pwc-archive/papers-with-abstracts huggingface dataset:
Domain Generalization with Domain-Specific Aggregation Modules
Authors: Antonio D'Innocente, Barbara Caputo
Date: 2018-09-28 00:00:00
Tasks: Domain Generalization

Visual recognition systems are meant to work in the real world. For this to
happen, they must work robustly in any visual domain, and not only on the data
used during training. Within this context, a very realistic scenario deals with
domain generalization, i.e. the ability to build visual recognition algorithms
able to work robustly in several visual domains, without having access to any
information about target data statistic. This paper contributes to this
research thread, proposing a deep architecture that maintains separated the
information about the available source domains data while at the same time
leveraging over generic perceptual information. We achieve this by introducing
domain-specific aggregation modules that through an aggregation layer strategy
are able to merge generic and specific information in an effective manner.
Experiments on two different benchmark databases show the power of our
approach, reaching the new state of the art in domain generalization.
Target-Independent Active Learning via Distribution-Splitting
Authors: Xiaofeng Cao, Ivor W. Tsang, Xiaofeng Xu, Guandong Xu
Date: 2018-09-28 00:00:00
Tasks: Active Learning

To reduce the label complexity in Agnostic Active Learning (A^2 algorithm), volume-splitting splits the hypothesis edges to reduce the Vapnik-Chervonenkis (VC) dimension in version space. However, the effectiveness of volume-splitting critically depends on the initial hypothesis and this problem is also known as target-dependent label complexity gap. This paper attempts to minimize this gap by introducing a novel notion of number density which provides a more natural and direct way to describe the hypothesis distribution than volume. By discovering the connections between hypothesis and input distribution, we map the volume of version space into the number density and propose a target-independent distribution-splitting strategy with the following advantages: 1) provide theoretical guarantees on reducing label complexity and error rate as volume-splitting; 2) break the curse of initial hypothesis; 3) provide model guidance for a target-independent AL algorithm in real AL tasks. With these guarantees, for AL application, we then split the input distribution into more near-optimal spheres and develop an application algorithm called Distribution-based A^2 (DA^2). Experiments further verify the effectiveness of the halving and querying abilities of DA^2. Contributions of this paper are as follows.
Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers
Authors: Yutong Ban, Xavier Alameda-Pineda, Laurent Girin, Radu Horaud
Date: 2018-09-28 00:00:00
Tasks: Bayesian Inference, Variational Inference, Visual Tracking

In this paper we address the problem of tracking multiple speakers via the fusion of visual and auditory information. We propose to exploit the complementary nature of these two modalities in order to accurately estimate smooth trajectories of the tracked persons, to deal with the partial or total absence of one of the modalities over short periods of time, and to estimate the acoustic status -- either speaking or silent -- of each tracked person along time. We propose to cast the problem at hand into a generative audio-visual fusion (or association) model formulated as a latent-variable temporal graphical model. This may well be viewed as the problem of maximizing the posterior joint distribution of a set of continuous and discrete latent variables given the past and current observations, which is intractable. We propose a variational inference model which amounts to approximate the joint distribution with a factorized distribution. The solution takes the form of a closed-form expectation maximization procedure. We describe in detail the inference algorithm, we evaluate its performance and we compare it with several baseline methods. These experiments show that the proposed audio-visual tracker performs well in informal meetings involving a time-varying number of people.
Overview of PicTropes, a film trope dataset
Authors: Rubén H. García-Ortega, Juan J. Merelo-Guervós, Pablo García Sánchez, Gad Pitaru
Date: 2018-09-28 00:00:00
Tasks: Descriptive

From the database DBTropes.org, we have created a dataset of films and the
tropes that they use, which we have called PicTropes. In this report we provide
the descriptive analysis and a further discussion on the dataset PicTropes: The
extracted features will help us decide the best values for a future
recommendation system and content generator, whereas the analysis of the
distribution functions that fit the best will help us interpret the relation
between the films and the tropes that were found inside them. Additionally, we
provide rankings of the top-25 tropes and films, which will help us discuss and
formulate questions to guide future extensions of the PicTropes dataset.
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
Authors: Sheng He, Lambert Schomaker
Date: 2018-09-28 00:00:00
Tasks: Multi-Task Learning, Open-Ended Question Answering

There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.
On Rational Entailment for Propositional Typicality Logic
Authors: Richard Booth, Giovanni Casini, Thomas Meyer, Ivan Varzinczak
Date: 2018-09-28 00:00:00
Tasks: Sentence

