
Date: 2019-03-15 00:00:00
Tasks: Probabilistic Programming

Affective Computing is a rapidly growing field spurred by advancements in
artificial intelligence, but often, held back by the inability to translate
psychological theories of emotion into tractable computational models. To
address this, we propose a probabilistic programming approach to affective
computing, which models psychological-grounded theories as generative models of
emotion, and implements them as stochastic, executable computer programs. We
first review probabilistic approaches that integrate reasoning about emotions
with reasoning about other latent mental states (e.g., beliefs, desires) in
context. Recently-developed probabilistic programming languages offer several
key desidarata over previous approaches, such as: (i) flexibility in
representing emotions and emotional processes; (ii) modularity and
compositionality; (iii) integration with deep learning libraries that
facilitate efficient inference and learning from large, naturalistic data; and
(iv) ease of adoption. Furthermore, using a probabilistic programming framework
allows a standardized platform for theory-building and experimentation:
Competing theories (e.g., of appraisal or other emotional processes) can be
easily compared via modular substitution of code followed by model comparison.
To jumpstart adoption, we illustrate our points with executable code that
researchers can easily modify for their own models. We end with a discussion of
applications and future directions of the probabilistic programming approach.
Online Explanation Generation for Human-Robot Teaming
Authors: Mehrdad Zakershahrak, Ze Gong, Nikhillesh Sadassivam, Yu Zhang
Date: 2019-03-15 00:00:00
Tasks: Decision Making, Explanation Generation

As AI becomes an integral part of our lives, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to justify its behavior is one of the key requirements of explainable agency. Prior work on explanation generation has been focused on supporting the rationale behind the robot's decision or behavior. These approaches, however, fail to consider the mental demand for understanding the received explanation. In other words, the human teammate is expected to understand an explanation no matter how much information is presented. In this work, we argue that explanations, especially those of a complex nature, should be made in an online fashion during the execution, which helps spread out the information to be explained and thus reduce the mental workload of humans in highly cognitive demanding tasks. However, a challenge here is that the different parts of an explanation may be dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented with three variations satisfying different "online" properties. The new explanation generation methods are based on a model reconciliation setting introduced in our prior work. We evaluated our methods both with human subjects in a simulated rover domain, using NASA Task Load Index (TLX), and synthetically with ten different problems across two standard IPC domains. Results strongly suggest that our methods generate explanations that are perceived as less cognitively demanding and much preferred over the baselines and are computationally efficient.
A proposed method to extract maximum possible power in the shortest time on solar PV arrays under partial shadings using metaheuristic algorithms
Authors: Reza Hedayati Majdabadi, Saeed Sharifian Khortoomi
Date: 2019-03-15 00:00:00

The increasing use of fossil fuels to produce energy is leading to
environmental problems. Hence, it has led the human society to move towards the
use of renewable energies, including solar energy. In recent years, one of the
most popular methods to gain energy is using photovoltaic arrays to produce
solar energy. Skyscrapers and different weather conditions cause shadings on
these PV arrays, which leads to less power generation. Various methods such as
TCT and Sudoku patterns have been proposed to improve power generation for
partial shading PV arrays, but these methods have some problems such as not
generating maximum power and being designed for a specific dimension of PV
arrays. Therefore, we proposed a metaheuristic algorithm-based approach to
extract maximum possible power in the shortest possible time. In this paper,
five algorithms which have proper results in most of the searching problems are
chosen from different groups of metaheuristic algorithms. Also, four different
standard shading patterns are used for more realistic analysis. Results show
that the proposed method achieves better results in maximum power generation
compared to TCT arrangement (18.53%) and Sudoku arrangement (4.93%). Also, the
results show that GWO is the fastest metaheuristic algorithm to reach maximum
output power in PV arrays under partial shading condition. Thus, the authors
believe that by using metaheuristic algorithms, an efficient, reliable, and
fast solution is reached to solve partial shading PV arrays problem
A Faster Algorithm Enumerating Relevant Features over Finite Fields
Authors: Mikito Nanashima
Date: 2019-03-15 00:00:00

