pwc-archive/papers-with-abstracts huggingface dataset:
Social Participation Ontology: community documentation, enhancements and use examples
Authors: Renato Fabbri, Henrique Parra Parra Filho, Rodrigo Bandeira de Luna, Ricardo Augusto Poppi Martins, Flor Karina Mamani Amanqui, Dilvan de Abreu Moreira, Osvaldo Novais de Oliveira Junior
Date: 2015-01-12 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Information Retrieval, Retrieval

Participatory democracy advances in virtually all governments and especially
in South America which exhibits a mixed culture and social predisposition. This
article presents the "Social Participation Ontology" (OPS from the Brazilian
name \emph{Ontologia de Participa\c{c}\~ao Social}) implemented in compliance
with the Web Ontology Language standard (OWL) for fostering social
participation, specially in virtual platforms. The entities and links of OPS
were defined based on an extensive collaboration of specialists. It is shown
that OPS is instrumental for information retrieval from the contents of the
portal, both in terms of the actors (at various levels) as well as mechanisms
and activities. Significantly, OPS is linked to other OWL ontologies as an
upper ontology and via FOAF and BFO as higher upper ontologies, which yields
sound organization and access of knowledge and data. In order to illustrate the
usefulness of OPS, we present results on ontological expansion and integration
with other ontologies and data. Ongoing work involves further adoption of OPS
by the official Brazilian federal portal for social participation and NGO s,
and further linkage to other ontologies for social participation.
Creation of an Annotated Corpus of Spanish Radiology Reports
Authors: Viviana Cotik, Darío Filippo, Roland Roller, Hans Uszkoreit, Feiyu Xu
Date: 2017-10-30 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Negation, Relation Extraction

This paper presents a new annotated corpus of 513 anonymized radiology
reports written in Spanish. Reports were manually annotated with entities,
negation and uncertainty terms and relations. The corpus was conceived as an
evaluation resource for named entity recognition and relation extraction
algorithms, and as input for the use of supervised methods. Biomedical
annotated resources are scarce due to confidentiality issues and associated
costs. This work provides some guidelines that could help other researchers to
undertake similar tasks.
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition
Authors: Neha Bhargava, Subhasis Chaudhuri
Date: 2017-10-30 00:00:00
Tasks: Activity Recognition

We present an integrated framework for simultaneous tracking, group detection
and multi-level activity recognition in crowd videos. Instead of solving these
problems independently and sequentially, we solve them together in a unified
framework to utilize the strong correlation that exists among individual
motion, groups, and activities. We explore the hierarchical structure hidden in
the video that connects individuals over time to produce tracks, connects
individuals to form groups and also connects groups together to form a crowd.
We show that estimation of this hidden structure corresponds to track
association and group detection. We estimate this hidden structure under a
linear programming formulation. The obtained graphical representation is
further explored to recognize the node values that corresponds to multi-level
activity recognition. This problem is solved under a structured SVM framework.
The results on publicly available dataset show very competitive performance at
all levels of granularity with the state-of-the-art batch processing methods
despite the proposed technique being an online (causal) one.
Continuous Authentication Using One-class Classifiers and their Fusion
Authors: Rajesh Kumar, Partha Pratim Kundu, Vir V. Phoha
Date: 2017-10-30 00:00:00

While developing continuous authentication systems (CAS), we generally assume
that samples from both genuine and impostor classes are readily available.
However, the assumption may not be true in certain circumstances. Therefore, we
explore the possibility of implementing CAS using only genuine samples.
Specifically, we investigate the usefulness of four one-class classifiers OCC
(elliptic envelope, isolation forest, local outliers factor, and one-class
support vector machines) and their fusion. The performance of these classifiers
was evaluated on four distinct behavioral biometric datasets, and compared with
eight multi-class classifiers (MCC). The results demonstrate that if we have
sufficient training data from the genuine user the OCC, and their fusion can
closely match the performance of the majority of MCC. Our findings encourage
the research community to use OCC in order to build CAS as they do not require
knowledge of impostor class during the enrollment process.
Deep Neural Networks Regularization for Structured Output Prediction
Authors: Soufiane Belharbi, Romain Hérault, Clément Chatelain, Sébastien Adam
Date: 2015-04-28 00:00:00
Tasks: Facial Landmark Detection, Prediction