Propositional Typicality Logic (PTL) is a recently proposed logic, obtained by enriching classical propositional logic with a typicality operator capturing the most typical (alias normal or conventional) situations in which a given sentence holds. The semantics of PTL is in terms of ranked models as studied in the well-known KLM approach to preferential reasoning and therefore KLM-style rational consequence relations can be embedded in PTL. In spite of the non-monotonic features introduced by the semantics adopted for the typicality operator, the obvious Tarskian definition of entailment for PTL remains monotonic and is therefore not appropriate in many contexts. Our first important result is an impossibility theorem showing that a set of proposed postulates that at first all seem appropriate for a notion of entailment with regard to typicality cannot be satisfied simultaneously. Closer inspection reveals that this result is best interpreted as an argument for advocating the development of more than one type of PTL entailment. In the spirit of this interpretation, we investigate three different (semantic) versions of entailment for PTL, each one based on the definition of rational closure as introduced by Lehmann and Magidor for KLM-style conditionals, and constructed using different notions of minimality.
Deep learning systems as complex networks
Authors: Alberto Testolin, Michele Piccolini, Samir Suweis
Date: 2018-09-28 00:00:00
Tasks: Deep Learning, Natural Language Understanding, Object Recognition, Translation

Thanks to the availability of large scale digital datasets and massive
amounts of computational power, deep learning algorithms can learn
representations of data by exploiting multiple levels of abstraction. These
machine learning methods have greatly improved the state-of-the-art in many
challenging cognitive tasks, such as visual object recognition, speech
processing, natural language understanding and automatic translation. In
particular, one class of deep learning models, known as deep belief networks,
can discover intricate statistical structure in large data sets in a completely
unsupervised fashion, by learning a generative model of the data using
Hebbian-like learning mechanisms. Although these self-organizing systems can be
conveniently formalized within the framework of statistical mechanics, their
internal functioning remains opaque, because their emergent dynamics cannot be
solved analytically. In this article we propose to study deep belief networks
using techniques commonly employed in the study of complex networks, in order
to gain some insights into the structural and functional properties of the
computational graph resulting from the learning process.
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
Authors: Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chén, Maarten De Vos
Date: 2018-09-28 00:00:00
Tasks: General Classification, Sleep Stage Detection, Sleep Staging

Automatic sleep staging has been often treated as a simple classification
problem that aims at determining the label of individual target polysomnography
(PSG) epochs one at a time. In this work, we tackle the task as a
sequence-to-sequence classification problem that receives a sequence of
multiple epochs as input and classifies all of their labels at once. For this
purpose, we propose a hierarchical recurrent neural network named SeqSleepNet.
At the epoch processing level, the network consists of a filterbank layer
tailored to learn frequency-domain filters for preprocessing and an
attention-based recurrent layer designed for short-term sequential modelling.
At the sequence processing level, a recurrent layer placed on top of the
learned epoch-wise features for long-term modelling of sequential epochs. The
classification is then carried out on the output vectors at every time step of
the top recurrent layer to produce the sequence of output labels. Despite being
hierarchical, we present a strategy to train the network in an end-to-end
fashion. We show that the proposed network outperforms state-of-the-art
approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of
87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.
Depth Reconstruction of Translucent Objects from a Single Time-of-Flight Camera using Deep Residual Networks
Authors: Seongjong Song, Hyunjung Shim
Date: 2018-09-28 00:00:00

We propose a novel approach to recovering the translucent objects from a
single time-of-flight (ToF) depth camera using deep residual networks. When
recording the translucent objects using the ToF depth camera, their depth
values are severely contaminated due to complex light interactions with the
surrounding environment. While existing methods suggested new capture systems
or developed the depth distortion models, their solutions were less practical
because of strict assumptions or heavy computational complexity. In this paper,
we adopt the deep residual networks for modeling the ToF depth distortion
caused by translucency. To fully utilize both the local and semantic
information of objects, multi-scale patches are used to predict the depth
value. Based on the quantitative and qualitative evaluation on our benchmark
database, we show the effectiveness and robustness of the proposed algorithm.
Evidential community detection based on density peaks
Authors: Kuang Zhou, Quan Pan, Arnaud Martin
Date: 2018-09-28 00:00:00
Tasks: Community Detection

