pwc-archive/papers-with-abstracts huggingface dataset:
Gated Attentive-Autoencoder for Content-Aware Recommendation
Authors: Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, Xue Liu
Date: 2018-12-07 00:00:00
Tasks: Product Recommendation, Recommendation Systems

The rapid growth of Internet services and mobile devices provides an
excellent opportunity to satisfy the strong demand for the personalized item or
product recommendation. However, with the tremendous increase of users and
items, personalized recommender systems still face several challenging
problems: (1) the hardness of exploiting sparse implicit feedback; (2) the
difficulty of combining heterogeneous data. To cope with these challenges, we
propose a gated attentive-autoencoder (GATE) model, which is capable of
learning fused hidden representations of items' contents and binary ratings,
through a neural gating structure. Based on the fused representations, our
model exploits neighboring relations between items to help infer users'
preferences. In particular, a word-level and a neighbor-level attention module
are integrated with the autoencoder. The word-level attention learns the item
hidden representations from items' word sequences, while favoring informative
words by assigning larger attention weights. The neighbor-level attention
learns the hidden representation of an item's neighborhood by considering its
neighbors in a weighted manner. We extensively evaluate our model with several
state-of-the-art methods and different validation metrics on four real-world
datasets. The experimental results not only demonstrate the effectiveness of
our model on top-N recommendation but also provide interpretable results
attributed to the attention modules.
Use of neural networks for the identification of new z>=3.6 QSOs from FIRST-SDSS DR5
Authors: R. Carballo, J. I. Gonzalez-Serrano, C. R. Benn, F. Jimenez-Lujan
Date: 2008-09-03 00:00:00

We aim to obtain a complete sample of redshift > 3.6 radio QSOs from FIRST
sources having star-like counterparts in the SDSS DR5 photometric survey
(r<=20.2). We found that simple supervised neural networks, trained on sources
with SDSS spectra, and using optical photometry and radio data, are very
effective for identifying high-z QSOs without spectra. The technique yields a
completeness of 96 per cent and an efficiency of 62 per cent. Applying the
trained networks to 4415 sources without DR5 spectra we found 58 z>=3.6 QSO
candidates. We obtained spectra of 27 of them, and 17 are confirmed as high-z
QSOs. Spectra of 13 additional candidates from the literature and from SDSS DR6
revealed 7 more z>=3.6 QSOs, giving and overall efficiency of 60 per cent. None
of the non-candidates with spectra from NED or DR6 is a z>=3.6 QSO,
consistently with a high completeness. The initial sample of z>=3.6 QSOs is
increased from 52 to 76, i.e. by a factor 1.46. From the new identifications
and candidates we estimate an incompleteness of SDSS for the spectroscopic
classification of FIRST 3.6<=z<=4.6 QSOs of 15 percent for r<=20.2.
Metrics for Explainable AI: Challenges and Prospects
Authors: Robert R. Hoffman, Shane T. Mueller, Gary Klein, Jordan Litman
Date: 2018-12-11 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

The question addressed in this paper is: If we present to a user an AI system
that explains how it works, how do we know whether the explanation works and
the user has achieved a pragmatic understanding of the AI? In other words, how
do we know that an explanainable AI system (XAI) is any good? Our focus is on
the key concepts of measurement. We discuss specific methods for evaluating:
(1) the goodness of explanations, (2) whether users are satisfied by
explanations, (3) how well users understand the AI systems, (4) how curiosity
motivates the search for explanations, (5) whether the user's trust and
reliance on the AI are appropriate, and finally, (6) how the human-XAI work
system performs. The recommendations we present derive from our integration of
extensive research literatures and our own psychometric evaluations.
Deep Anomaly Detection with Outlier Exposure
Authors: Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
Date: 2018-12-11 00:00:00
Proceeding: ICLR 2019 5
Tasks: Anomaly Detection, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

