pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable machine learning: definitions, methods, and applications
Authors: W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu
Date: 2019-01-14 00:00:00
Tasks: BIG-bench Machine Learning, Feature Importance, Interpretable Machine Learning

Official code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions" https://arxiv.org/abs/1806.05337
Human few-shot learning of compositional instructions
Authors: Brenden M. Lake, Tal Linzen, Marco Baroni
Date: 2019-01-14 00:00:00
Tasks: Few-Shot Learning

People learn in fast and flexible ways that have not been emulated by machines. Once a person learns a new verb "dax," he or she can effortlessly understand how to "dax twice," "walk and dax," or "dax vigorously." There have been striking recent improvements in machine learning for natural language processing, yet the best algorithms require vast amounts of experience and struggle to generalize new concepts in compositional ways. To better understand these distinctively human abilities, we study the compositional skills of people through language-like instruction learning tasks. Our results show that people can learn and use novel functional concepts from very few examples (few-shot learning), successfully applying familiar functions to novel inputs. People can also compose concepts in complex ways that go beyond the provided demonstrations. Two additional experiments examined the assumptions and inductive biases that people make when solving these tasks, revealing three biases: mutual exclusivity, one-to-one mappings, and iconic concatenation. We discuss the implications for cognitive modeling and the potential for building machines with more human-like language learning capabilities.
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
Authors: Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi
Date: 2019-01-14 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

As more researchers have become aware of and passionate about algorithmic
fairness, there has been an explosion in papers laying out new metrics,
suggesting algorithms to address issues, and calling attention to issues in
existing applications of machine learning. This research has greatly expanded
our understanding of the concerns and challenges in deploying machine learning,
but there has been much less work in seeing how the rubber meets the road.
  In this paper we provide a case-study on the application of fairness in
machine learning research to a production classification system, and offer new
insights in how to measure and address algorithmic fairness issues. We discuss
open questions in implementing equality of opportunity and describe our
fairness metric, conditional equality, that takes into account distributional
differences. Further, we provide a new approach to improve on the fairness
metric during model training and demonstrate its efficacy in improving
performance for a real-world product
Music Artist Classification with Convolutional Recurrent Neural Networks
Authors: Zain Nasrullah, Yue Zhao
Date: 2019-01-14 00:00:00
Tasks: Artist classification, Classification, General Classification, Information Retrieval, Music Information Retrieval, Retrieval

Previous attempts at music artist classification use frame level audio
features which summarize frequency content within short intervals of time.
Comparatively, more recent music information retrieval tasks take advantage of
temporal structure in audio spectrograms using deep convolutional and recurrent
models. This paper revisits artist classification with this new framework and
empirically explores the impacts of incorporating temporal structure in the
feature representation. To this end, an established classification
architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to
the artist20 music artist identification dataset under a comprehensive set of
conditions. These include audio clip length, which is a novel contribution in
this work, and previously identified considerations such as dataset split and
feature level. Our results improve upon baseline works, verify the influence of
the producer effect on classification performance and demonstrate the
trade-offs between audio length and training set size. The best performing
model achieves an average F1 score of 0.937 across three independent trials
which is a substantial improvement over the corresponding baseline under
similar conditions. Additionally, to showcase the effectiveness of the CRNN's
feature extraction capabilities, we visualize audio samples at the model's
bottleneck layer demonstrating that learned representations segment into
clusters belonging to their respective artists.
Self-Supervised Deep Active Accelerated MRI
Authors: Kyong Hwan Jin, Michael Unser, Kwang Moo Yi
Date: 2019-01-14 00:00:00

We propose to simultaneously learn to sample and reconstruct magnetic
resonance images (MRI) to maximize the reconstruction quality given a limited
sample budget, in a self-supervised setup. Unlike existing deep methods that
focus only on reconstructing given data, thus being passive, we go beyond the
current state of the art by considering both the data acquisition and the
reconstruction process within a single deep-learning framework. As our network
learns to acquire data, the network is active in nature. In order to do so, we
simultaneously train two neural networks, one dedicated to reconstruction and
the other to progressive sampling, each with an automatically generated
supervision signal that links them together. The two supervision signals are
created through Monte Carlo tree search (MCTS). MCTS returns a better sampling
pattern than what the current sampling network can give and, thus, a better
final reconstruction. The sampling network is trained to mimic the MCTS results
using the previous sampling network, thus being enhanced. The reconstruction
network is trained to give the highest reconstruction quality, given the MCTS
sampling pattern. Through this framework, we are able to train the two networks
without providing any direct supervision on sampling.
Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning
Authors: Yi Zhen, Hang Chen, Xu Zhang, Meng Liu, Xin Meng, Jian Zhang, Jiantao Pu
Date: 2019-01-14 00:00:00
Tasks: Deep Learning

