pwc-archive/papers-with-abstracts huggingface dataset:
DeepTriangle: A Deep Learning Approach to Loss Reserving
Authors: Kevin Kuo
Date: 2018-04-24 00:00:00
Tasks: Deep Learning, Feature Engineering

We propose a novel approach for loss reserving based on deep neural networks. The approach allows for joint modeling of paid losses and claims outstanding, and incorporation of heterogeneous inputs. We validate the models on loss reserving data across lines of business, and show that they improve on the predictive accuracy of existing stochastic methods. The models require minimal feature engineering and expert input, and can be automated to produce forecasts more frequently than manual workflows.
Efficient Online Portfolio with Logarithmic Regret
Authors: Haipeng Luo, Chen-Yu Wei, Kai Zheng
Date: 2018-05-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Management

We study the decades-old problem of online portfolio management and propose
the first algorithm with logarithmic regret that is not based on Cover's
Universal Portfolio algorithm and admits much faster implementation.
Specifically Universal Portfolio enjoys optimal regret $\mathcal{O}(N\ln T)$
for $N$ financial instruments over $T$ rounds, but requires log-concave
sampling and has a large polynomial running time. Our algorithm, on the other
hand, ensures a slightly larger but still logarithmic regret of
$\mathcal{O}(N^2(\ln T)^4)$, and is based on the well-studied Online Mirror
Descent framework with a novel regularizer that can be implemented via standard
optimization methods in time $\mathcal{O}(TN^{2.5})$ per round. The regret of
all other existing works is either polynomial in $T$ or has a potentially
unbounded factor such as the inverse of the smallest price relative.
Designing communication systems via iterative improvement: error correction coding with Bayes decoder and codebook optimized for source symbol error
Authors: Chai Wah Wu
Date: 2018-05-18 00:00:00
Tasks: Decoder

In most error correction coding (ECC) frameworks, the typical error metric is the bit error rate (BER) which measures the number of bit errors. For this metric, the positions of the bits are not relevant to the decoding, and in many noise models, not relevant to the BER either. In many applications this is unsatisfactory as typically all bits are not equal and have different significance. We consider the problem of bit error correction and mitigation where bits in different positions have different importance. For error correction, we look at ECC from a Bayesian perspective and introduce Bayes estimators with general loss functions to take into account the bit significance. We propose ECC schemes that optimize this error metric. As the problem is highly nonlinear, traditional ECC construction techniques are not applicable. Using exhaustive search is cost prohibitive, and thus we use iterative improvement search techniques to find good codebooks. We optimize both general codebooks and linear codes. We provide numerical experiments to show that they can be superior to classical linear block codes such as Hamming codes and decoding methods such as minimum distance decoding. For error mitigation, we study the case where ECC is not possible or not desirable, but significance aware encoding of information is still beneficial in reducing the average error. We propose a novel number presentation format suitable for emerging storage media where the noise magnitude is unknown and possibly large and show that it has lower mean error than the traditional number format.
RF-PUF: Enhancing IoT Security through Authentication of Wireless Nodes using In-situ Machine Learning
Authors: Baibhab Chatterjee, Debayan Das, Shovan Maity, Shreyas Sen
Date: 2018-05-03 00:00:00
Tasks: BIG-bench Machine Learning

Traditional authentication in radio-frequency (RF) systems enable secure data
communication within a network through techniques such as digital signatures
and hash-based message authentication codes (HMAC), which suffer from key
recovery attacks. State-of-the-art IoT networks such as Nest also use Open
Authentication (OAuth 2.0) protocols that are vulnerable to cross-site-recovery
forgery (CSRF), which shows that these techniques may not prevent an adversary
from copying or modeling the secret IDs or encryption keys using invasive, side
channel, learning or software attacks. Physical unclonable functions (PUF), on
the other hand, can exploit manufacturing process variations to uniquely
identify silicon chips which makes a PUF-based system extremely robust and
secure at low cost, as it is practically impossible to replicate the same
silicon characteristics across dies. Taking inspiration from human
communication, which utilizes inherent variations in the voice signatures to
identify a certain speaker, we present RF- PUF: a deep neural network-based
framework that allows real-time authentication of wireless nodes, using the
effects of inherent process variation on RF properties of the wireless
transmitters (Tx), detected through in-situ machine learning at the receiver
(Rx) end. The proposed method utilizes the already-existing asymmetric RF
communication framework and does not require any additional circuitry for PUF
generation or feature extraction. Simulation results involving the process
variations in a standard 65 nm technology node, and features such as LO offset
and I-Q imbalance detected with a neural network having 50 neurons in the
hidden layer indicate that the framework can distinguish up to 4800
transmitters with an accuracy of 99.9% (~ 99% for 10,000 transmitters) under
varying channel conditions, and without the need for traditional preambles.
Incept-N: A Convolutional Neural Network based Classification Approach for Predicting Nationality from Facial Features
Authors: Masum Shah Junayed, Afsana Ahsan Jeny, Nafis Neehal
Date: 2018-05-18 00:00:00
Tasks: General Classification