Credal partitions in the framework of belief functions can give us a better
understanding of the analyzed data set. In order to find credal community
structure in graph data sets, in this paper, we propose a novel evidential
community detection algorithm based on density peaks (EDPC). Two new metrics,
the local density $\rho$ and the minimum dissimi-larity $\delta$, are first
defined for each node in the graph. Then the nodes with both higher $\rho$ and
$\delta$ values are identified as community centers. Finally, the remaing nodes
are assigned with corresponding community labels through a simple two-step
evidential label propagation strategy. The membership of each node is described
in the form of basic belief assignments , which can well express the
uncertainty included in the community structure of the graph. The experiments
demonstrate the effectiveness of the proposed method on real-world networks.
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
Authors: Zheyi Pan, Yuxuan Liang, Junbo Zhang, Xiuwen Yi, Yong Yu, Yu Zheng
Date: 2018-09-28 00:00:00
Tasks: Spatio-Temporal Forecasting, Time Series, Time Series Analysis

Spatio-temporal (ST) data, which represent multiple time series data
corresponding to different spatial locations, are ubiquitous in real-world
dynamic systems, such as air quality readings. Forecasting over ST data is of
great importance but challenging as it is affected by many complex factors,
including spatial characteristics, temporal characteristics and the intrinsic
causality between them. In this paper, we propose a general framework
(HyperST-Net) based on hypernetworks for deep ST models. More specifically, it
consists of three major modules: a spatial module, a temporal module and a
deduction module. Among them, the deduction module derives the parameter
weights of the temporal module from the spatial characteristics, which are
extracted by the spatial module. Then, we design a general form of HyperST
layer as well as different forms for several basic layers in neural networks,
including the dense layer (HyperST-Dense) and the convolutional layer
(HyperST-Conv). Experiments on three types of real-world tasks demonstrate that
the predictive models integrated with our framework achieve significant
improvements, and outperform the state-of-the-art baselines as well.
Learning for Single-Shot Confidence Calibration in Deep Neural Networks through Stochastic Inferences
Authors: Seonguk Seo, Paul Hongsuck Seo, Bohyung Han
Date: 2018-09-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Prediction

We propose a generic framework to calibrate accuracy and confidence of a
prediction in deep neural networks through stochastic inferences. We interpret
stochastic regularization using a Bayesian model, and analyze the relation
between predictive uncertainty of networks and variance of the prediction
scores obtained by stochastic inferences for a single example. Our empirical
study shows that the accuracy and the score of a prediction are highly
correlated with the variance of multiple stochastic inferences given by
stochastic depth or dropout. Motivated by this observation, we design a novel
variance-weighted confidence-integrated loss function that is composed of two
cross-entropy loss terms with respect to ground-truth and uniform distribution,
which are balanced by variance of stochastic prediction scores. The proposed
loss function enables us to learn deep neural networks that predict confidence
calibrated scores using a single inference. Our algorithm presents outstanding
confidence calibration performance and improves classification accuracy when
combined with two popular stochastic regularization techniques---stochastic
depth and dropout---in multiple models and datasets; it alleviates
overconfidence issue in deep neural networks significantly by training networks
to achieve prediction accuracy proportional to confidence of prediction.
Characterizing Audio Adversarial Examples Using Temporal Dependency
Authors: Zhuolin Yang, Bo Li, Pin-Yu Chen, Dawn Song
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Defense, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inputs. In particular, our results reveal the importance of using the temporal dependency in audio data to gain discriminate power against adversarial examples. Tested on the automatic speech recognition (ASR) tasks and three recent audio adversarial attacks, we find that (i) input transformation developed from image adversarial defense provides limited robustness improvement and is subtle to advanced attacks; (ii) temporal dependency can be exploited to gain discriminative power against audio adversarial examples and is resistant to adaptive attacks considered in our experiments. Our results not only show promising means of improving the robustness of ASR systems, but also offer novel insights in exploiting domain-specific data properties to mitigate negative effects of adversarial examples.
The Rule of Three: Abstractive Text Summarization in Three Bullet Points
Authors: Tomonori Kodaira, Mamoru Komachi
Date: 2018-09-28 00:00:00
Proceeding: PACLIC 2018 12
Tasks: Abstractive Text Summarization, Sentence, Text Summarization