To investigate whether and to what extent central serous chorioretinopathy
(CSC) depicted on color fundus photographs can be assessed using deep learning
technology. We collected a total of 2,504 fundus images acquired on different
subjects. We verified the CSC status of these images using their corresponding
optical coherence tomography (OCT) images. A total of 1,329 images depicted
CSC. These images were preprocessed and normalized. This resulting dataset was
randomly split into three parts in the ratio of 8:1:1 respectively for
training, validation, and testing purposes. We used the deep learning
architecture termed InceptionV3 to train the classifier. We performed
nonparametric receiver operating characteristic (ROC) analyses to assess the
capability of the developed algorithm to identify CSC. The Kappa coefficient
between the two raters was 0.48 (p < 0.001), while the Kappa coefficients
between the computer and the two raters were 0.59 (p < 0.001) and 0.33 (p <
0.05).Our experiments showed that the computer algorithm based on deep learning
can assess CSC depicted on color fundus photographs in a relatively reliable
and consistent way.
CrossNet: Latent Cross-Consistency for Unpaired Image Translation
Authors: Omry Sendik, Dani Lischinski, Daniel Cohen-Or
Date: 2019-01-14 00:00:00
Tasks: Image-to-Image Translation, Translation

Recent GAN-based architectures have been able to deliver impressive performance on the general task of image-to-image translation. In particular, it was shown that a wide variety of image translation operators may be learned from two image sets, containing images from two different domains, without establishing an explicit pairing between the images. This was made possible by introducing clever regularizers to overcome the under-constrained nature of the unpaired translation problem. In this work, we introduce a novel architecture for unpaired image translation, and explore several new regularizers enabled by it. Specifically, our architecture comprises a pair of GANs, as well as a pair of translators between their respective latent spaces. These cross-translators enable us to impose several regularizing constraints on the learnt image translation operator, collectively referred to as latent cross-consistency. Our results show that our proposed architecture and latent cross-consistency constraints are able to outperform the existing state-of-the-art on a variety of image translation tasks.
Approaching Ethical Guidelines for Data Scientists
Authors: Ursula Garzcarek, Detlef Steuer
Date: 2019-01-14 00:00:00
Tasks: Ethics, Fairness

The goal of this article is to inspire data scientists to participate in the
debate on the impact that their professional work has on society, and to become
active in public debates on the digital world as data science professionals.
How do ethical principles (e.g., fairness, justice, beneficence, and
non-maleficence) relate to our professional lives? What lies in our
responsibility as professionals by our expertise in the field? More
specifically this article makes an appeal to statisticians to join that debate,
and to be part of the community that establishes data science as a proper
profession in the sense of Airaksinen, a philosopher working on professional
ethics. As we will argue, data science has one of its roots in statistics and
extends beyond it. To shape the future of statistics, and to take
responsibility for the statistical contributions to data science, statisticians
should actively engage in the discussions. First the term data science is
defined, and the technical changes that have led to a strong influence of data
science on society are outlined. Next the systematic approach from CNIL is
introduced. Prominent examples are given for ethical issues arising from the
work of data scientists. Further we provide reasons why data scientists should
engage in shaping morality around and to formulate codes of conduct and codes
of practice for data science. Next we present established ethical guidelines
for the related fields of statistics and computing machinery. Thereafter
necessary steps in the community to develop professional ethics for data
science are described. Finally we give our starting statement for the debate:
Data science is in the focal point of current societal development. Without
becoming a profession with professional ethics, data science will fail in
building trust in its interaction with and its much needed contributions to
society!
A Novel Topology Optimization Approach using Conditional Deep Learning
Authors: Sharad Rawat, M. -H. Herman Shen
Date: 2019-01-14 00:00:00
Tasks: Deep Learning

In this study, a novel topology optimization approach based on conditional
Wasserstein generative adversarial networks (CWGAN) is developed to replicate
the conventional topology optimization algorithms in an extremely
computationally inexpensive way. CWGAN consists of a generator and a
discriminator, both of which are deep convolutional neural networks (CNN). The
limited samples of data, quasi-optimal planar structures, needed for training
purposes are generated using the conventional topology optimization algorithms.
With CWGANs, the topology optimization conditions can be set to a required
value before generating samples. CWGAN truncates the global design space by
introducing an equality constraint by the designer. The results are validated
by generating an optimized planar structure using the conventional algorithms
with the same settings. A proof of concept is presented which is known to be
the first such illustration of fusion of CWGANs and topology optimization.
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study
Authors: Johan Pensar, Yingying Xu, Santeri Puranen, Maiju Pesonen, Yoshiyuki Kabashima, Jukka Corander
Date: 2019-01-14 00:00:00

Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades. As a result of the general applicability of the model class, a myriad of methods have been developed in parallel in several research fields. Recently, as the size of the considered systems has increased, the focus of new methods has been shifted towards the high-dimensional domain. In particular, introduction of the pseudo-likelihood function has pushed the limits of score-based methods which were originally based on the likelihood function. At the same time, methods based on simple pairwise tests have been developed to meet the challenges arising from increasingly large data sets in computational biology. Apart from being applicable to high-dimensional problems, methods based on the pseudo-likelihood and pairwise tests are fundamentally very different. To compare the accuracy of the different types of methods, an extensive numerical study is performed on data generated by binary pairwise Markov networks. A parallelizable Gibbs sampler, based on restricted Boltzmann machines, is proposed as a tool to efficiently sample from sparse high-dimensional networks. The results of the study show that pairwise methods can be more accurate than pseudo-likelihood methods in settings often encountered in high-dimensional structure learning applications.
A data-driven approach for discovering heat load patterns in district heating
Authors: Ece Calikus, Slawomir Nowaczyk, Anita Sant'Anna, Henrik Gadd, Sven Werner
Date: 2019-01-14 00:00:00
Tasks: Management

Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers' heat-use habits.
Tango: A Deep Neural Network Benchmark Suite for Various Accelerators
Authors: Aajna Karki, Chethan Palangotu Keshava, Spoorthi Mysore Shivakumar, Joshua Skow, Goutam Madhukeshwar Hegde, Hyeran Jeon
Date: 2019-01-14 00:00:00
Tasks: GPU

Deep neural networks (DNNs) have been proving the effectiveness in various
computing fields. To provide more efficient computing platforms for DNN
applications, it is essential to have evaluation environments that include
assorted benchmark workloads. Though a few DNN benchmark suites have been
recently released, most of them require to install proprietary DNN libraries or
resource-intensive DNN frameworks, which are hard to run on resource-limited
mobile platforms or architecture simulators. To provide a more scalable
evaluation environment, we propose a new DNN benchmark suite that can run on
any platform that supports CUDA and OpenCL. The proposed benchmark suite
includes the most widely used five convolution neural networks and two
recurrent neural networks. We provide in-depth architectural statistics of
these networks while running them on an architecture simulator, a server- and a
mobile-GPU, and a mobile FPGA.
Electrical Impedance Tomography based on Genetic Algorithm
Authors: Mingyong Zhou
Date: 2019-01-14 00:00:00
Tasks: 1 Image, 2*2 Stitchi

In this paper, we applies GA algorithm into Electrical Impedance Tomography
(EIT) application. We first outline the EIT problem as an optimization problem
and define a target optimization function. Then we show how the GA algorithm as
an alternative searching algorithm can be used for solving EIT inverse problem.
In this paper, we explore evolutionary methods such as GA algorithms combined
with various regularization operators to solve EIT inverse computing problem.
  Key words: Electrical Impedance Tomography (EIT), GA, Tikhonov operator ,
Mumford-Shah operator, Particle Swarm Optimization(PSO), Back Propagation(BP).
NNStreamer: Stream Processing Paradigm for Neural Networks, Toward Efficient Development and Execution of On-Device AI Applications
Authors: MyungJoo Ham, Ji Joong Moon, Geunsik Lim, Wook Song, Jaeyun Jung, Hyoungjoo Ahn, Sangjung Woo, Youngchul Cho, Jinhyuck Park, Sewon Oh, Hong-Seok Kim
Date: 2019-01-12 00:00:00

We propose nnstreamer, a software system that handles neural networks as
filters of stream pipelines, applying the stream processing paradigm to neural
network applications. A new trend with the wide-spread of deep neural network
applications is on-device AI; i.e., processing neural networks directly on
mobile devices or edge/IoT devices instead of cloud servers. Emerging privacy
issues, data transmission costs, and operational costs signifies the need for
on-device AI especially when a huge number of devices with real-time data
processing are deployed. Nnstreamer efficiently handles neural networks with
complex data stream pipelines on devices, improving the overall performance
significantly with minimal efforts. Besides, nnstreamer simplifies the neural
network pipeline implementations and allows reusing off-shelf multimedia stream
filters directly; thus it reduces the developmental costs significantly.
Nnstreamer is already being deployed with a product releasing soon and is open
source software applicable to a wide range of hardware architectures and
software platforms.
Determining Multifunctional Genes and Diseases in Human Using Gene Ontology
Authors: Hisham Al-Mubaid, Sasikanth Potu, M. Shenify
Date: 2019-01-11 00:00:00
Tasks: Drug Discovery