A deep neural network model is a powerful framework for learning
representations. Usually, it is used to learn the relation $x \to y$ by
exploiting the regularities in the input $x$. In structured output prediction
problems, $y$ is multi-dimensional and structural relations often exist between
the dimensions. The motivation of this work is to learn the output dependencies
that may lie in the output data in order to improve the prediction accuracy.
Unfortunately, feedforward networks are unable to exploit the relations between
the outputs. In order to overcome this issue, we propose in this paper a
regularization scheme for training neural networks for these particular tasks
using a multi-task framework. Our scheme aims at incorporating the learning of
the output representation $y$ in the training process in an unsupervised
fashion while learning the supervised mapping function $x \to y$.
  We evaluate our framework on a facial landmark detection problem which is a
typical structured output task. We show over two public challenging datasets
(LFPW and HELEN) that our regularization scheme improves the generalization of
deep neural networks and accelerates their training. The use of unlabeled data
and label-only data is also explored, showing an additional improvement of the
results. We provide an opensource implementation
(https://github.com/sbelharbi/structured-output-ae) of our framework.
Semantic Code Repair using Neuro-Symbolic Transformation Networks
Authors: Jacob Devlin, Jonathan Uesato, Rishabh Singh, Pushmeet Kohli
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Code Repair

We study the problem of semantic code repair, which can be broadly defined as
automatically fixing non-syntactic bugs in source code. The majority of past
work in semantic code repair assumed access to unit tests against which
candidate repairs could be validated. In contrast, the goal here is to develop
a strong statistical model to accurately predict both bug locations and exact
fixes without access to information about the intended correct behavior of the
program. Achieving such a goal requires a robust contextual repair model, which
we train on a large corpus of real-world source code that has been augmented
with synthetically injected bugs. Our framework adopts a two-stage approach
where first a large set of repair candidates are generated by rule-based
processors, and then these candidates are scored by a statistical model using a
novel neural network architecture which we refer to as Share, Specialize, and
Compete. Specifically, the architecture (1) generates a shared encoding of the
source code using an RNN over the abstract syntax tree, (2) scores each
candidate repair using specialized network modules, and (3) then normalizes
these scores together so they can compete against one another in comparable
probability space. We evaluate our model on a real-world test set gathered from
GitHub containing four common categories of bugs. Our model is able to predict
the exact correct repair 41\% of the time with a single guess, compared to 13\%
accuracy for an attentional sequence-to-sequence model.
A Connection between Feed-Forward Neural Networks and Probabilistic Graphical Models
Authors: Dmitrij Schlesinger
Date: 2017-10-30 00:00:00
Tasks: General Classification, Image Segmentation, Semantic Segmentation

Two of the most popular modelling paradigms in computer vision are
feed-forward neural networks (FFNs) and probabilistic graphical models (GMs).
Various connections between the two have been studied in recent works, such as
e.g. expressing mean-field based inference in a GM as an FFN. This paper
establishes a new connection between FFNs and GMs. Our key observation is that
any FFN implements a certain approximation of a corresponding Bayesian network
(BN). We characterize various benefits of having this connection. In
particular, it results in a new learning algorithm for BNs. We validate the
proposed methods for a classification problem on CIFAR-10 dataset and for
binary image segmentation on Weizmann Horse dataset. We show that statistically
learned BNs improve performance, having at the same time essentially better
generalization capability, than their FFN counterparts.
Tensor network language model
Authors: Vasily Pestun, Yiannis Vlassopoulos
Date: 2017-10-27 00:00:00
Tasks: BIG-bench Machine Learning, Language Modeling, Language Modelling, model, Tensor Networks, Translation

