pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Artificial Intelligence with Real-time Intracranial EEG Monitoring to Automate Interictal Identification of Seizure Onset Zones in Focal Epilepsy
Authors: Yogatheesan Varatharajah, Brent Berry, Jan Cimbalnik, Vaclav Kremen, Jamie Van Gompel, Matt Stead, Benjamin Brinkmann, Ravishankar Iyer, Gregory Worrell
Date: 2018-12-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

An ability to map seizure-generating brain tissue, i.e., the seizure onset
zone (SOZ), without recording actual seizures could reduce the duration of
invasive EEG monitoring for patients with drug-resistant epilepsy. A
widely-adopted practice in the literature is to compare the incidence
(events/time) of putative pathological electrophysiological biomarkers
associated with epileptic brain tissue with the SOZ determined from spontaneous
seizures recorded with intracranial EEG, primarily using a single biomarker.
Clinical translation of the previous efforts suffers from their inability to
generalize across multiple patients because of (a) the inter-patient
variability and (b) the temporal variability in the epileptogenic activity.
Here, we report an artificial intelligence-based approach for combining
multiple interictal electrophysiological biomarkers and their temporal
characteristics as a way of accounting for the above barriers and show that it
can reliably identify seizure onset zones in a study cohort of 82 patients who
underwent evaluation for drug-resistant epilepsy. Our investigation provides
evidence that utilizing the complementary information provided by multiple
electrophysiological biomarkers and their temporal characteristics can
significantly improve the localization potential compared to previously
published single-biomarker incidence-based approaches, resulting in an average
area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our
results also suggest that recording durations between ninety minutes and two
hours are sufficient to localize SOZs with accuracies that may prove clinically
relevant. The successful validation of our approach on a large cohort of 82
patients warrants future investigation on the feasibility of utilizing
intra-operative EEG monitoring and artificial intelligence to localize
epileptogenic brain tissue.
Mapper Comparison with Wasserstein Metrics
Authors: Michael McCabe
Date: 2018-12-15 00:00:00
Tasks: Anomaly Detection, Open-Ended Question Answering, Topological Data Analysis

The challenge of describing model drift is an open question in unsupervised
learning. It can be difficult to evaluate at what point an unsupervised model
has deviated beyond what would be expected from a different sample from the
same population. This is particularly true for models without a probabilistic
interpretation. One such family of techniques, Topological Data Analysis, and
the Mapper algorithm in particular, has found use in a variety of fields, but
describing model drift for Mapper graphs is an understudied area as even
existing techniques for measuring distances between related constructs like
graphs or simplicial complexes fail to account for the fact that Mapper graphs
represent a combination of topological, metric, and density information. In
this paper, we develop an optimal transport based metric which we call the
Network Augmented Wasserstein Distance for evaluating distances between Mapper
graphs and demonstrate the value of the metric for model drift analysis by
using the metric to transform the model drift problem into an anomaly detection
problem over dynamic graphs.
Domain-to-Domain Translation Model for Recommender System
Authors: Linh Nguyen, Tsukasa Ishigaki
Date: 2018-12-15 00:00:00
Tasks: model, Multi-Domain Recommender Systems, Recommendation Systems, Translation

Recently multi-domain recommender systems have received much attention from
researchers because they can solve cold-start problem as well as support for
cross-selling. However, when applying into multi-domain items, although
algorithms specifically addressing a single domain have many difficulties in
capturing the specific characteristics of each domain, multi-domain algorithms
have less opportunity to obtain similar features among domains. Because both
similarities and differences exist among domains, multi-domain models must
capture both to achieve good performance. Other studies of multi-domain systems
merely transfer knowledge from the source domain to the target domain, so the
source domain usually comes from external factors such as the search query or
social network, which is sometimes impossible to obtain. To handle the two
problems, we propose a model that can extract both homogeneous and divergent
features among domains and extract data in a domain can support for other
domain equally: a so-called Domain-to-Domain Translation Model (D2D-TM). It is
based on generative adversarial networks (GANs), Variational Autoencoders
(VAEs), and Cycle-Consistency (CC) for weight-sharing. We use the user
interaction history of each domain as input and extract latent features through
a VAE-GAN-CC network. Experiments underscore the effectiveness of the proposed
system over state-of-the-art methods by a large margin.
Weakly supervised segment annotation via expectation kernel density estimation
Authors: Lian-Tao Wang, Qingwu Li, Jianfeng Lu
Date: 2018-12-15 00:00:00
Tasks: Density Estimation