The nationality of a human being is a well-known identifying characteristic
used for every major authentication purpose in every country. Albeit advances
in the application of Artificial Intelligence and Computer Vision in different
aspects, its contribution to this specific security procedure is yet to be
cultivated. With a goal to successfully applying computer vision techniques to
predict the nationality of a person based on his facial features, we have
proposed this novel method and have achieved an average of 93.6% accuracy with
very low misclassification rate.
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks
Authors: Chaim Baskin, Eli Schwartz, Evgenii Zheltonozhskii, Natan Liss, Raja Giryes, Alex M. Bronstein, Avi Mendelson
Date: 2018-04-29 00:00:00
Tasks: Quantization

We present a novel method for neural network quantization that emulates a
non-uniform $k$-quantile quantizer, which adapts to the distribution of the
quantized parameters. Our approach provides a novel alternative to the existing
uniform quantization techniques for neural networks. We suggest to compare the
results as a function of the bit-operations (BOPS) performed, assuming a
look-up table availability for the non-uniform case. In this setup, we show the
advantages of our strategy in the low computational budget regime. While the
proposed solution is harder to implement in hardware, we believe it sets a
basis for new alternatives to neural networks quantization.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
Authors: Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley
Date: 2018-02-22 00:00:00
Tasks: Data Augmentation, Translation

We introduce tensor field neural networks, which are locally equivariant to
3D rotations, translations, and permutations of points at every layer. 3D
rotation equivariance removes the need for data augmentation to identify
features in arbitrary orientations. Our network uses filters built from
spherical harmonics; due to the mathematical consequences of this filter
choice, each layer accepts as input (and guarantees as output) scalars,
vectors, and higher-order tensors, in the geometric sense of these terms. We
demonstrate the capabilities of tensor field networks with tasks in geometry,
physics, and chemistry.
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
Authors: Benjamin Guedj, Le Li
Date: 2018-05-18 00:00:00
Tasks: Dimensionality Reduction

When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls. Principal curves act as a nonlinear generalization of PCA and the present paper proposes a novel algorithm to automatically and sequentially learn principal curves from data streams. We show that our procedure is supported by regret bounds with optimal sublinear remainder terms. A greedy local search implementation (called \texttt{slpc}, for Sequential Learning Principal Curves) that incorporates both sleeping experts and multi-armed bandit ingredients is presented, along with its regret computation and performance on synthetic and real-life data.
Computing Kantorovich-Wasserstein Distances on $d$-dimensional histograms using $(d+1)$-partite graphs
Authors: Gennaro Auricchio, Federico Bassetti, Stefano Gualandi, Marco Veneroni
Date: 2018-05-18 00:00:00
Proceeding: NeurIPS 2018

This paper presents a novel method to compute the exact
Kantorovich-Wasserstein distance between a pair of $d$-dimensional histograms
having $n$ bins each. We prove that this problem is equivalent to an
uncapacitated minimum cost flow problem on a $(d+1)$-partite graph with
$(d+1)n$ nodes and $dn^{\frac{d+1}{d}}$ arcs, whenever the cost is separable
along the principal $d$-dimensional directions. We show numerically the
benefits of our approach by computing the Kantorovich-Wasserstein distance of
order 2 among two sets of instances: gray scale images and $d$-dimensional
biomedical histograms. On these types of instances, our approach is competitive
with state-of-the-art optimal transport algorithms.
Predictive Uncertainty Estimation via Prior Networks
Authors: Andrey Malinin, Mark Gales
Date: 2018-02-28 00:00:00
Proceeding: NeurIPS 2018 12

Estimating how uncertain an AI system is in its predictions is important to
improve the safety of such systems. Uncertainty in predictive can result from
uncertainty in model parameters, irreducible data uncertainty and uncertainty
due to distributional mismatch between the test and training data
distributions. Different actions might be taken depending on the source of the
uncertainty so it is important to be able to distinguish between them.
Recently, baseline tasks and metrics have been defined and several practical
methods to estimate uncertainty developed. These methods, however, attempt to
model uncertainty due to distributional mismatch either implicitly through
model uncertainty or as data uncertainty. This work proposes a new framework
for modeling predictive uncertainty called Prior Networks (PNs) which
explicitly models distributional uncertainty. PNs do this by parameterizing a
prior distribution over predictive distributions. This work focuses on
uncertainty for classification and evaluates PNs on the tasks of identifying
out-of-distribution (OOD) samples and detecting misclassification on the MNIST
dataset, where they are found to outperform previous methods. Experiments on
synthetic and MNIST and CIFAR-10 data show that unlike previous non-Bayesian
methods PNs are able to distinguish between data and distributional
uncertainty.
Wasserstein Measure Coresets
Authors: Sebastian Claici, Aude Genevay, Justin Solomon
Date: 2018-05-18 00:00:00
Tasks: Bayesian Inference, Clustering