We consider the problem of enumerating relevant features hidden in other irrelevant information for multi-labeled data, which is formalized as learning juntas. A $k$-junta function is a function which depends on only $k$ coordinates of the input. For relatively small $k$ w.r.t. the input size $n$, learning $k$-junta functions is one of fundamental problems both theoretically and practically in machine learning. For the last two decades, much effort has been made to design efficient learning algorithms for Boolean junta functions, and some novel techniques have been developed. However, in real world, multi-labeled data seem to be obtained in much more often than binary-labeled one. Thus, it is a natural question whether these techniques can be applied to more general cases about the alphabet size. In this paper, we expand the Fourier detection techniques for the binary alphabet to any finite field $\mathbb{F}_q$, and give, roughly speaking, an $O(n^{0.8k})$-time learning algorithm for $k$-juntas over $\mathbb{F}_q$. Note that our algorithm is the first non-trivial (i.e., non-brute force) algorithm for such a class even in the case where $q=3$ and we give an affirmative answer to the question posed by Mossel et al. Our algorithm consists of two reductions: (1) from learning juntas to LDME which is a variant of the learning with errors (LWE) problems introduced by Regev, and (2) from LDME to the light bulb problem (LBP) introduced by L.Valiant. Since the reduced problem (i.e., LBP) is a kind of binary problem regardless of the alphabet size of the original problem (i.e., learning juntas), we can directly apply the techniques for the binary case in the previous work.
Automatic assessment of spoken language proficiency of non-native children
Authors: Roberto Gretter, Katharina Allgaier, Svetlana Tchistiakova, Daniele Falavigna
Date: 2019-03-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper describes technology developed to automatically grade Italian
students (ages 9-16) on their English and German spoken language proficiency.
The students' spoken answers are first transcribed by an automatic speech
recognition (ASR) system and then scored using a feedforward neural network
(NN) that processes features extracted from the automatic transcriptions.
In-domain acoustic models, employing deep neural networks (DNNs), are derived
by adapting the parameters of an original out of domain DNN.
BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
Authors: Jianru Xue, Jianwu Fang, Tao Li, Bohua Zhang, Pu Zhang, Zhen Ye, Jian Dou
Date: 2019-03-15 00:00:00
Tasks: 2D Object Detection, Autonomous Driving, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation, valid

In autonomous driving community, numerous benchmarks have been established to
assist the tasks of 3D/2D object detection, stereo vision, semantic/instance
segmentation. However, the more meaningful dynamic evolution of the surrounding
objects of ego-vehicle is rarely exploited, and lacks a large-scale dataset
platform. To address this, we introduce BLVD, a large-scale 5D semantics
benchmark which does not concentrate on the static detection or
semantic/instance segmentation tasks tackled adequately before. Instead, BLVD
aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking,
5D (4D+interactive) interactive event recognition and intention prediction.
This benchmark will boost the deeper understanding of traffic scenes than ever
before. We totally yield 249,129 3D annotations, 4,902 independent individuals
for tracking with the length of overall 214,922 points, 6,004 valid fragments
for 5D interactive event recognition, and 4,900 individuals for 5D intention
prediction. These tasks are contained in four kinds of scenarios depending on
the object density (low and high) and light conditions (daytime and nighttime).
The benchmark can be downloaded from our project site
https://github.com/VCCIV/BLVD/.
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages
Authors: Shauli Ravfogel, Yoav Goldberg, Tal Linzen
Date: 2019-03-15 00:00:00
Proceeding: NAACL 2019 6
Tasks: Object

How do typological properties such as word order and morphological case
marking affect the ability of neural sequence models to acquire the syntax of a
language? Cross-linguistic comparisons of RNNs' syntactic performance (e.g., on
subject-verb agreement prediction) are complicated by the fact that any two
languages differ in multiple typological properties, as well as by differences
in training corpus. We propose a paradigm that addresses these issues: we
create synthetic versions of English, which differ from English in one or more
typological parameters, and generate corpora for those languages based on a
parsed English corpus. We report a series of experiments in which RNNs were
trained to predict agreement features for verbs in each of those synthetic
languages. Among other findings, (1) performance was higher in
subject-verb-object order (as in English) than in subject-object-verb order (as
in Japanese), suggesting that RNNs have a recency bias; (2) predicting
agreement with both subject and object (polypersonal agreement) improves over
predicting each separately, suggesting that underlying syntactic knowledge
transfers across the two tasks; and (3) overt morphological case makes
agreement prediction significantly easier, regardless of word order.
Quality-aware Unpaired Image-to-Image Translation
Authors: Lei Chen, Le Wu, Zhenzhen Hu, Meng Wang
Date: 2019-03-15 00:00:00
Tasks: Image Quality Assessment, Image-to-Image Translation, Translation