Since the labelling for the positive images/videos is ambiguous in weakly
supervised segment annotation, negative mining based methods that only use the
intra-class information emerge. In these methods, negative instances are
utilized to penalize unknown instances to rank their likelihood of being an
object, which can be considered as a voting in terms of similarity. However,
these methods 1) ignore the information contained in positive bags, 2) only
rank the likelihood but cannot generate an explicit decision function. In this
paper, we propose a voting scheme involving not only the definite negative
instances but also the ambiguous positive instances to make use of the extra
useful information in the weakly labelled positive bags. In the scheme, each
instance votes for its label with a magnitude arising from the similarity, and
the ambiguous positive instances are assigned soft labels that are iteratively
updated during the voting. It overcomes the limitations of voting using only
the negative bags. We also propose an expectation kernel density estimation
(eKDE) algorithm to gain further insight into the voting mechanism.
Experimental results demonstrate the superiority of our scheme beyond the
baselines.
Balanced Linear Contextual Bandits
Authors: Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens
Date: 2018-12-15 00:00:00
Tasks: Causal Inference, Multi-Armed Bandits

Contextual bandit algorithms are sensitive to the estimation method of the
outcome model as well as the exploration method used, particularly in the
presence of rich heterogeneity or complex outcome models, which can lead to
difficult estimation problems along the path of learning. We develop algorithms
for contextual bandits with linear payoffs that integrate balancing methods
from the causal inference literature in their estimation to make it less prone
to problems of estimation bias. We provide the first regret bound analyses for
linear contextual bandits with balancing and show that our algorithms match the
state of the art theoretical guarantees. We demonstrate the strong practical
advantage of balanced contextual bandits on a large number of supervised
learning datasets and on a synthetic example that simulates model
misspecification and prejudice in the initial training data.
A Low Effort Approach to Structured CNN Design Using PCA
Authors: Isha Garg, Priyadarshini Panda, Kaushik Roy
Date: 2018-12-15 00:00:00
Tasks: Dimensionality Reduction, Model Compression

Deep learning models hold state of the art performance in many fields, yet their design is still based on heuristics or grid search methods that often result in overparametrized networks. This work proposes a method to analyze a trained network and deduce an optimized, compressed architecture that preserves accuracy while keeping computational costs tractable. Model compression is an active field of research that targets the problem of realizing deep learning models in hardware. However, most pruning methodologies tend to be experimental, requiring large compute and time intensive iterations of retraining the entire network. We introduce structure into model design by proposing a single shot analysis of a trained network that serves as a first order, low effort approach to dimensionality reduction, by using PCA (Principal Component Analysis). The proposed method simultaneously analyzes the activations of each layer and considers the dimensionality of the space described by the filters generating these activations. It optimizes the architecture in terms of number of layers, and number of filters per layer without any iterative retraining procedures, making it a viable, low effort technique to design efficient networks. We demonstrate the proposed methodology on AlexNet and VGG style networks on the CIFAR-10, CIFAR-100 and ImageNet datasets, and successfully achieve an optimized architecture with a reduction of up to 3.8X and 9X in the number of operations and parameters respectively, while trading off less than 1% accuracy. We also apply the method to MobileNet, and achieve 1.7X and 3.9X reduction in the number of operations and parameters respectively, while improving accuracy by almost one percentage point.
Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network
Authors: Haiyong Chen, Yue Pang, Qidi Hu, Kun Liu
Date: 2018-12-15 00:00:00
Tasks: Defect Detection, Multi-Document Summarization

Similar and indeterminate defect detection of solar cell surface with
heterogeneous texture and complex background is a challenge of solar cell
manufacturing. The traditional manufacturing process relies on human eye
detection which requires a large number of workers without a stable and good
detection effect. In order to solve the problem, a visual defect detection
method based on multi-spectral deep convolutional neural network (CNN) is
designed in this paper. Firstly, a selected CNN model is established. By
adjusting the depth and width of the model, the influence of model depth and
kernel size on the recognition result is evaluated. The optimal CNN model
structure is selected. Secondly, the light spectrum features of solar cell
color image are analyzed. It is found that a variety of defects exhibited
different distinguishable characteristics in different spectral bands. Thus, a
multi-spectral CNN model is constructed to enhance the discrimination ability
of the model to distinguish between complex texture background features and
defect features. Finally, some experimental results and K-fold cross validation
show that the multi-spectral deep CNN model can effectively detect the solar
cell surface defects with higher accuracy and greater adaptability. The
accuracy of defect recognition reaches 94.30%. Applying such an algorithm can
increase the efficiency of solar cell manufacturing and make the manufacturing
process smarter.
ABC: A Big CAD Model Dataset For Geometric Deep Learning
Authors: Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo
Date: 2018-12-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deep Learning