The study of human genes and diseases is very rewarding and can lead to
improvements in healthcare, disease diagnostics and drug discovery. In this
paper, we further our previous study on gene disease relationship specifically
with the multifunctional genes. We investigate the multifunctional gene disease
relationship based on the published molecular function annotations of genes
from the Gene Ontology which is the most comprehensive source on gene
functions.
Low Precision Constant Parameter CNN on FPGA
Authors: Thiam Khean Hah, Yeong Tat Liew, Jason Ong
Date: 2019-01-11 00:00:00
Tasks: GPU

We report FPGA implementation results of low precision CNN convolution layers
optimized for sparse and constant parameters. We describe techniques that
amortizes the cost of common factor multiplication and automatically leverage
dense hand tuned LUT structures. We apply this method to corner case residual
blocks of Resnet on a sparse Resnet50 model to assess achievable utilization
and frequency and demonstrate an effective performance of 131 and 23 TOP/chip
for the corner case blocks. The projected performance on a multichip persistent
implementation of all Resnet50 convolution layers is 10k im/s/chip at batch
size 2. This is 1.37x higher than V100 GPU upper bound at the same batch size
after normalizing for sparsity.
Precise Performance Analysis of the Box-Elastic Net under Matrix Uncertainties
Authors: Ayed M. Alrashdi, Ismail Ben Atitallah, Tareq Y. Al-Naffouri
Date: 2019-01-11 00:00:00

In this letter, we consider the problem of recovering an unknown sparse signal from noisy linear measurements, using an enhanced version of the popular Elastic-Net (EN) method. We modify the EN by adding a box-constraint, and we call it the Box-Elastic Net (Box-EN). We assume independent identically distributed (iid) real Gaussian measurement matrix with additive Gaussian noise. In many practical situations, the measurement matrix is not perfectly known, and so we only have a noisy estimate of it. In this work, we precisely characterize the mean squared error and the probability of support recovery of the Box-Elastic Net in the high-dimensional asymptotic regime. Numerical simulations validate the theoretical predictions derived in the paper and also show that the boxed variant outperforms the standard EN.
A mixed model approach to drought prediction using artificial neural networks: Case of an operational drought monitoring environment
Authors: Chrisgone Adede, Robert Oboko, Peter Wagacha, Clement Atzberger
Date: 2019-01-10 00:00:00

Droughts, with their increasing frequency of occurrence, continue to
negatively affect livelihoods and elements at risk. For example, the 2011 in
drought in east Africa has caused massive losses document to have cost the
Kenyan economy over $12bn. With the foregoing, the demand for ex-ante drought
monitoring systems is ever-increasing. The study uses 10 precipitation and
vegetation variables that are lagged over 1, 2 and 3-month time-steps to
predict drought situations. In the model space search for the most predictive
artificial neural network (ANN) model, as opposed to the traditional greedy
search for the most predictive variables, we use the General Additive Model
(GAM) approach. Together with a set of assumptions, we thereby reduce the
cardinality of the space of models. Even though we build a total of 102 GAM
models, only 21 have R2 greater than 0.7 and are thus subjected to the ANN
process. The ANN process itself uses the brute-force approach that
automatically partitions the training data into 10 sub-samples, builds the ANN
models in these samples and evaluates their performance using multiple metrics.
The results show the superiority of 1-month lag of the variables as compared to
longer time lags of 2 and 3 months. The champion ANN model recorded an R2 of
0.78 in model testing using the out-of-sample data. This illustrates its
ability to be a good predictor of drought situations 1-month ahead.
Investigated as a classifier, the champion has a modest accuracy of 66% and a
multi-class area under the ROC curve (AUROC) of 89.99%
A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
Authors: Lixue Cheng, Matthew Welborn, Anders S. Christensen, Thomas F. Miller III
Date: 2019-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Diversity

We address the degree to which machine learning can be used to accurately and
transferably predict post-Hartree-Fock correlation energies. Refined strategies
for feature design and selection are presented, and the molecular-orbital-based
machine learning (MOB-ML) method is applied to several test systems.
Strikingly, for the MP2, CCSD, and CCSD(T) levels of theory, it is shown that
the thermally accessible (350 K) potential energy surface for a single water
molecule can be described to within 1 millihartree using a model that is
trained from only a single reference calculation at a randomized geometry. To
explore the breadth of chemical diversity that can be described, MOB-ML is also
applied to a new dataset of thermalized (350 K) geometries of 7211 organic
models with up to seven heavy atoms. In comparison with the previously reported
$\Delta$-ML method, MOB-ML is shown to reach chemical accuracy with three-fold
fewer training geometries. Finally, a transferability test in which models
trained for seven-heavy-atom systems are used to predict energies for
thirteen-heavy-atom systems reveals that MOB-ML reaches chemical accuracy with
36-fold fewer training calculations than $\Delta$-ML (140 versus 5000 training
calculations).