The proliferation of large data sets and Bayesian inference techniques motivates demand for better data sparsification. Coresets provide a principled way of summarizing a large dataset via a smaller one that is guaranteed to match the performance of the full data set on specific problems. Classical coresets, however, neglect the underlying data distribution, which is often continuous. We address this oversight by introducing Wasserstein measure coresets, an extension of coresets which by definition takes into account generalization. Our formulation of the problem, which essentially consists in minimizing the Wasserstein distance, is solvable via stochastic gradient descent. This yields an algorithm which simply requires sample access to the data distribution and is able to handle large data streams in an online manner. We validate our construction for inference and clustering.
Learning to Collaborate for User-Controlled Privacy
Authors: Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Guillermo Sapiro
Date: 2018-05-18 00:00:00

It is becoming increasingly clear that users should own and control their
data. Utility providers are also becoming more interested in guaranteeing data
privacy. As such, users and utility providers should collaborate in data
privacy, a paradigm that has not yet been developed in the privacy research
community. We introduce this concept and present explicit architectures where
the user controls what characteristics of the data she/he wants to share and
what she/he wants to keep private. This is achieved by collaborative learning a
sensitization function, either a deterministic or a stochastic one, that
retains valuable information for the utility tasks but it also eliminates
necessary information for the privacy ones. As illustration examples, we
implement them using a plug-and-play approach, where no algorithm is changed at
the system provider end, and an adversarial approach, where minor re-training
of the privacy inferring engine is allowed. In both cases the learned
sanitization function keeps the data in the original domain, thereby allowing
the system to use the same algorithms it was using before for both original and
privatized data. We show how we can maintain utility while fully protecting
private information if the user chooses to do so, even when the first is harder
than the second, as in the case here illustrated of identity detection while
hiding gender.
A syllogistic system for propositions with intermediate quantifiers
Authors: Pasquale Iero, Allan Third, Paul Piwek
Date: 2018-05-18 00:00:00
Tasks: Form, Negation

This paper describes a formalism that subsumes Peterson's intermediate
quantifier syllogistic system, and extends the ideas by van Eijck on
Aristotle's logic. Syllogisms are expressed in a concise form making use of and
extending the Monotonicity Calculus. Contradictory and contrary relationships
are added so that deduction can derive propositions expressing a form of
negation.
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Authors: Andrew L. Beam, Benjamin Kompa, Allen Schmaltz, Inbar Fried, Griffin Weber, Nathan P. Palmer, Xu Shi, Tianxi Cai, Isaac S. Kohane
Date: 2018-04-04 00:00:00
Tasks: Articles, Word Embeddings

Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of embeddings for medical concepts learned using an extremely large collection of multimodal medical data. Leaning on recent theoretical insights, we demonstrate how an insurance claims database of 60 million members, a collection of 20 million clinical notes, and 1.7 million full text biomedical journal articles can be combined to embed concepts into a common space, resulting in the largest ever set of embeddings for 108,477 medical concepts. To evaluate our approach, we present a new benchmark methodology based on statistical power specifically designed to test embeddings of medical concepts. Our approach, called cui2vec, attains state-of-the-art performance relative to previous methods in most instances. Finally, we provide a downloadable set of pre-trained embeddings for other researchers to use, as well as an online tool for interactive exploration of the cui2vec embeddings
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks
Authors: Sungjoon Choi, Sanghoon Hong, Kyungjae Lee, Sungbin Lim
Date: 2018-05-16 00:00:00
Proceeding: CVPR 2020 6
Tasks: Autonomous Driving, General Classification, regression, Weakly-supervised Learning

In this paper, we focus on weakly supervised learning with noisy training data for both classification and regression problems.We assume that the training outputs are collected from a mixture of a target and correlated noise distributions.Our proposed method simultaneously estimates the target distribution and the quality of each data which is defined as the correlation between the target and data generating distributions.The cornerstone of the proposed method is a Cholesky Block that enables modeling dependencies among mixture distributions in a differentiable manner where we maintain the distribution over the network weights.We first provide illustrative examples in both regression and classification tasks to show the effectiveness of the proposed method.Then, the proposed method is extensively evaluated in a number of experiments where we show that it constantly shows comparable or superior performances compared to existing baseline methods in the handling of noisy data.
Processing of missing data by neural networks
Authors: Marek Smieja, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, Przemysław Spurek
Date: 2018-05-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Imputation