It is important to detect anomalous inputs when deploying machine learning
systems. The use of larger and more complex inputs in deep learning magnifies
the difficulty of distinguishing between anomalous and in-distribution
examples. At the same time, diverse image and text data are available in
enormous quantities. We propose leveraging these data to improve deep anomaly
detection by training anomaly detectors against an auxiliary dataset of
outliers, an approach we call Outlier Exposure (OE). This enables anomaly
detectors to generalize and detect unseen anomalies. In extensive experiments
on natural language processing and small- and large-scale vision tasks, we find
that Outlier Exposure significantly improves detection performance. We also
observe that cutting-edge generative models trained on CIFAR-10 may assign
higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to
mitigate this issue. We also analyze the flexibility and robustness of Outlier
Exposure, and identify characteristics of the auxiliary dataset that improve
performance.
DeepV2D: Video to Depth with Differentiable Structure from Motion
Authors: Zachary Teed, Jia Deng
Date: 2018-12-11 00:00:00
Proceeding: ICLR 2020 1
Tasks: 3D Scene Reconstruction, Depth Estimation, Motion Estimation, Optical Flow Estimation, Stereo Matching Hand

We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric principles governing image formation. We compose a collection of classical geometric algorithms, which are converted into trainable modules and combined into an end-to-end differentiable architecture. DeepV2D interleaves two stages: motion estimation and depth estimation. During inference, motion and depth estimation are alternated and converge to accurate depth. Code is available https://github.com/princeton-vl/DeepV2D.
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Authors: Biye Jiang, David M. Chan, Tianhao Zhang, John F. Canny
Date: 2018-12-11 00:00:00
Tasks: Diagnostic

The internal states of most deep neural networks are difficult to interpret,
which makes diagnosis and debugging during training challenging. Activation
maximization methods are widely used, but lead to multiple optima and are hard
to interpret (appear noise-like) for complex neurons. Image-based methods use
maximally-activating image regions which are easier to interpret, but do not
provide pixel-level insight into why the neuron responds to them. In this work
we introduce an MCMC method: Langevin Dynamics Activation Maximization (LDAM),
which is designed for diagnostic visualization. LDAM provides two affordances
in combination: the ability to explore the set of maximally activating
pre-images, and the ability to trade-off interpretability and pixel-level
accuracy using a GAN-style discriminator as a regularizer. We present case
studies on MNIST, CIFAR and ImageNet datasets exploring these trade-offs.
Finally we show that diagnostic visualization using LDAM leads to a novel
insight into the parameter averaging method for deep net training.
Adversarial Framing for Image and Video Classification
Authors: Konrad Zolna, Michal Zajac, Negar Rostamzadeh, Pedro O. Pinheiro
Date: 2018-12-11 00:00:00
Tasks: Classification, General Classification, Video Classification

Neural networks are prone to adversarial attacks. In general, such attacks deteriorate the quality of the input by either slightly modifying most of its pixels, or by occluding it with a patch. In this paper, we propose a method that keeps the image unchanged and only adds an adversarial framing on the border of the image. We show empirically that our method is able to successfully attack state-of-the-art methods on both image and video classification problems. Notably, the proposed method results in a universal attack which is very fast at test time. Source code can be found at https://github.com/zajaczajac/adv_framing .
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport
Authors: Adarsh Subbaswamy, Peter Schulam, Suchi Saria
Date: 2018-12-11 00:00:00

Classical supervised learning produces unreliable models when training and
target distributions differ, with most existing solutions requiring samples
from the target domain. We propose a proactive approach which learns a
relationship in the training domain that will generalize to the target domain
by incorporating prior knowledge of aspects of the data generating process that
are expected to differ as expressed in a causal selection diagram.
Specifically, we remove variables generated by unstable mechanisms from the
joint factorization to yield the Surgery Estimator---an interventional
distribution that is invariant to the differences across environments. We prove
that the surgery estimator finds stable relationships in strictly more
scenarios than previous approaches which only consider conditional
relationships, and demonstrate this in simulated experiments. We also evaluate
on real world data for which the true causal diagram is unknown, performing
competitively against entirely data-driven approaches.
Bounding the Error From Reference Set Kernel Maximum Mean Discrepancy
Authors: Alexander Cloninger
Date: 2018-12-11 00:00:00