We propose a new statistical model suitable for machine learning of systems
with long distance correlations such as natural languages. The model is based
on directed acyclic graph decorated by multi-linear tensor maps in the vertices
and vector spaces in the edges, called tensor network. Such tensor networks
have been previously employed for effective numerical computation of the
renormalization group flow on the space of effective quantum field theories and
lattice models of statistical mechanics. We provide explicit algebro-geometric
analysis of the parameter moduli space for tree graphs, discuss model
properties and applications such as statistical translation.
Named Entity Recognition in Twitter using Images and Text
Authors: Diego Esteves, Rafael Peres, Jens Lehmann, Giulio Napolitano
Date: 2017-10-30 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER) is an important subtask of information
extraction that seeks to locate and recognise named entities. Despite recent
achievements, we still face limitations with correctly detecting and
classifying entities, prominently in short and noisy text, such as Twitter. An
important negative aspect in most of NER approaches is the high dependency on
hand-crafted features and domain-specific knowledge, necessary to achieve
state-of-the-art results. Thus, devising models to deal with such
linguistically complex contexts is still challenging. In this paper, we propose
a novel multi-level architecture that does not rely on any specific linguistic
resource or encoded rule. Unlike traditional approaches, we use features
extracted from images and text to classify named entities. Experimental tests
against state-of-the-art NER for Twitter on the Ritter dataset present
competitive results (0.59 F-measure), indicating that this approach may lead
towards better NER models.
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Authors: Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, Sanja Fidler
Date: 2017-07-18 00:00:00
Tasks: Cross-Modal Retrieval, Image Retrieval, Retrieval, Structured Prediction, Visual Reasoning

We present a new technique for learning visual-semantic embeddings for
cross-modal retrieval. Inspired by hard negative mining, the use of hard
negatives in structured prediction, and ranking loss functions, we introduce a
simple change to common loss functions used for multi-modal embeddings. That,
combined with fine-tuning and use of augmented data, yields significant gains
in retrieval performance. We showcase our approach, VSE++, on MS-COCO and
Flickr30K datasets, using ablation studies and comparisons with existing
methods. On MS-COCO our approach outperforms state-of-the-art methods by 8.8%
in caption retrieval and 11.3% in image retrieval (at R@1).
Denoising random forests
Authors: Masaya Hibino, Akisato Kimura, Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi
Date: 2017-10-30 00:00:00
Tasks: Denoising

This paper proposes a novel type of random forests called a denoising random
forests that are robust against noises contained in test samples. Such
noise-corrupted samples cause serious damage to the estimation performances of
random forests, since unexpected child nodes are often selected and the leaf
nodes that the input sample reaches are sometimes far from those for a clean
sample. Our main idea for tackling this problem originates from a binary
indicator vector that encodes a traversal path of a sample in the forest. Our
proposed method effectively employs this vector by introducing denoising
autoencoders into random forests. A denoising autoencoder can be trained with
indicator vectors produced from clean and noisy input samples, and non-leaf
nodes where incorrect decisions are made can be identified by comparing the
input and output of the trained denoising autoencoder. Multiple traversal paths
with respect to the nodes with incorrect decisions caused by the noises can
then be considered for the estimation.
Auto-Differentiating Linear Algebra
Authors: Matthias Seeger, Asmus Hetzel, Zhenwen Dai, Eric Meissner, Neil D. Lawrence
Date: 2017-10-24 00:00:00
Tasks: Active Learning, Bayesian Optimization, CPU, Gaussian Processes, GPU