We propose a general, theoretically justified mechanism for processing
missing data by neural networks. Our idea is to replace typical neuron's
response in the first hidden layer by its expected value. This approach can be
applied for various types of networks at minimal cost in their modification.
Moreover, in contrast to recent approaches, it does not require complete data
for training. Experimental results performed on different types of
architectures show that our method gives better results than typical imputation
strategies and other methods dedicated for incomplete data.
Online convex optimization for cumulative constraints
Authors: Jianjun Yuan, Andrew Lamperski
Date: 2018-02-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Form

We propose the algorithms for online convex optimization which lead to
cumulative squared constraint violations of the form
$\sum\limits_{t=1}^T\big([g(x_t)]_+\big)^2=O(T^{1-\beta})$, where
$\beta\in(0,1)$. Previous literature has focused on long-term constraints of
the form $\sum\limits_{t=1}^Tg(x_t)$. There, strictly feasible solutions can
cancel out the effects of violated constraints. In contrast, the new form
heavily penalizes large constraint violations and cancellation effects cannot
occur.
  Furthermore, useful bounds on the single step constraint violation
$[g(x_t)]_+$ are derived.
  For convex objectives, our regret bounds generalize existing bounds, and for
strongly convex objectives we give improved regret bounds.
  In numerical experiments, we show that our algorithm closely follows the
constraint boundary leading to low cumulative violation.
Robust Handling of Polysemy via Sparse Representations
Authors: Abhijit Mahabal, Dan Roth, Sid Mittal
Date: 2018-05-18 00:00:00
Proceeding: SEMEVAL 2018 6

Words are polysemous and multi-faceted, with many shades of meanings. We
suggest that sparse distributed representations are more suitable than other,
commonly used, (dense) representations to express these multiple facets, and
present Category Builder, a working system that, as we show, makes use of
sparse representations to support multi-faceted lexical representations. We
argue that the set expansion task is well suited to study these meaning
distinctions since a word may belong to multiple sets with a different reason
for membership in each. We therefore exhibit the performance of Category
Builder on this task, while showing that our representation captures at the
same time analogy problems such as "the Ganga of Egypt" or "the Voldemort of
Tolkien". Category Builder is shown to be a more expressive lexical
representation and to outperform dense representations such as Word2Vec in some
analogy classes despite being shown only two of the three input terms.
Controllable Abstractive Summarization
Authors: Angela Fan, David Grangier, Michael Auli
Date: 2017-11-14 00:00:00
Proceeding: WS 2018 7
Tasks: Abstractive Text Summarization, Document Summarization

Current models for document summarization disregard user preferences such as
the desired length, style, the entities that the user might be interested in,
or how much of the document the user has already read. We present a neural
summarization model with a simple but effective mechanism to enable users to
specify these high level attributes in order to control the shape of the final
summaries to better suit their needs. With user input, our system can produce
high quality summaries that follow user preferences. Without user input, we set
the control variables automatically. On the full text CNN-Dailymail dataset, we
outperform state of the art abstractive systems (both in terms of F1-ROUGE1
40.38 vs. 39.53 and human evaluation).
Batch Normalization in the final layer of generative networks
Authors: Sean Mullery, Paul F. Whelan
Date: 2018-05-18 00:00:00

Generative Networks have shown great promise in generating photo-realistic
images. Despite this, the theory surrounding them is still an active research
area. Much of the useful work with Generative networks rely on heuristics that
tend to produce good results. One of these heuristics is the advice not to use
Batch Normalization in the final layer of the generator network. Many of the
state-of-the-art generative network architectures use this heuristic, but the
reasons for doing so are inconsistent. This paper will show that this is not
necessarily a good heuristic and that Batch Normalization can be beneficial in
the final layer of the generator network either by placing it before the final
non-linear activation, usually a $tanh$ or replacing the final $tanh$
activation altogether with Batch Normalization and clipping. We show that this
can lead to the faster training of Generator networks by matching the generator
to the mean and standard deviation of the target distribution's image colour
values.
Algorithms for Estimating Trends in Global Temperature Volatility
Authors: Arash Khodadadi, Daniel J. McDonald
Date: 2018-05-18 00:00:00

Trends in terrestrial temperature variability are perhaps more relevant for
species viability than trends in mean temperature. In this paper, we develop
methodology for estimating such trends using multi-resolution climate data from
polar orbiting weather satellites. We derive two novel algorithms for
computation that are tailored for dense, gridded observations over both space
and time. We evaluate our methods with a simulation that mimics these data's
features and on a large, publicly available, global temperature dataset with
the eventual goal of tracking trends in cloud reflectance temperature
variability.