In this paper, we bound the error induced by using a weighted skeletonization
of two data sets for computing a two sample test with kernel maximum mean
discrepancy. The error is quantified in terms of the speed in which heat
diffuses from those points to the rest of the data, as well as how at the
weights on the reference points are, and gives a non-asymptotic,
non-probabilistic bound. The result ties into the problem of the eigenvector
triple product, which appears in a number of important problems. The error
bound also suggests an optimization scheme for choosing the best set of
reference points and weights. The method is tested on a several two sample test
examples.
Simulation Study on a New Peer Review Approach
Authors: Albert Steppi, Jinchan Qu, Minjing Tao, Tingting Zhao, Xiaodong Pang, Jinfeng Zhang
Date: 2018-06-11 00:00:00
Tasks: Decision Making

The increasing volume of scientific publications and grant proposals has generated an unprecedentedly high workload to scientific communities. Consequently, review quality has been decreasing and review outcomes have become less correlated with the real merits of the papers and proposals. A novel distributed peer review (DPR) approach has recently been proposed to address these issues. The new approach assigns principal investigators (PIs) who submitted proposals (or papers) to the same program as reviewers. Each PI reviews and ranks a small number (such as seven) of other PIs' proposals. The individual rankings are then used to estimate a global ranking of all proposals using the Modified Borda Count (MBC). In this study, we perform simulation studies to investigate several parameters important for the decision making when adopting this new approach. We also propose a new method called Concordance Index-based Global Ranking (CIGR) to estimate global ranking from individual rankings. An efficient simulated annealing algorithm is designed to search the optimal Concordance Index (CI). Moreover, we design a new balanced review assignment procedure, which can result in significantly better performance for both MBC and CIGR methods. We found that CIGR performs better than MBC when the review quality is relatively high. As review quality and review difficulty are tightly correlated, we constructed a boundary in the space of review quality vs review difficulty that separates the CIGR-superior and MBC-superior regions. Finally, we propose a multi-stage DPR strategy based on CIGR, which has the potential to substantially improve the overall review performance while reducing the review workload.
Doubly Bayesian Optimization
Authors: Alexander Lavin
Date: 2018-12-11 00:00:00
Tasks: Bayesian Optimization, Probabilistic Programming

Probabilistic programming systems enable users to encode model structure and
naturally reason about uncertainties, which can be leveraged towards improved
Bayesian optimization (BO) methods. Here we present a probabilistic program
embedding of BO that is capable of addressing main issues such as problematic
domains (noisy, non-smooth, high-dimensional) and the neglected
inner-optimization. Not only can we utilize programmable structure to
incorporate domain knowledge to aid optimization, but dealing with
uncertainties and implementing advanced BO techniques become trivial, crucial
for use in practice (particularly for non-experts). We demonstrate the efficacy
of the approach on optimization benchmarks and a real-world drug development
scenario.
Grounded Human-Object Interaction Hotspots from Video
Authors: Tushar Nagarajan, Christoph Feichtenhofer, Kristen Grauman
Date: 2018-12-11 00:00:00
Proceeding: ICCV 2019 10
Tasks: Human-Object Interaction Detection, Object, Object Recognition, Semantic Segmentation, Video-to-image Affordance Grounding

