pwc-archive/papers-with-abstracts huggingface dataset:
Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection
Authors: Tadanobu Inoue, Subhajit Chaudhury, Giovanni De Magistris, Sakyasingha Dasgupta
Date: 2018-07-04 00:00:00
Tasks: Position, Transfer Learning

Capturing and labeling camera images in the real world is an expensive task,
whereas synthesizing labeled images in a simulation environment is easy for
collecting large-scale image data. However, learning from only synthetic images
may not achieve the desired performance in the real world due to a gap between
synthetic and real images. We propose a method that transfers learned detection
of an object position from a simulation environment to the real world. This
method uses only a significantly limited dataset of real images while
leveraging a large dataset of synthetic images using variational autoencoders.
Additionally, the proposed method consistently performed well in different
lighting conditions, in the presence of other distractor objects, and on
different backgrounds. Experimental results showed that it achieved accuracy of
1.5mm to 3.5mm on average. Furthermore, we showed how the method can be used in
a real-world scenario like a "pick-and-place" robotic task.
Restructuring Batch Normalization to Accelerate CNN Training
Authors: Wonkyung Jung, Daejin Jung, and Byeongho Kim, Sunjung Lee, Wonjong Rhee, Jung Ho Ahn
Date: 2018-07-04 00:00:00

Batch Normalization (BN) has become a core design block of modern
Convolutional Neural Networks (CNNs). A typical modern CNN has a large number
of BN layers in its lean and deep architecture. BN requires mean and variance
calculations over each mini-batch during training. Therefore, the existing
memory access reduction techniques, such as fusing multiple CONV layers, are
not effective for accelerating BN due to their inability to optimize mini-batch
related calculations during training. To address this increasingly important
problem, we propose to restructure BN layers by first splitting a BN layer into
two sub-layers (fission) and then combining the first sub-layer with its
preceding CONV layer and the second sub-layer with the following activation and
CONV layers (fusion). The proposed solution can significantly reduce
main-memory accesses while training the latest CNN models, and the experiments
on a chip multiprocessor show that the proposed BN restructuring can improve
the performance of DenseNet-121 by 25.7%.
A complete framework for linear filtering of bivariate signals
Authors: Julien Flamant, Pierre Chainais, Nicolas Le Bihan
Date: 2018-02-07 00:00:00

A complete framework for the linear time-invariant (LTI) filtering theory of
bivariate signals is proposed based on a tailored quaternion Fourier transform.
This framework features a direct description of LTI filters in terms of their
eigenproperties enabling compact calculus and physically interpretable
filtering relations in the frequency domain. The design of filters exhibiting
fondamental properties of polarization optics (birefringence, diattenuation) is
straightforward. It yields an efficient spectral synthesis method and new
insights on Wiener filtering for bivariate signals with prescribed
frequency-dependent polarization properties. This generic framework facilitates
original descriptions of bivariate signals in two components with specific
geometric or statistical properties. Numerical experiments support our
theoretical analysis and illustrate the relevance of the approach on synthetic
data.
Modern hierarchical, agglomerative clustering algorithms
Authors: Daniel Müllner
Date: 2011-09-12 00:00:00
Tasks: Clustering

This paper presents algorithms for hierarchical, agglomerative clustering
which perform most efficiently in the general-purpose setup that is given in
modern standard software. Requirements are: (1) the input data is given by
pairwise dissimilarities between data points, but extensions to vector data are
also discussed (2) the output is a "stepwise dendrogram", a data structure
which is shared by all implementations in current standard software. We present
algorithms (old and new) which perform clustering in this setting efficiently,
both in an asymptotic worst-case analysis and from a practical point of view.
The main contributions of this paper are: (1) We present a new algorithm which
is suitable for any distance update scheme and performs significantly better
than the existing algorithms. (2) We prove the correctness of two algorithms by
Rohlf and Murtagh, which is necessary in each case for different reasons. (3)
We give well-founded recommendations for the best current algorithms for the
various agglomerative clustering schemes.
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
Authors: Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang, Ce Zhu
Date: 2018-07-06 00:00:00
Tasks: Denoising, Image Compression, Image Denoising, Image Restoration

In this paper, we propose a novel approach to the rank minimization problem, termed rank residual constraint (RRC) model. Different from existing low-rank based approaches, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM), which estimate the underlying low-rank matrix directly from the corrupted observations, we progressively approximate the underlying low-rank matrix via minimizing the rank residual. Through integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we apply it to image restoration tasks, including image denoising and image compression artifacts reduction. Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image. In this manner, both the reference and the estimated image are updated gradually and jointly in each iteration. Based on the group-based sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art schemes in both the objective and perceptual quality.
Deep Multiple Instance Feature Learning via Variational Autoencoder
Authors: Shabnam Ghaffarzadegan
Date: 2018-07-06 00:00:00
Tasks: Event Detection, Multiple Instance Learning, Weakly-supervised Learning