Development systems for deep learning (DL), such as Theano, Torch, TensorFlow, or MXNet, are easy-to-use tools for creating complex neural network models. Since gradient computations are automatically baked in, and execution is mapped to high performance hardware, these models can be trained end-to-end on large amounts of data. However, it is currently not easy to implement many basic machine learning primitives in these systems (such as Gaussian processes, least squares estimation, principal components analysis, Kalman smoothing), mainly because they lack efficient support of linear algebra primitives as differentiable operators. We detail how a number of matrix decompositions (Cholesky, LQ, symmetric eigen) can be implemented as differentiable operators. We have implemented these primitives in MXNet, running on CPU and GPU in single and double precision. We sketch use cases of these new operators, learning Gaussian process and Bayesian linear regression models, where we demonstrate very substantial reductions in implementation complexity and running time compared to previous codes. Our MXNet extension allows end-to-end learning of hybrid models, which combine deep neural networks (DNNs) with Bayesian concepts, with applications in advanced Gaussian process models, scalable Bayesian optimization, and Bayesian active learning.
LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
Authors: Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, Alexander M. Rush
Date: 2016-06-23 00:00:00

Recurrent neural networks, and in particular long short-term memory (LSTM)
networks, are a remarkably effective tool for sequence modeling that learn a
dense black-box hidden representation of their sequential input. Researchers
interested in better understanding these models have studied the changes in
hidden state representations over time and noticed some interpretable patterns
but also significant noise. In this work, we present LSTMVIS, a visual analysis
tool for recurrent neural networks with a focus on understanding these hidden
state dynamics. The tool allows users to select a hypothesis input range to
focus on local state changes, to match these states changes to similar patterns
in a large data set, and to align these results with structural annotations
from their domain. We show several use cases of the tool for analyzing specific
hidden state properties on dataset containing nesting, phrase structure, and
chord progressions, and demonstrate how the tool can be used to isolate
patterns for further statistical analysis. We characterize the domain, the
different stakeholders, and their goals and tasks.
Deriving Neural Architectures from Sequence and Graph Kernels
Authors: Tao Lei, Wengong Jin, Regina Barzilay, Tommi Jaakkola
Date: 2017-05-25 00:00:00
Proceeding: ICML 2017 8
Tasks: Graph Regression, Language Modeling, Language Modelling, regression

The design of neural architectures for structured objects is typically guided
by experimental insights rather than a formal process. In this work, we appeal
to kernels over combinatorial structures, such as sequences and graphs, to
derive appropriate neural operations. We introduce a class of deep recurrent
neural operations and formally characterize their associated kernel spaces. Our
recurrent modules compare the input to virtual reference objects (cf. filters
in CNN) via the kernels. Similar to traditional neural operations, these
reference objects are parameterized and directly optimized in end-to-end
training. We empirically evaluate the proposed class of neural architectures on
standard applications such as language modeling and molecular graph regression,
achieving state-of-the-art results across these applications.
Learning to solve inverse problems using Wasserstein loss
Authors: Jonas Adler, Axel Ringh, Ozan Öktem, Johan Karlsson
Date: 2017-10-30 00:00:00

We propose using the Wasserstein loss for training in inverse problems. In
particular, we consider a learned primal-dual reconstruction scheme for
ill-posed inverse problems using the Wasserstein distance as loss function in
the learning. This is motivated by miss-alignments in training data, which when
using standard mean squared error loss could severely degrade reconstruction
quality. We prove that training with the Wasserstein loss gives a
reconstruction operator that correctly compensates for miss-alignments in
certain cases, whereas training with the mean squared error gives a smeared
reconstruction. Moreover, we demonstrate these effects by training a
reconstruction algorithm using both mean squared error and optimal transport
loss for a problem in computerized tomography.
Open Set Logo Detection and Retrieval
Authors: Andras Tüzkö, Christian Herrmann, Daniel Manger, Jürgen Beyerer
Date: 2017-10-30 00:00:00
Tasks: Retrieval

