pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Variational Inference: Three arguments for deriving new Posteriors
Authors: Jeremias Knoblauch, Jack Jewson, Theodoros Damoulas
Date: 2019-04-03 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, Variational Inference

We advocate an optimization-centric view on and introduce a novel generalization of Bayesian inference. Our inspiration is the representation of Bayes' rule as infinite-dimensional optimization problem (Csiszar, 1975; Donsker and Varadhan; 1975, Zellner; 1988). First, we use it to prove an optimality result of standard Variational Inference (VI): Under the proposed view, the standard Evidence Lower Bound (ELBO) maximizing VI posterior is preferable to alternative approximations of the Bayesian posterior. Next, we argue for generalizing standard Bayesian inference. The need for this arises in situations of severe misalignment between reality and three assumptions underlying standard Bayesian inference: (1) Well-specified priors, (2) well-specified likelihoods, (3) the availability of infinite computing power. Our generalization addresses these shortcomings with three arguments and is called the Rule of Three (RoT). We derive it axiomatically and recover existing posteriors as special cases, including the Bayesian posterior and its approximation by standard VI. In contrast, approximations based on alternative ELBO-like objectives violate the axioms. Finally, we study a special case of the RoT that we call Generalized Variational Inference (GVI). GVI posteriors are a large and tractable family of belief distributions specified by three arguments: A loss, a divergence and a variational family. GVI posteriors have appealing properties, including consistency and an interpretation as approximate ELBO. The last part of the paper explores some attractive applications of GVI in popular machine learning models, including robustness and more appropriate marginals. After deriving black box inference schemes for GVI posteriors, their predictive performance is investigated on Bayesian Neural Networks and Deep Gaussian Processes, where GVI can comprehensively improve upon existing methods.
Interpreting Adversarial Examples by Activation Promotion and Suppression
Authors: Kaidi Xu, Sijia Liu, Gaoyuan Zhang, Mengshu Sun, Pu Zhao, Quanfu Fan, Chuang Gan, Xue Lin
Date: 2019-04-03 00:00:00
Tasks: Adversarial Robustness

It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers. However, interpretability of these perturbations is less explored in the literature. This work aims to better understand the roles of adversarial perturbations and provide visual explanations from pixel, image and network perspectives. We show that adversaries have a promotion-suppression effect (PSE) on neurons' activations and can be primarily categorized into three types: i) suppression-dominated perturbations that mainly reduce the classification score of the true label, ii) promotion-dominated perturbations that focus on boosting the confidence of the target label, and iii) balanced perturbations that play a dual role in suppression and promotion. We also provide image-level interpretability of adversarial examples. This links PSE of pixel-level perturbations to class-specific discriminative image regions localized by class activation mapping (Zhou et al. 2016). Further, we examine the adversarial effect through network dissection (Bau et al. 2017), which offers concept-level interpretability of hidden units. We show that there exists a tight connection between the units' sensitivity to adversarial attacks and their interpretability on semantic concepts. Lastly, we provide some new insights from our interpretation to improve the adversarial robustness of networks.
Estimating Chlorophyll a Concentrations of Several Inland Waters with Hyperspectral Data and Machine Learning Models
Authors: Philipp M. Maier, Sina Keller
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning, regression

Water is a key component of life, the natural environment and human health.
For monitoring the conditions of a water body, the chlorophyll a concentration
can serve as a proxy for nutrients and oxygen supply. In situ measurements of
water quality parameters are often time-consuming, expensive and limited in
areal validity. Therefore, we apply remote sensing techniques. During field
campaigns, we collected hyperspectral data with a spectrometer and in situ
measured chlorophyll a concentrations of 13 inland water bodies with different
spectral characteristics. One objective of this study is to estimate
chlorophyll a concentrations of these inland waters by applying three machine
learning regression models: Random Forest, Support Vector Machine and an
Artificial Neural Network. Additionally, we simulate four different
hyperspectral resolutions of the spectrometer data to investigate the effects
on the estimation performance. Furthermore, the application of first order
derivatives of the spectra is evaluated in turn to the regression performance.
This study reveals the potential of combining machine learning approaches and
remote sensing data for inland waters. Each machine learning model achieves an
R2-score between 80 % to 90 % for the regression on chlorophyll a
concentrations. The random forest model benefits clearly from the applied
derivatives of the spectra. In further studies, we will focus on the
application of machine learning models on spectral satellite data to enhance
the area-wide estimation of chlorophyll a concentration for inland waters.
Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions
Authors: Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman
Date: 2019-04-03 00:00:00
Tasks: regression, Symbolic Regression