We describe a novel weakly supervised deep learning framework that combines
both the discriminative and generative models to learn meaningful
representation in the multiple instance learning (MIL) setting. MIL is a weakly
supervised learning problem where labels are associated with groups of
instances (referred as bags) instead of individual instances. To address the
essential challenge in MIL problems raised from the uncertainty of positive
instances label, we use a discriminative model regularized by variational
autoencoders (VAEs) to maximize the differences between latent representations
of all instances and negative instances. As a result, the hidden layer of the
variational autoencoder learns meaningful representation. This representation
can effectively be used for MIL problems as illustrated by better performance
on the standard benchmark datasets comparing to the state-of-the-art
approaches. More importantly, unlike most related studies, the proposed
framework can be easily scaled to large dataset problems, as illustrated by the
audio event detection and segmentation task. Visualization also confirms the
effectiveness of the latent representation in discriminating positive and
negative classes.
A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation
Authors: Yang Hu, Andrea Soltoggio, Russell Lock, Steve Carter
Date: 2018-07-06 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Vocal Bursts Valence Prediction

In this paper, we propose a novel fully convolutional two-stream fusion
network (FCTSFN) for interactive image segmentation. The proposed network
includes two sub-networks: a two-stream late fusion network (TSLFN) that
predicts the foreground at a reduced resolution, and a multi-scale refining
network (MSRN) that refines the foreground at full resolution. The TSLFN
includes two distinct deep streams followed by a fusion network. The intuition
is that, since user interactions are more direct information on
foreground/background than the image itself, the two-stream structure of the
TSLFN reduces the number of layers between the pure user interaction features
and the network output, allowing the user interactions to have a more direct
impact on the segmentation result. The MSRN fuses the features from different
layers of TSLFN with different scales, in order to seek the local to global
information on the foreground to refine the segmentation result at full
resolution. We conduct comprehensive experiments on four benchmark datasets.
The results show that the proposed network achieves competitive performance
compared to current state-of-the-art interactive image segmentation methods
Development of a sensory-neural network for medical diagnosing
Authors: Igor Grabec, Eva Švegl, Mihael Sok
Date: 2018-07-06 00:00:00

Performance of a sensory-neural network developed for diagnosing of diseases
is described. Information about patient's condition is provided by answers to
the questionnaire. Questions correspond to sensors generating signals when
patients acknowledge symptoms. These signals excite neurons in which
characteristics of the diseases are represented by synaptic weights associated
with indicators of symptoms. The disease corresponding to the most excited
neuron is proposed as the result of diagnosing. Its reliability is estimated by
the likelihood defined by the ratio of excitation of the most excited neuron
and the complete neural network.
Multi-modal Non-line-of-sight Passive Imaging
Authors: Andre Beckus, Alexandru Tamasan, George K. Atia
Date: 2018-07-06 00:00:00
Tasks: Object

We consider the non-line-of-sight (NLOS) imaging of an object using the light
reflected off a diffusive wall. The wall scatters incident light such that a
lens is no longer useful to form an image. Instead, we exploit the 4D spatial
coherence function to reconstruct a 2D projection of the obscured object. The
approach is completely passive in the sense that no control over the light
illuminating the object is assumed and is compatible with the partially
coherent fields ubiquitous in both the indoor and outdoor environments. We
formulate a multi-criteria convex optimization problem for reconstruction,
which fuses the reflected field's intensity and spatial coherence information
at different scales. Our formulation leverages established optics models of
light propagation and scattering and exploits the sparsity common to many
images in different bases. We also develop an algorithm based on the
alternating direction method of multipliers to efficiently solve the convex
program proposed. A means for analyzing the null space of the measurement
matrices is provided as well as a means for weighting the contribution of
individual measurements to the reconstruction. This paper holds promise to
advance passive imaging in the challenging NLOS regimes in which the intensity
does not necessarily retain distinguishable features and provides a framework
for multi-modal information fusion for efficient scene reconstruction.
Tangent Convolutions for Dense Prediction in 3D
Authors: Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, Qian-Yi Zhou
Date: 2018-07-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Semantic Segmentation, Prediction, Semantic Segmentation