Learning how to interact with objects is an important step towards embodied
visual intelligence, but existing techniques suffer from heavy supervision or
sensing requirements. We propose an approach to learn human-object interaction
"hotspots" directly from video. Rather than treat affordances as a manually
supervised semantic segmentation task, our approach learns about interactions
by watching videos of real human behavior and anticipating afforded actions.
Given a novel image or video, our model infers a spatial hotspot map indicating
how an object would be manipulated in a potential interaction-- even if the
object is currently at rest. Through results with both first and third person
video, we show the value of grounding affordances in real human-object
interactions. Not only are our weakly supervised hotspots competitive with
strongly supervised affordance methods, but they can also anticipate object
interaction for novel object categories.
Controlling Covariate Shift using Balanced Normalization of Weights
Authors: Aaron Defazio, Léon Bottou
Date: 2018-12-11 00:00:00
Proceeding: ICLR 2019 5

We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs. The proposed technique keeps the contribution of positive and negative weights to the layer output balanced. We validate our method on a set of standard benchmarks including CIFAR-10/100, SVHN and ILSVRC 2012 ImageNet.
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
Authors: Aaron Defazio, Léon Bottou
Date: 2018-12-11 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Learning

The application of stochastic variance reduction to optimization has shown remarkable recent theoretical and practical success. The applicability of these techniques to the hard non-convex optimization problems encountered during training of modern deep neural networks is an open problem. We show that naive application of the SVRG technique and related approaches fail, and explore why.
Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation
Authors: Shenhao Wang, Qingyi Wang, Jinhua Zhao
Date: 2018-12-11 00:00:00
Tasks: Discrete Choice Models

While deep neural networks (DNNs) have been increasingly applied to choice analysis showing high predictive power, it is unclear to what extent researchers can interpret economic information from DNNs. This paper demonstrates that DNNs can provide economic information as complete as classical discrete choice models (DCMs). The economic information includes choice predictions, choice probabilities, market shares, substitution patterns of alternatives, social welfare, probability derivatives, elasticities, marginal rates of substitution (MRS), and heterogeneous values of time (VOT). Unlike DCMs, DNNs can automatically learn the utility function and reveal behavioral patterns that are not prespecified by domain experts. However, the economic information obtained from DNNs can be unreliable because of the three challenges associated with the automatic learning capacity: high sensitivity to hyperparameters, model non-identification, and local irregularity. To demonstrate the strength and challenges of DNNs, we estimated the DNNs using a stated preference survey, extracted the full list of economic information from the DNNs, and compared them with those from the DCMs. We found that the economic information either aggregated over trainings or population is more reliable than the disaggregate information of the individual observations or trainings, and that even simple hyperparameter searching can significantly improve the reliability of the economic information extracted from the DNNs. Future studies should investigate other regularizations and DNN architectures, better optimization algorithms, and robust DNN training methods to address DNNs' three challenges, to provide more reliable economic information from DNN-based choice models.
The Impact of Quantity of Training Data on Recognition of Eating Gestures
Authors: Yiru Shen, Eric Muth, Adam Hoover
Date: 2018-12-11 00:00:00

This paper considers the problem of recognizing eating gestures by tracking
wrist motion. Eating gestures can have large variability in motion depending on
the subject, utensil, and type of food or beverage being consumed. Previous
works have shown viable proofs-of-concept of recognizing eating gestures in
laboratory settings with small numbers of subjects and food types, but it is
unclear how well these methods would work if tested on a larger population in
natural settings. As more subjects, locations and foods are tested, a larger
amount of motion variability could cause a decrease in recognition accuracy. To
explore this issue, this paper describes the collection and annotation of
51,614 eating gestures taken by 269 subjects eating a meal in a cafeteria.
Experiments are described that explore the complexity of hidden Markov models
(HMMs) and the amount of training data needed to adequately capture the motion
variability across this large data set. Results found that HMMs needed a
complexity of 13 states and 5 Gaussians to reach a plateau in accuracy,
signifying that a minimum of 65 samples per gesture type are needed. Results
also found that 500 training samples per gesture type were needed to identify
the point of diminishing returns in recognition accuracy. Overall, the findings
provide evidence that the size a data set typically used to demonstrate a
laboratory proofs-of-concept may not be sufficiently large enough to capture
all the motion variability that could be expected in transitioning to
deployment with a larger population. Our data set, which is 1-2 orders of
magnitude larger than all data sets tested in previous works, is being made
publicly available.
Multi-objective evolutionary algorithms for quantum circuit discovery
Authors: Václav Potoček, Alan P. Reynolds, Alessandro Fedrizzi, David W. Corne
Date: 2018-12-11 00:00:00
Tasks: Evolutionary Algorithms