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, i.e., the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.
Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review
Authors: Qinbing Fu, Hongxin Wang, Cheng Hu, Shigang Yue
Date: 2019-04-03 00:00:00

Motion perception is a critical capability determining a variety of aspects
of insects' life, including avoiding predators, foraging and so forth. A good
number of motion detectors have been identified in the insects' visual
pathways. Computational modelling of these motion detectors has not only been
providing effective solutions to artificial intelligence, but also benefiting
the understanding of complicated biological visual systems. These biological
mechanisms through millions of years of evolutionary development will have
formed solid modules for constructing dynamic vision systems for future
intelligent machines. This article reviews the computational motion perception
models originating from biological research of insects' visual systems in the
literature. These motion perception models or neural networks comprise the
looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in
locusts, the translation sensitive neural systems of direction selective
neurons (DSNs) in fruit flies, bees and locusts, as well as the small target
motion detectors (STMDs) in dragonflies and hover flies. We also review the
applications of these models to robots and vehicles. Through these modelling
studies, we summarise the methodologies that generate different direction and
size selectivity in motion perception. At last, we discuss about multiple
systems integration and hardware realisation of these bio-inspired motion
perception models.
Automated Fact Checking in the News Room
Authors: Sebastião Miranda, David Nogueira, Afonso Mendes, Andreas Vlachos, Andrew Secker, Rebecca Garrett, Jeff Mitchel, Zita Marinho
Date: 2019-04-03 00:00:00
Tasks: Articles, Fact Checking, Misinformation

Fact checking is an essential task in journalism; its importance has been
highlighted due to recently increased concerns and efforts in combating
misinformation. In this paper, we present an automated fact-checking platform
which given a claim, it retrieves relevant textual evidence from a document
collection, predicts whether each piece of evidence supports or refutes the
claim, and returns a final verdict. We describe the architecture of the system
and the user interface, focusing on the choices made to improve its
user-friendliness and transparency. We conduct a user study of the
fact-checking platform in a journalistic setting: we integrated it with a
collection of news articles and provide an evaluation of the platform using
feedback from journalists in their workflow. We found that the predictions of
our platform were correct 58\% of the time, and 59\% of the returned evidence
was relevant.
A Large-Scale Comparison of Historical Text Normalization Systems
Authors: Marcel Bollmann
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decoder, Machine Translation, Text Normalization, Translation

There is no consensus on the state-of-the-art approach to historical text
normalization. Many techniques have been proposed, including rule-based
methods, distance metrics, character-based statistical machine translation, and
neural encoder--decoder models, but studies have used different datasets,
different evaluation methods, and have come to different conclusions. This
paper presents the largest study of historical text normalization done so far.
We critically survey the existing literature and report experiments on eight
languages, comparing systems spanning all categories of proposed normalization
techniques, analysing the effect of training data quantity, and using different
evaluation methods. The datasets and scripts are made publicly available.
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
Authors: Hao Chen, Ilaria Chillotti, Yihe Dong, Oxana Poburinnaya, Ilya Razenshteyn, M. Sadegh Riazi
Date: 2019-04-03 00:00:00
Tasks: Clustering, Face Recognition, Recommendation Systems

The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and receives the response in which case the query and response are revealed to the service provider. Such data disclosures are unacceptable in several scenarios due to the sensitivity of data and/or privacy laws. In this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's query and the search result confidential. SANNS comprises two protocols: an optimized linear scan and a protocol based on a novel sublinear time clustering-based algorithm. We prove the security of both protocols in the standard semi-honest model. The protocols are built upon several state-of-the-art cryptographic primitives such as lattice-based additively homomorphic encryption, distributed oblivious RAM, and garbled circuits. We provide several contributions to each of these primitives which are applicable to other secure computation tasks. Both of our protocols rely on a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from $O(n + k^2)$ comparators. We have implemented our proposed system and performed extensive experimental results on four datasets in two different computation environments, demonstrating more than $18-31\times$ faster response time compared to optimally implemented protocols from the prior work. Moreover, SANNS is the first work that scales to the database of 10 million entries, pushing the limit by more than two orders of magnitude.
OpBerg: Discovering causal sentences using optimal alignments
Authors: Justin Wood, Nicholas J. Matiasz, Alcino J. Silva, William Hsu, Alexej Abyzov, Wei Wang
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning

The biological literature is rich with sentences that describe causal
relations. Methods that automatically extract such sentences can help
biologists to synthesize the literature and even discover latent relations that
had not been articulated explicitly. Current methods for extracting causal
sentences are based on either machine learning or a predefined database of
causal terms. Machine learning approaches require a large set of labeled
training data and can be susceptible to noise. Methods based on predefined
databases are limited by the quality of their curation and are unable to
capture new concepts or mistakes in the input. We address these challenges by
adapting and improving a method designed for a seemingly unrelated problem:
finding alignments between genomic sequences. This paper presents a novel and
outperforming method for extracting causal relations from text by aligning the
part-of-speech representations of an input set with that of known causal
sentences. Our experiments show that when applied to the task of finding causal
sentences in biological literature, our method improves on the accuracy of
other methods in a computationally efficient manner.
Super accurate low latency object detection on a surveillance UAV
Authors: Maarten Vandersteegen, Kristof Vanbeeck, Toon goedeme
Date: 2019-04-03 00:00:00
Tasks: Object, object-detection, Object Detection, Object Tracking

Drones have proven to be useful in many industry segments such as security
and surveillance, where e.g. on-board real-time object tracking is a necessity
for autonomous flying guards. Tracking and following suspicious objects is
therefore required in real-time on limited hardware. With an object detector in
the loop, low latency becomes extremely important. In this paper, we propose a
solution to make object detection for UAVs both fast and super accurate. We
propose a multi-dataset learning strategy yielding top eye-sky object detection
accuracy. Our model generalizes well on unseen data and can cope with different
flying heights, optically zoomed-in shots and different viewing angles. We
apply optimization steps such that we achieve minimal latency on embedded
on-board hardware by fusing layers, quantizing calculations to 16-bit floats
and 8-bit integers, with negligible loss in accuracy. We validate on NVIDIA's
Jetson TX2 and Jetson Xavier platforms where we achieve a speed-wise
performance boost of more than 10x.
Unsupervised Progressive Learning and the STAM Architecture
Authors: James Smith, Cameron Taylor, Seth Baer, Constantine Dovrolis
Date: 2019-04-03 00:00:00
Tasks: Clustering, Continual Learning, Novelty Detection, Online Clustering, Representation Learning

We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) architecture. Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, forgetting outliers, and storing only prototypical features rather than specific examples. We evaluate STAM representations using clustering and classification tasks. While there are no existing learning scenarios that are directly comparable to UPL, we compare the STAM architecture with two recent continual learning models, Memory Aware Synapses (MAS) and Gradient Episodic Memories (GEM), after adapting them in the UPL setting.
Jointly Extracting and Compressing Documents with Summary State Representations
Authors: Afonso Mendes, Shashi Narayan, Sebastião Miranda, Zita Marinho, André F. T. Martins, Shay B. Cohen
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Extractive Summarization, Text Summarization

We present a new neural model for text summarization that first extracts
sentences from a document and then compresses them. The proposed model offers a
balance that sidesteps the difficulties in abstractive methods while generating
more concise summaries than extractive methods. In addition, our model
dynamically determines the length of the output summary based on the gold
summaries it observes during training and does not require length constraints
typical to extractive summarization. The model achieves state-of-the-art
results on the CNN/DailyMail and Newsroom datasets, improving over current
extractive and abstractive methods. Human evaluations demonstrate that our
model generates concise and informative summaries. We also make available a new
dataset of oracle compressive summaries derived automatically from the
CNN/DailyMail reference summaries.
Stochastic Blockmodels with Edge Information
Authors: Guy W. Cole, Sinead A. Williamson
Date: 2019-04-03 00:00:00

Stochastic blockmodels allow us to represent networks in terms of a latent
community structure, often yielding intuitions about the underlying social
structure. Typically, this structure is inferred based only on a binary network
representing the presence or absence of interactions between nodes, which
limits the amount of information that can be extracted from the data. In
practice, many interaction networks contain much more information about the
relationship between two nodes. For example, in an email network, the volume of
communication between two users and the content of that communication can give
us information about both the strength and the nature of their relationship.
  In this paper, we propose the Topic Blockmodel, a stochastic blockmodel that
uses a count-based topic model to capture the interaction modalities within and
between latent communities. By explicitly incorporating information sent
between nodes in our network representation, we are able to address questions
of interest in real-world situations, such as predicting recipients for an
email message or inferring the content of an unopened email. Further, by
considering topics associated with a pair of communities, we are better able to
interpret the nature of each community and the manner in which it interacts
with other communities.
Deep Landscape Features for Improving Vector-borne Disease Prediction
Authors: Nabeel Abdur Rehman, Umar Saif, Rumi Chunara
Date: 2019-04-03 00:00:00
Tasks: Disease Prediction, Prediction

The global population at risk of mosquito-borne diseases such as dengue,
yellow fever, chikungunya and Zika is expanding. Infectious disease models
commonly incorporate environmental measures like temperature and precipitation.
Given increasing availability of high-resolution satellite imagery, here we
consider including landscape features from satellite imagery into infectious
disease prediction models. To do so, we implement a Convolutional Neural
Network (CNN) model trained on Imagenet data and labelled landscape features in
satellite data from London. We then incorporate landscape features from
satellite image data from Pakistan, labelled using the CNN, in a well-known
Susceptible-Infectious-Recovered epidemic model, alongside dengue case data
from 2012-2016 in Pakistan. We study improvement of the prediction model for
each of the individual landscape features, and assess the feasibility of using
image labels from a different place. We find that incorporating
satellite-derived landscape features can improve prediction of outbreaks, which
is important for proactive and strategic surveillance and control programmes.
Subword-Level Language Identification for Intra-Word Code-Switching
Authors: Manuel Mager, Özlem Çetinoğlu, Katharina Kann
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Identification

Language identification for code-switching (CS), the phenomenon of
alternating between two or more languages in conversations, has traditionally
been approached under the assumption of a single language per token. However,
if at least one language is morphologically rich, a large number of words can
be composed of morphemes from more than one language (intra-word CS). In this
paper, we extend the language identification task to the subword-level, such
that it includes splitting mixed words while tagging each part with a language
ID. We further propose a model for this task, which is based on a segmental
recurrent neural network. In experiments on a new Spanish--Wixarika dataset and
on an adapted German--Turkish dataset, our proposed model performs slightly
better than or roughly on par with our best baseline, respectively. Considering
only mixed words, however, it strongly outperforms all baselines.
Hybrid Cosine Based Convolutional Neural Networks
Authors: Adrià Ciurana, Albert Mosella-Montoro, Javier Ruiz-Hidalgo
Date: 2019-04-03 00:00:00
Tasks: General Classification

Convolutional neural networks (CNNs) have demonstrated their capability to
solve different kind of problems in a very huge number of applications.
However, CNNs are limited for their computational and storage requirements.
These limitations make difficult to implement these kind of neural networks on
embedded devices such as mobile phones, smart cameras or advanced driving
assistance systems. In this paper, we present a novel layer named Hybrid Cosine
Based Convolution that replaces standard convolutional layers using cosine
basis to generate filter weights. The proposed layers provide several
advantages: faster convergence in training, the receptive field can be
increased at no cost and substantially reduce the number of parameters. We
evaluate our proposed layers on three competitive classification tasks where
our proposed layers can achieve similar (and in some cases better) performances
than VGG and ResNet architectures.
D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Authors: Zhengping Che, Guangyu Li, Tracy Li, Bo Jiang, Xuefeng Shi, Xinsheng Zhang, Ying Lu, Guobin Wu, Yan Liu, Jieping Ye
Date: 2019-04-03 00:00:00
Tasks: Diversity

Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models. We propose D$^2$-City, a large-scale comprehensive collection of dashcam videos collected by vehicles on DiDi's platform. D$^2$-City contains more than 10000 video clips which deeply reflect the diversity and complexity of real-world traffic scenarios in China. We also provide bounding boxes and tracking annotations of 12 classes of objects in all frames of 1000 videos and detection annotations on keyframes for the remainder of the videos. Compared with existing datasets, D$^2$-City features data in varying weather, road, and traffic conditions and a huge amount of elaborate detection and tracking annotations. By bringing a diverse set of challenging cases to the community, we expect the D$^2$-City dataset will advance the perception and related areas of intelligent driving.