Neural network-based approaches have become widespread for abstractive text
summarization. Though previously proposed models for abstractive text
summarization addressed the problem of repetition of the same contents in the
summary, they did not explicitly consider its information structure. One of the
reasons these previous models failed to account for information structure in
the generated summary is that standard datasets include summaries of variable
lengths, resulting in problems in analyzing information flow, specifically, the
manner in which the first sentence is related to the following sentences.
Therefore, we use a dataset containing summaries with only three bullet points,
and propose a neural network-based abstractive summarization model that
considers the information structures of the generated summaries. Our
experimental results show that the information structure of a summary can be
controlled, thus improving the performance of the overall summarization.
Semantic Segmentation for Urban Planning Maps based on U-Net
Authors: Zhiling Guo, Hiroaki Shengoku, Guangming Wu, Qi Chen, Wei Yuan, Xiaodan Shi, Xiaowei Shao, Yongwei Xu, Ryosuke Shibasaki
Date: 2018-09-28 00:00:00
Tasks: Segmentation, Semantic Segmentation

The automatic digitizing of paper maps is a significant and challenging task
for both academia and industry. As an important procedure of map digitizing,
the semantic segmentation section mainly relies on manual visual interpretation
with low efficiency. In this study, we select urban planning maps as a
representative sample and investigate the feasibility of utilizing U-shape
fully convolutional based architecture to perform end-to-end map semantic
segmentation. The experimental results obtained from the test area in Shibuya
district, Tokyo, demonstrate that our proposed method could achieve a very high
Jaccard similarity coefficient of 93.63% and an overall accuracy of 99.36%. For
implementation on GPGPU and cuDNN, the required processing time for the whole
Shibuya district can be less than three minutes. The results indicate the
proposed method can serve as a viable tool for urban planning map semantic
segmentation task with high accuracy and efficiency.
Minimax Lower Bounds for $\mathcal{H}_\infty$-Norm Estimation
Authors: Stephen Tu, Ross Boczar, Benjamin Recht
Date: 2018-09-28 00:00:00

The problem of estimating the $\mathcal{H}_\infty$-norm of an LTI system from
noisy input/output measurements has attracted recent attention as an
alternative to parameter identification for bounding unmodeled dynamics in
robust control. In this paper, we study lower bounds for
$\mathcal{H}_\infty$-norm estimation under a query model where at each
iteration the algorithm chooses a bounded input signal and receives the
response of the chosen signal corrupted by white noise. We prove that when the
underlying system is an FIR filter, $\mathcal{H}_\infty$-norm estimation is no
more efficient than model identification for passive sampling. For active
sampling, we show that norm estimation is at most a factor of $\log{r}$ more
sample efficient than model identification, where $r$ is the length of the
filter. We complement our theoretical results with experiments which
demonstrate that a simple non-adaptive estimator of the norm is competitive
with state-of-the-art adaptive norm estimation algorithms.
Adaptive Input Representations for Neural Language Modeling
Authors: Alexei Baevski, Michael Auli
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Language Modeling, Language Modelling

We introduce adaptive input representations for neural language modeling
which extend the adaptive softmax of Grave et al. (2017) to input
representations of variable capacity. There are several choices on how to
factorize the input and output layers, and whether to model words, characters
or sub-word units. We perform a systematic comparison of popular choices for a
self-attentional architecture. Our experiments show that models equipped with
adaptive embeddings are more than twice as fast to train than the popular
character input CNN while having a lower number of parameters. On the
WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5
perplexity compared to the previously best published result and on the Billion
Word benchmark, we achieve 23.02 perplexity.
Encoding Robust Representation for Graph Generation
Authors: Dongmian Zou, Gilad Lerman
Date: 2018-09-28 00:00:00
Tasks: Decoder, Graph Generation, Link Prediction

Generative networks have made it possible to generate meaningful signals such
as images and texts from simple noise. Recently, generative methods based on
GAN and VAE were developed for graphs and graph signals. However, the
mathematical properties of these methods are unclear, and training good
generative models is difficult. This work proposes a graph generation model
that uses a recent adaptation of Mallat's scattering transform to graphs. The
proposed model is naturally composed of an encoder and a decoder. The encoder
is a Gaussianized graph scattering transform, which is robust to signal and
graph manipulation. The decoder is a simple fully connected network that is
adapted to specific tasks, such as link prediction, signal generation on graphs
and full graph and signal generation. The training of our proposed system is
efficient since it is only applied to the decoder and the hardware requirements
are moderate. Numerical results demonstrate state-of-the-art performance of the
proposed system for both link prediction and graph and signal generation.