Generative Adversarial Networks (GANs) have been widely used for the
image-to-image translation task. While these models rely heavily on the labeled
image pairs, recently some GAN variants have been proposed to tackle the
unpaired image translation task. These models exploited supervision at the
domain level with a reconstruction process for unpaired image translation. On
the other hand, parallel works have shown that leveraging perceptual loss
functions based on high level deep features could enhance the generated image
quality. Nevertheless, as these GAN-based models either depended on the
pretrained deep network structure or relied on the labeled image pairs, they
could not be directly applied to the unpaired image translation task. Moreover,
despite the improvement of the introduced perceptual losses from deep neural
networks, few researchers have explored the possibility of improving the
generated image quality from classical image quality measures. To tackle the
above two challenges, in this paper, we propose a unified quality-aware
GAN-based framework for unpaired image-to-image translation, where a
quality-aware loss is explicitly incorporated by comparing each source image
and the reconstructed image at the domain level. Specifically, we design two
detailed implementations of the quality loss. The first method is based on a
classical image quality assessment measure by defining a classical
quality-aware loss. The second method proposes an adaptive deep network based
loss. Finally, extensive experimental results on many real-world datasets
clearly show the quality improvement of our proposed framework, and the
superiority of leveraging classical image quality measures for unpaired image
translation compared to the deep network based model.
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance
Authors: Yilun Zhang, Ty Nguyen, Ian D. Miller, Shreyas S. Shivakumar, Steven Chen, Camillo J. Taylor, Vijay Kumar
Date: 2019-03-15 00:00:00
Tasks: Autonomous Vehicles, Depth Completion, Depth Estimation, Motion Estimation

Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution. Accurate depth sensors such as LiDARs are often heavy, expensive and can only provide sparse depth while lighter depth sensors such as stereo cameras are noiser in comparison. We propose an end-to-end learning algorithm that is capable of using sparse, noisy input depth for refinement and depth completion. Our model also produces the camera pose as a byproduct, making it a great solution for autonomous systems. We evaluate our approach on both indoor and outdoor datasets. Empirical results show that our method performs well on the KITTI~\cite{kitti_geiger2012we} dataset when compared to other competing methods, while having superior performance in dealing with sparse, noisy input depth on the TUM~\cite{sturm12iros} dataset.
COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite
Authors: Ouassim Elhara, Konstantinos Varelas, Duc Nguyen, Tea Tusar, Dimo Brockhoff, Nikolaus Hansen, Anne Auger
Date: 2019-03-15 00:00:00
Tasks: Benchmarking

The bbob-largescale test suite, containing 24 single-objective functions in
continuous domain, extends the well-known single-objective noiseless bbob test
suite, which has been used since 2009 in the BBOB workshop series, to large
dimension. The core idea is to make the rotational transformations R, Q in
search space that appear in the bbob test suite computationally cheaper while
retaining some desired properties. This documentation presents an approach that
replaces a full rotational transformation with a combination of a
block-diagonal matrix and two permutation matrices in order to construct test
functions whose computational and memory costs scale linearly in the dimension
of the problem.
Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors
Authors: Quazi Marufur Rahman, Niko Sünderhauf, Feras Dayoub
Date: 2019-03-15 00:00:00
Tasks: Object, object-detection, Object Detection, Traffic Sign Detection, Traffic Sign Recognition

Object detection is an integral part of an autonomous vehicle for its
safety-critical and navigational purposes. Traffic signs as objects play a
vital role in guiding such systems. However, if the vehicle fails to locate any
critical sign, it might make a catastrophic failure. In this paper, we propose
an approach to identify traffic signs that have been mistakenly discarded by
the object detector. The proposed method raises an alarm when it discovers a
failure by the object detector to detect a traffic sign. This approach can be
useful to evaluate the performance of the detector during the deployment phase.
We trained a single shot multi-box object detector to detect traffic signs and
used its internal features to train a separate false negative detector (FND).
During deployment, FND decides whether the traffic sign detector (TSD) has
missed a sign or not. We are using precision and recall to measure the accuracy
of FND in two different datasets. For 80% recall, FND has achieved 89.9%
precision in Belgium Traffic Sign Detection dataset and 90.8% precision in
German Traffic Sign Recognition Benchmark dataset respectively. To the best of
our knowledge, our method is the first to tackle this critical aspect of false
negative detection in robotic vision. Such a fail-safe mechanism for object
detection can improve the engagement of robotic vision systems in our daily
life.