We introduce ABC-Dataset, a collection of one million Computer-Aided Design
(CAD) models for research of geometric deep learning methods and applications.
Each model is a collection of explicitly parametrized curves and surfaces,
providing ground truth for differential quantities, patch segmentation,
geometric feature detection, and shape reconstruction. Sampling the parametric
descriptions of surfaces and curves allows generating data in different formats
and resolutions, enabling fair comparisons for a wide range of geometric
learning algorithms. As a use case for our dataset, we perform a large-scale
benchmark for estimation of surface normals, comparing existing data driven
methods and evaluating their performance against both the ground truth and
traditional normal estimation methods.
A General Approach to Adding Differential Privacy to Iterative Training Procedures
Authors: H. Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, Peter Kairouz
Date: 2018-12-15 00:00:00

In this work we address the practical challenges of training machine learning
models on privacy-sensitive datasets by introducing a modular approach that
minimizes changes to training algorithms, provides a variety of configuration
strategies for the privacy mechanism, and then isolates and simplifies the
critical logic that computes the final privacy guarantees. A key challenge is
that training algorithms often require estimating many different quantities
(vectors) from the same set of examples --- for example, gradients of different
layers in a deep learning architecture, as well as metrics and batch
normalization parameters. Each of these may have different properties like
dimensionality, magnitude, and tolerance to noise. By extending previous work
on the Moments Accountant for the subsampled Gaussian mechanism, we can provide
privacy for such heterogeneous sets of vectors, while also structuring the
approach to minimize software engineering challenges.
Causal Identification under Markov Equivalence
Authors: Amin Jaber, Jiji Zhang, Elias Bareinboim
Date: 2018-12-15 00:00:00
Tasks: Causal Identification

Assessing the magnitude of cause-and-effect relations is one of the central
challenges found throughout the empirical sciences. The problem of
identification of causal effects is concerned with determining whether a causal
effect can be computed from a combination of observational data and substantive
knowledge about the domain under investigation, which is formally expressed in
the form of a causal graph. In many practical settings, however, the knowledge
available for the researcher is not strong enough so as to specify a unique
causal graph. Another line of investigation attempts to use observational data
to learn a qualitative description of the domain called a Markov equivalence
class, which is the collection of causal graphs that share the same set of
observed features. In this paper, we marry both approaches and study the
problem of causal identification from an equivalence class, represented by a
partial ancestral graph (PAG). We start by deriving a set of graphical
properties of PAGs that are carried over to its induced subgraphs. We then
develop an algorithm to compute the effect of an arbitrary set of variables on
an arbitrary outcome set. We show that the algorithm is strictly more powerful
than the current state of the art found in the literature.
TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition
Authors: Xiyang Dai, Bharat Singh, Joe Yue-Hei Ng, Larry S. Davis
Date: 2018-12-14 00:00:00
Tasks: Action Recognition, Temporal Action Localization

We present Temporal Aggregation Network (TAN) which decomposes 3D
convolutions into spatial and temporal aggregation blocks. By stacking spatial
and temporal convolutions repeatedly, TAN forms a deep hierarchical
representation for capturing spatio-temporal information in videos. Since we do
not apply 3D convolutions in each layer but only apply temporal aggregation
blocks once after each spatial downsampling layer in the network, we
significantly reduce the model complexity. The use of dilated convolutions at
different resolutions of the network helps in aggregating multi-scale
spatio-temporal information efficiently. Experiments show that our model is
well suited for dense multi-label action recognition, which is a challenging
sub-topic of action recognition that requires predicting multiple action labels
in each frame. We outperform state-of-the-art methods by 5% and 3% on the
Charades and Multi-THUMOS dataset respectively.
Inter-sentence Relation Extraction for Associating Biological Context with Events in Biomedical Texts
Authors: Enrique Noriega-Atala, Paul D. Hein, Shraddha S. Thumsi, Zechy Wong, Xia Wang, Clayton T. Morrison
Date: 2018-12-14 00:00:00
Tasks: Relation, Relation Extraction, Sentence, Vocal Bursts Type Prediction

We present an analysis of the problem of identifying biological context and
associating it with biochemical events in biomedical texts. This constitutes a
non-trivial, inter-sentential relation extraction task. We focus on biological
context as descriptions of the species, tissue type and cell type that are
associated with biochemical events. We describe the properties of an annotated
corpus of context-event relations and present and evaluate several classifiers
for context-event association trained on syntactic, distance and frequency
features.
Learning Latent Subspaces in Variational Autoencoders
Authors: Jack Klys, Jake Snell, Richard Zemel
Date: 2018-12-14 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Attribute