Quantum hardware continues to advance, yet finding new quantum algorithms -
quantum software - remains a challenge, with classically trained computer
programmers having little intuition of how computational tasks may be performed
in the quantum realm. As such, the idea of developing automated tools for
algorithm development is even more appealing for quantum computing than for
classical. Here we develop a robust, multi-objective evolutionary search
strategy to design quantum circuits 'from scratch', by combining and
parameterizing a task-generic library of quantum circuit elements. When applied
to 'ab initio' design of quantum circuits for the input/output mapping
requirements of the quantum Fourier transform and Grover's search algorithm, it
finds textbook circuit designs, along with alternative structures that achieve
the same functionality. Exploiting its multi-objective nature, the discovery
algorithm can trade off performance measures such as accuracy, circuit width or
depth, gate count, or implementability - a crucial requirement for
first-generation quantum processors and applications.
Semi-supervised dual graph regularized dictionary learning
Authors: Khanh-Hung Tran, Fred-Maurice Ngole-Mboula, Jean-Luc Starck
Date: 2018-12-11 00:00:00
Tasks: Dictionary Learning

In this paper, we propose a semi-supervised dictionary learning method that
uses both the information in labelled and unlabelled data and jointly trains a
linear classifier embedded on the sparse codes. The manifold structure of the
data in the sparse code space is preserved using the same approach as the
Locally Linear Embedding method (LLE). This enables one to enforce the
predictive power of the unlabelled data sparse codes. We show that our approach
provides significant improvements over other methods. The results can be
further improved by training a simple nonlinear classifier as SVM on the sparse
codes.
Coconditional Autoencoding Adversarial Networks for Chinese Font Feature Learning
Authors: Zhizhan Zheng, Feiyun Zhang
Date: 2018-12-11 00:00:00

In this work, we propose a novel framework named Coconditional Autoencoding
Adversarial Networks (CocoAAN) for Chinese font learning, which jointly learns
a generation network and two encoding networks of different feature domains
using an adversarial process. The encoding networks map the glyph images into
style and content features respectively via the pairwise substitution
optimization strategy, and the generation network maps these two kinds of
features to glyph samples. Together with a discriminative network conditioned
on the extracted features, our framework succeeds in producing
realistic-looking Chinese glyph images flexibly. Unlike previous models relying
on the complex segmentation of Chinese components or strokes, our model can
"parse" structures in an unsupervised way, through which the content feature
representation of each character is captured. Experiments demonstrate our
framework has a powerful generalization capacity to other unseen fonts and
characters.
Data Strategies for Fleetwide Predictive Maintenance
Authors: David Noever
Date: 2018-12-11 00:00:00
Tasks: Feature Importance, regression

For predictive maintenance, we examine one of the largest public datasets for
machine failures derived along with their corresponding precursors as error
rates, historical part replacements, and sensor inputs. To simplify the time
and accuracy comparison between 27 different algorithms, we treat the imbalance
between normal and failing states with nominal under-sampling. We identify 3
promising regression and discriminant algorithms with both higher accuracy
(96%) and twenty-fold faster execution times than previous work. Because
predictive maintenance success hinges on input features prior to prediction, we
provide a methodology to rank-order feature importance and show that for this
dataset, error counts prove more predictive than scheduled maintenance might
imply solely based on more traditional factors such as machine age or last
replacement times.