Current logo retrieval research focuses on closed set scenarios. We argue
that the logo domain is too large for this strategy and requires an open set
approach. To foster research in this direction, a large-scale logo dataset,
called Logos in the Wild, is collected and released to the public. A typical
open set logo retrieval application is, for example, assessing the
effectiveness of advertisement in sports event broadcasts. Given a query sample
in shape of a logo image, the task is to find all further occurrences of this
logo in a set of images or videos. Currently, common logo retrieval approaches
are unsuitable for this task because of their closed world assumption. Thus, an
open set logo retrieval method is proposed in this work which allows searching
for previously unseen logos by a single query sample. A two stage concept with
separate logo detection and comparison is proposed where both modules are based
on task specific CNNs. If trained with the Logos in the Wild data, significant
performance improvements are observed, especially compared with
state-of-the-art closed set approaches.
Fast Linear Model for Knowledge Graph Embeddings
Authors: Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov
Date: 2017-10-30 00:00:00
Tasks: General Classification, Knowledge Base Completion, Knowledge Graph Embeddings, model, Question Answering

This paper shows that a simple baseline based on a Bag-of-Words (BoW)
representation learns surprisingly good knowledge graph embeddings. By casting
knowledge base completion and question answering as supervised classification
problems, we observe that modeling co-occurences of entities and relations
leads to state-of-the-art performance with a training time of a few minutes
using the open sourced library fastText.
Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming
Authors: Tadashi Kozuno, Eiji Uchibe, Kenji Doya
Date: 2017-10-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Approximate dynamic programming algorithms, such as approximate value
iteration, have been successfully applied to many complex reinforcement
learning tasks, and a better approximate dynamic programming algorithm is
expected to further extend the applicability of reinforcement learning to
various tasks. In this paper we propose a new, robust dynamic programming
algorithm that unifies value iteration, advantage learning, and dynamic policy
programming. We call it generalized value iteration (GVI) and its approximated
version, approximate GVI (AGVI). We show AGVI's performance guarantee, which
includes performance guarantees for existing algorithms, as special cases. We
discuss theoretical weaknesses of existing algorithms, and explain the
advantages of AGVI. Numerical experiments in a simple environment support
theoretical arguments, and suggest that AGVI is a promising alternative to
previous algorithms.
PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection
Authors: Wolfgang Fuhl, Thiago Santini, Gjergji Kasneci, Wolfgang Rosenstiel, Enkelejda Kasneci
Date: 2017-10-30 00:00:00
Tasks: CPU, Pupil Detection

Real-time, accurate, and robust pupil detection is an essential prerequisite
for pervasive video-based eye-tracking. However, automated pupil detection in
realworld scenarios has proven to be an intricate challenge due to fast
illumination changes, pupil occlusion, non-centered and off-axis eye recording,
as well as physiological eye characteristics. In this paper, we approach this
challenge through: I) a convolutional neural network (CNN) running in real time
on a single core, II) a novel computational intensive two stage CNN for
accuracy improvement, and III) a fast propability distribution based refinement
method as a practical alternative to II. We evaluate the proposed approaches
against the state-of-the-art pupil detection algorithms, improving the
detection rate up to ~9% percent points on average over all data sets (~7% on
one CPU core 7ms). This evaluation was performed on over 135,000 images: 94,000
images from the literature, and 41,000 new hand-labeled and challenging images
contributed by this work (v1.0).
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
Authors: Florian Dubost, Gerda Bortsova, Hieab Adams, Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
Date: 2017-05-22 00:00:00
Tasks: Lesion Detection, regression, Sensitivity

We propose a novel convolutional neural network for lesion detection from
weak labels. Only a single, global label per image - the lesion count - is
needed for training. We train a regression network with a fully convolutional
architecture combined with a global pooling layer to aggregate the 3D output
into a scalar indicating the lesion count. When testing on unseen images, we
first run the network to estimate the number of lesions. Then we remove the
global pooling layer to compute localization maps of the size of the input
image. We evaluate the proposed network on the detection of enlarged
perivascular spaces in the basal ganglia in MRI. Our method achieves a
sensitivity of 62% with on average 1.5 false positives per image. Compared with
four other approaches based on intensity thresholding, saliency and class maps,
our method has a 20% higher sensitivity.