Variational autoencoders (VAEs) are widely used deep generative models
capable of learning unsupervised latent representations of data. Such
representations are often difficult to interpret or control. We consider the
problem of unsupervised learning of features correlated to specific labels in a
dataset. We propose a VAE-based generative model which we show is capable of
extracting features correlated to binary labels in the data and structuring it
in a latent subspace which is easy to interpret. Our model, the Conditional
Subspace VAE (CSVAE), uses mutual information minimization to learn a
low-dimensional latent subspace associated with each label that can easily be
inspected and independently manipulated. We demonstrate the utility of the
learned representations for attribute manipulation tasks on both the Toronto
Face and CelebA datasets.
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Authors: Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou, Juntang Zhuang, Pamela Ventola, James S. Duncan
Date: 2018-12-14 00:00:00
Tasks: Deep Learning, Feature Importance

Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical
to help explain ASD and predict or monitor treatment outcomes. Toward this end,
deep learning classifiers have recently been used for identifying ASD from
functional magnetic resonance imaging (fMRI) with higher accuracy than
traditional learning strategies. However, a key challenge with deep learning
models is understanding just what image features the network is using, which
can in turn be used to define the biomarkers. Current methods extract
biomarkers, i.e., important features, by looking at how the prediction changes
if "ignoring" one feature at a time. In this work, we go beyond looking at only
individual features by using Shapley value explanation (SVE) from cooperative
game theory. Cooperative game theory is advantageous here because it directly
considers the interaction between features and can be applied to any machine
learning method, making it a novel, more accurate way of determining
instance-wise biomarker importance from deep learning models. A barrier to
using SVE is its computational complexity: $2^N$ given $N$ features. We
explicitly reduce the complexity of SVE computation by two approaches based on
the underlying graph structure of the input data: 1) only consider the
centralized coalition of each feature; 2) a hierarchical pipeline which first
clusters features into small communities, then applies SVE in each community.
Monte Carlo approximation can be used for large permutation sets. We first
validate our methods on the MNIST dataset and compare to human perception.
Next, to insure plausibility of our biomarker results, we train a Random Forest
(RF) to classify ASD/control subjects from fMRI and compare SVE results to
standard RF-based feature importance. Finally, we show initial results on
ranked fMRI biomarkers using SVE on a deep learning classifier for the
ASD/control dataset.
Bootstrapping Conversational Agents With Weak Supervision
Authors: Neil Mallinar, Abhishek Shah, Rajendra Ugrani, Ayush Gupta, Manikandan Gurusankar, Tin Kam Ho, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, Robert Yates, Chris Desmarais, Blake McGregor
Date: 2018-12-14 00:00:00

Many conversational agents in the market today follow a standard bot
development framework which requires training intent classifiers to recognize
user input. The need to create a proper set of training examples is often the
bottleneck in the development process. In many occasions agent developers have
access to historical chat logs that can provide a good quantity as well as
coverage of training examples. However, the cost of labeling them with tens to
hundreds of intents often prohibits taking full advantage of these chat logs.
In this paper, we present a framework called \textit{search, label, and
propagate} (SLP) for bootstrapping intents from existing chat logs using weak
supervision. The framework reduces hours to days of labeling effort down to
minutes of work by using a search engine to find examples, then relies on a
data programming approach to automatically expand the labels. We report on a
user study that shows positive user feedback for this new approach to build
conversational agents, and demonstrates the effectiveness of using data
programming for auto-labeling. While the system is developed for training
conversational agents, the framework has broader application in significantly
reducing labeling effort for training text classifiers.
Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting
Authors: Yaodong Yang, Alisa Kolesnikova, Stefan Lessmann, Tiejun Ma, Ming-Chien Sung, Johnnie E. V. Johnson
Date: 2018-12-14 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Feature Engineering, Management

The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies typical modeling challenges faced in risk and behavior forecasting. Conventional machine learning requires data that is representative of the feature-target relationship and relies on the often costly development, maintenance, and revision of handcrafted features. Consequently, modeling highly variable, heterogeneous patterns such as trader behavior is challenging. Deep learning promises a remedy. Learning hierarchical distributed representations of the data in an automatic manner (e.g. risk taking behavior), it uncovers generative features that determine the target (e.g., trader's profitability), avoids manual feature engineering, and is more robust toward change (e.g. dynamic market conditions). The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks.