pwc-archive/papers-with-abstracts huggingface dataset:
Copy mechanism and tailored training for character-based data-to-text generation
Authors: Marco Roberti, Giovanni Bonetta, Rossella Cancelliere, Patrick Gallinari
Date: 2019-04-26 00:00:00
Tasks: Data-to-Text Generation, Text Generation, Transfer Learning

In the last few years, many different methods have been focusing on using deep recurrent neural networks for natural language generation. The most widely used sequence-to-sequence neural methods are word-based: as such, they need a pre-processing step called delexicalization (conversely, relexicalization) to deal with uncommon or unknown words. These forms of processing, however, give rise to models that depend on the vocabulary used and are not completely neural. In this work, we present an end-to-end sequence-to-sequence model with attention mechanism which reads and generates at a character level, no longer requiring delexicalization, tokenization, nor even lowercasing. Moreover, since characters constitute the common "building blocks" of every text, it also allows a more general approach to text generation, enabling the possibility to exploit transfer learning for training. These skills are obtained thanks to two major features: (i) the possibility to alternate between the standard generation mechanism and a copy one, which allows to directly copy input facts to produce outputs, and (ii) the use of an original training pipeline that further improves the quality of the generated texts. We also introduce a new dataset called E2E+, designed to highlight the copying capabilities of character-based models, that is a modified version of the well-known E2E dataset used in the E2E Challenge. We tested our model according to five broadly accepted metrics (including the widely used BLEU), showing that it yields competitive performance with respect to both character-based and word-based approaches.
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning
Authors: Artur Souza, Leonardo B. Oliveira, Sabine Hollatz, Matt Feldman, Kunle Olukotun, James M. Holton, Aina E. Cohen, Luigi Nardi
Date: 2019-04-26 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, General Classification

Serial crystallography is the field of science that studies the structure and properties of crystals via diffraction patterns. In this paper, we introduce a new serial crystallography dataset comprised of real and synthetic images; the synthetic images are generated through the use of a simulator that is both scalable and accurate. The resulting dataset is called DiffraNet, and it is composed of 25,457 512x512 grayscale labeled images. We explore several computer vision approaches for classification on DiffraNet such as standard feature extraction algorithms associated with Random Forests and Support Vector Machines but also an end-to-end CNN topology dubbed DeepFreak tailored to work on this new dataset. All implementations are publicly available and have been fine-tuned using off-the-shelf AutoML optimization tools for a fair comparison. Our best model achieves 98.5% accuracy on synthetic images and 94.51% accuracy on real images. We believe that the DiffraNet dataset and its classification methods will have in the long term a positive impact in accelerating discoveries in many disciplines, including chemistry, geology, biology, materials science, metallurgy, and physics.
Online Learning Algorithms for Quaternion ARMA Model
Authors: Xiaokun Pu, Chun-Guang Li
Date: 2019-04-26 00:00:00
Tasks: model

In this paper, we address the problem of adaptive learning for autoregressive
moving average (ARMA) model in the quaternion domain. By transforming the
original learning problem into a full information optimization task without
explicit noise terms, and then solving the optimization problem using the
gradient descent and the Newton analogues, we obtain two online learning
algorithms for the quaternion ARMA. Furthermore, regret bound analysis
accounting for the specific properties of quaternion algebra is presented,
which proves that the performance of the online algorithms asymptotically
approaches that of the best quaternion ARMA model in hindsight.
Evaluating Recurrent Neural Network Explanations
Authors: Leila Arras, Ahmed Osman, Klaus-Robert Müller, Wojciech Samek
Date: 2019-04-26 00:00:00
Proceeding: WS 2019 8
Tasks: Negation, Sentence, Sentiment Analysis

Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network's decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular prediction. In previous works, some of these methods were not yet compared to one another, or were evaluated only qualitatively. We close this gap by systematically and quantitatively comparing these methods in different settings, namely (1) a toy arithmetic task which we use as a sanity check, (2) a five-class sentiment prediction of movie reviews, and besides (3) we explore the usefulness of word relevances to build sentence-level representations. Lastly, using the method that performed best in our experiments, we show how specific linguistic phenomena such as the negation in sentiment analysis reflect in terms of relevance patterns, and how the relevance visualization can help to understand the misclassification of individual samples.
Think Again Networks and the Delta Loss
Authors: Alexandre Salle, Marcelo Prates
Date: 2019-04-26 00:00:00
Tasks: Language Modelling

This short paper introduces an abstraction called Think Again Networks
(ThinkNet) which can be applied to any state-dependent function (such as a
recurrent neural network).
Producing Corpora of Medieval and Premodern Occitan
Authors: Jean-Baptiste Camps, Gilles Guilhem Couffignal
Date: 2019-04-26 00:00:00
Tasks: Lemmatization, Optical Character Recognition, Optical Character Recognition (OCR)

At a time when the quantity of - more or less freely - available data is
increasing significantly, thanks to digital corpora, editions or libraries, the
development of data mining tools or deep learning methods allows researchers to
build a corpus of study tailored for their research, to enrich their data and
to exploit them.Open optical character recognition (OCR) tools can be adapted
to old prints, incunabula or even manuscripts, with usable results, allowing
the rapid creation of textual corpora. The alternation of training and
correction phases makes it possible to improve the quality of the results by
rapidly accumulating raw text data. These can then be structured, for example
in XML/TEI, and enriched.The enrichment of the texts with graphic or linguistic
annotations can also be automated. These processes, known to linguists and
functional for modern languages, present difficulties for languages such as
Medieval Occitan, due in part to the absence of big enough lemmatized corpora.
Suggestions for the creation of tools adapted to the considerable spelling
variation of ancient languages will be presented, as well as experiments for
the lemmatization of Medieval and Premodern Occitan.These techniques open the
way for many exploitations. The much desired increase in the amount of
available quality texts and data makes it possible to improve digital philology
methods, if everyone takes the trouble to make their data freely available
online and reusable.By exposing different technical solutions and some
micro-analyses as examples, this paper aims to show part of what digital
philology can offer to researchers in the Occitan domain, while recalling the
ethical issues on which such practices are based.
Robustness Verification of Support Vector Machines
Authors: Francesco Ranzato, Marco Zanella
Date: 2019-04-26 00:00:00

We study the problem of formally verifying the robustness to adversarial
examples of support vector machines (SVMs), a major machine learning model for
classification and regression tasks. Following a recent stream of works on
formal robustness verification of (deep) neural networks, our approach relies
on a sound abstract version of a given SVM classifier to be used for checking
its robustness. This methodology is parametric on a given numerical abstraction
of real values and, analogously to the case of neural networks, needs neither
abstract least upper bounds nor widening operators on this abstraction. The
standard interval domain provides a simple instantiation of our abstraction
technique, which is enhanced with the domain of reduced affine forms, which is
an efficient abstraction of the zonotope abstract domain. This robustness
verification technique has been fully implemented and experimentally evaluated
on SVMs based on linear and nonlinear (polynomial and radial basis function)
kernels, which have been trained on the popular MNIST dataset of images and on
the recent and more challenging Fashion-MNIST dataset. The experimental results
of our prototype SVM robustness verifier appear to be encouraging: this
automated verification is fast, scalable and shows significantly high
percentages of provable robustness on the test set of MNIST, in particular
compared to the analogous provable robustness of neural networks.
A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis
Authors: Guojun Yin, Bin Liu, Huihui Zhu, Tao Gong, Nenghai Yu
Date: 2019-04-26 00:00:00
Tasks: Management, Multiple Object Tracking, Object, Object Tracking

Multiple-object tracking and behavior analysis have been the essential parts of surveillance video analysis for public security and urban management. With billions of surveillance video captured all over the world, multiple-object tracking and behavior analysis by manual labor are cumbersome and cost expensive. Due to the rapid development of deep learning algorithms in recent years, automatic object tracking and behavior analysis put forward an urgent demand on a large scale well-annotated surveillance video dataset that can reflect the diverse, congested, and complicated scenarios in real applications. This paper introduces an urban surveillance video dataset (USVD) which is by far the largest and most comprehensive. The dataset consists of 16 scenes captured in 7 typical outdoor scenarios: street, crossroads, hospital entrance, school gate, park, pedestrian mall, and public square. Over 200k video frames are annotated carefully, resulting in more than 3:7 million object bounding boxes and about 7:1 thousand trajectories. We further use this dataset to evaluate the performance of typical algorithms for multiple-object tracking and anomaly behavior analysis and explore the robustness of these methods in urban congested scenarios.
Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors
Authors: Anne Lauscher, Goran Glavaš
Date: 2019-04-26 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Cross-Lingual Transfer, Word Embeddings

Word embeddings have recently been shown to reflect many of the pronounced
societal biases (e.g., gender bias or racial bias). Existing studies are,
however, limited in scope and do not investigate the consistency of biases
across relevant dimensions like embedding models, types of texts, and different
languages. In this work, we present a systematic study of biases encoded in
distributional word vector spaces: we analyze how consistent the bias effects
are across languages, corpora, and embedding models. Furthermore, we analyze
the cross-lingual biases encoded in bilingual embedding spaces, indicative of
the effects of bias transfer encompassed in cross-lingual transfer of NLP
models. Our study yields some unexpected findings, e.g., that biases can be
emphasized or downplayed by different embedding models or that user-generated
content may be less biased than encyclopedic text. We hope our work catalyzes
bias research in NLP and informs the development of bias reduction techniques.
EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association
Authors: Michael Strecke, Jörg Stückler
Date: 2019-04-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: Multi-Object Tracking, Object, Object Tracking, Occlusion Handling

The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers. The representation and tracking of moving objects, however, has significant potential for applications in robotics or augmented reality. In this paper, we propose a novel approach to dynamic SLAM with dense object-level representations. We represent rigid objects in local volumetric signed distance function (SDF) maps, and formulate multi-object tracking as direct alignment of RGB-D images with the SDF representations. Our main novelty is a probabilistic formulation which naturally leads to strategies for data association and occlusion handling. We analyze our approach in experiments and demonstrate that our approach compares favorably with the state-of-the-art methods in terms of robustness and accuracy.
Factored Contextual Policy Search with Bayesian Optimization
Authors: Robert Pinsler, Peter Karkus, Andras Kupcsik, David Hsu, Wee Sun Lee
Date: 2019-04-26 00:00:00
Tasks: Active Learning, Bayesian Optimization, Position

Scarce data is a major challenge to scaling robot learning to truly complex
tasks, as we need to generalize locally learned policies over different task
contexts. Contextual policy search offers data-efficient learning and
generalization by explicitly conditioning the policy on a parametric context
space. In this paper, we further structure the contextual policy
representation. We propose to factor contexts into two components: target
contexts that describe the task objectives, e.g. target position for throwing a
ball; and environment contexts that characterize the environment, e.g. initial
position or mass of the ball. Our key observation is that experience can be
directly generalized over target contexts. We show that this can be easily
exploited in contextual policy search algorithms. In particular, we apply
factorization to a Bayesian optimization approach to contextual policy search
both in sampling-based and active learning settings. Our simulation results
show faster learning and better generalization in various robotic domains. See
our supplementary video: https://youtu.be/MNTbBAOufDY.
The Potential of Restarts for ProbSAT
Authors: Jan-Hendrik Lorenz, Julian Nickerl
Date: 2019-04-26 00:00:00

This work analyses the potential of restarts for probSAT, a quite successful
algorithm for k-SAT, by estimating its runtime distributions on random 3-SAT
instances that are close to the phase transition. We estimate an optimal
restart time from empirical data, reaching a potential speedup factor of 1.39.
Calculating restart times from fitted probability distributions reduces this
factor to a maximum of 1.30. A spin-off result is that the Weibull distribution
approximates the runtime distribution for over 93% of the used instances well.
A machine learning pipeline is presented to compute a restart time for a
fixed-cutoff strategy to exploit this potential. The main components of the
pipeline are a random forest for determining the distribution type and a neural
network for the distribution's parameters. ProbSAT performs statistically
significantly better than Luby's restart strategy and the policy without
restarts when using the presented approach. The structure is particularly
advantageous on hard problems.
Formal Verification of Decision-Tree Ensemble Model and Detection of its Violating-input-value Ranges
Authors: Naoto Sato, Hironobu Kuruma, Yuichiroh Nakagawa, Hideto Ogawa
Date: 2019-04-26 00:00:00

As one type of machine-learning model, a "decision-tree ensemble model"
(DTEM) is represented by a set of decision trees. A DTEM is mainly known to be
valid for structured data; however, like other machine-learning models, it is
difficult to train so that it returns the correct output value for any input
value. Accordingly, when a DTEM is used in regard to a system that requires
reliability, it is important to comprehensively detect input values that lead
to malfunctions of a system (failures) during development and take appropriate
measures. One conceivable solution is to install an input filter that controls
the input to the DTEM, and to use separate software to process input values
that may lead to failures. To develop the input filter, it is necessary to
specify the filtering condition of the input value that leads to the
malfunction of the system. Given that necessity, in this paper, we propose a
method for formally verifying a DTEM and, according to the result of the
verification, if an input value leading to a failure is found, extracting the
range in which such an input value exists. The proposed method can
comprehensively extract the range in which the input value leading to the
failure exists; therefore, by creating an input filter based on that range, it
is possible to prevent the failure occurring in the system. In this paper, the
algorithm of the proposed method is described, and the results of a case study
using a dataset of house prices are presented. On the basis of those results,
the feasibility of the proposed method is demonstrated, and its scalability is
evaluated.
Representation Similarity Analysis for Efficient Task taxonomy & Transfer Learning
Authors: Kshitij Dwivedi, Gemma Roig
Date: 2019-04-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Segmentation, Semantic Segmentation, Transfer Learning

Transfer learning is widely used in deep neural network models when there are
few labeled examples available. The common approach is to take a pre-trained
network in a similar task and finetune the model parameters. This is usually
done blindly without a pre-selection from a set of pre-trained models, or by
finetuning a set of models trained on different tasks and selecting the best
performing one by cross-validation. We address this problem by proposing an
approach to assess the relationship between visual tasks and their
task-specific models. Our method uses Representation Similarity Analysis (RSA),
which is commonly used to find a correlation between neuronal responses from
brain data and models. With RSA we obtain a similarity score among tasks by
computing correlations between models trained on different tasks. Our method is
efficient as it requires only pre-trained models, and a few images with no
further training. We demonstrate the effectiveness and efficiency of our method
for generating task taxonomy on Taskonomy dataset. We next evaluate the
relationship of RSA with the transfer learning performance on Taskonomy tasks
and a new task: Pascal VOC semantic segmentation. Our results reveal that
models trained on tasks with higher similarity score show higher transfer
learning performance. Surprisingly, the best transfer learning result for
Pascal VOC semantic segmentation is not obtained from the pre-trained model on
semantic segmentation, probably due to the domain differences, and our method
successfully selects the high performing models.
Landmark-Based Approaches for Goal Recognition as Planning
Authors: Ramon Fraga Pereira, Nir Oren, Felipe Meneguzzi
Date: 2019-04-26 00:00:00

The task of recognizing goals and plans from missing and full observations can be done efficiently by using automated planning techniques. In many applications, it is important to recognize goals and plans not only accurately, but also quickly. To address this challenge, we develop novel goal recognition approaches based on planning techniques that rely on planning landmarks. In automated planning, landmarks are properties (or actions) that cannot be avoided to achieve a goal. We show the applicability of a number of planning techniques with an emphasis on landmarks for goal and plan recognition tasks in two settings: (1) we use the concept of landmarks to develop goal recognition heuristics; and (2) we develop a landmark-based filtering method to refine existing planning-based goal and plan recognition approaches. These recognition approaches are empirically evaluated in experiments over several classical planning domains. We show that our goal recognition approaches yield not only accuracy comparable to (and often higher than) other state-of-the-art techniques, but also substantially faster recognition time over such techniques.
Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory
Authors: Chun-kit Yeung
Date: 2019-04-26 00:00:00
Tasks: Deep Learning, Knowledge Tracing

Deep learning based knowledge tracing model has been shown to outperform
traditional knowledge tracing model without the need for human-engineered
features, yet its parameters and representations have long been criticized for
not being explainable. In this paper, we propose Deep-IRT which is a synthesis
of the item response theory (IRT) model and a knowledge tracing model that is
based on the deep neural network architecture called dynamic key-value memory
network (DKVMN) to make deep learning based knowledge tracing explainable.
Specifically, we use the DKVMN model to process the student's learning
trajectory and estimate the student ability level and the item difficulty level
over time. Then, we use the IRT model to estimate the probability that a
student will answer an item correctly using the estimated student ability and
the item difficulty. Experiments show that the Deep-IRT model retains the
performance of the DKVMN model, while it provides a direct psychological
interpretation of both students and items.
Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments
Authors: Ramon Fraga Pereira, Nir Oren, Felipe Meneguzzi
Date: 2019-04-26 00:00:00

Assessing whether an agent has abandoned a goal or is actively pursuing it is important when multiple agents are trying to achieve joint goals, or when agents commit to achieving goals for each other. Making such a determination for a single goal by observing only plan traces is not trivial as agents often deviate from optimal plans for various reasons, including the pursuit of multiple goals or the inability to act optimally. In this article, we develop an approach based on domain independent heuristics from automated planning, landmarks, and fact partitions to identify sub-optimal action steps - with respect to a plan - within a plan execution trace. Such capability is very important in domains where multiple agents cooperate and delegate tasks among themselves, e.g. through social commitments, and need to ensure that a delegating agent can infer whether or not another agent is actually progressing towards a delegated task. We demonstrate how an agent can use our technique to determine - by observing a trace - whether an agent is honouring a commitment. We empirically show, for a number of representative domains, that our approach infers sub-optimal action steps with very high accuracy and detects commitment abandonment in nearly all cases.