We present an approach to semantic scene analysis using deep convolutional
networks. Our approach is based on tangent convolutions - a new construction
for convolutional networks on 3D data. In contrast to volumetric approaches,
our method operates directly on surface geometry. Crucially, the construction
is applicable to unstructured point clouds and other noisy real-world data. We
show that tangent convolutions can be evaluated efficiently on large-scale
point clouds with millions of points. Using tangent convolutions, we design a
deep fully-convolutional network for semantic segmentation of 3D point clouds,
and apply it to challenging real-world datasets of indoor and outdoor 3D
environments. Experimental results show that the presented approach outperforms
other recent deep network constructions in detailed analysis of large 3D
scenes.
Multi-Task Learning with Incomplete Data for Healthcare
Authors: Xin J. Hunt, Saba Emrani, Ilknur Kaynar Kabul, Jorge Silva
Date: 2018-07-06 00:00:00
Tasks: Imputation, Missing Values, Multi-Task Learning, Transfer Learning

Multi-task learning is a type of transfer learning that trains multiple tasks
simultaneously and leverages the shared information between related tasks to
improve the generalization performance. However, missing features in the input
matrix is a much more difficult problem which needs to be carefully addressed.
Removing records with missing values can significantly reduce the sample size,
which is impractical for datasets with large percentage of missing values.
Popular imputation methods often distort the covariance structure of the data,
which causes inaccurate inference. In this paper we propose using plug-in
covariance matrix estimators to tackle the challenge of missing features.
Specifically, we analyze the plug-in estimators under the framework of robust
multi-task learning with LASSO and graph regularization, which captures the
relatedness between tasks via graph regularization. We use the Alzheimer's
disease progression dataset as an example to show how the proposed framework is
effective for prediction and model estimation when missing data is present.
Deep Sequential Segmentation of Organs in Volumetric Medical Scans
Authors: Alexey Novikov, David Major, Maria Wimmer, Dimitrios Lenis, Katja Bühler
Date: 2018-07-06 00:00:00
Tasks: Liver Segmentation, Segmentation

Segmentation in 3D scans is playing an increasingly important role in current
clinical practice supporting diagnosis, tissue quantification, or treatment
planning. The current 3D approaches based on convolutional neural networks
usually suffer from at least three main issues caused predominantly by
implementation constraints - first, they require resizing the volume to the
lower-resolutional reference dimensions, second, the capacity of such
approaches is very limited due to memory restrictions, and third, all slices of
volumes have to be available at any given training or testing time. We address
these problems by a U-Net-like architecture consisting of bidirectional
convolutional LSTM and convolutional, pooling, upsampling and concatenation
layers enclosed into time-distributed wrappers. Our network can either process
the full volumes in a sequential manner, or segment slabs of slices on demand.
We demonstrate performance of our architecture on vertebrae and liver
segmentation tasks in 3D CT scans.
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification
Authors: Yuexiang Li, Xinpeng Xie, Linlin Shen, Shaoxiong Liu
Date: 2018-07-06 00:00:00
Tasks: Active Learning, Classification, Deep Learning, General Classification, image-classification, Image Classification, Medical Image Analysis

Witnessed the development of deep learning in recent years, increasing number
of researches try to adopt deep learning model for medical image analysis.
However, the usage of deep learning networks for the pathological image
analysis encounters several challenges, e.g. high resolution (gigapixel) of
pathological images and lack of annotations of cancer areas. To address the
challenges, we proposed a complete framework for the pathological image
classification, which consists of a novel training strategy, namely reversed
active learning (RAL), and an advanced network, namely atrous DenseNet (ADN).
The proposed RAL can remove the mislabel patches in the training set. The
refined training set can then be used to train widely used deep learning
networks, e.g. VGG-16, ResNets, etc. A novel deep learning network, i.e. atrous
DenseNet (ADN), is also proposed for the classification of pathological images.
The proposed ADN achieves multi-scale feature extraction by integrating the
atrous convolutions to the Dense Block. The proposed RAL and ADN have been
evaluated on two pathological datasets, i.e. BACH and CCG. The experimental
results demonstrate the excellent performance of the proposed ADN + RAL
framework, i.e. the average patch-level ACAs of 94.10% and 92.05% on BACH and
CCG validation sets were achieved.
A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work
Authors: Enrique Fernández-Macías, Emilia Gómez, José Hernández-Orallo, Bao Sheng Loe, Bertin Martens, Fernando Martínez-Plumed, Songül Tolan
Date: 2018-07-06 00:00:00

This paper presents a multidisciplinary task approach for assessing the
impact of artificial intelligence on the future of work. We provide definitions
of a task from two main perspectives: socio-economic and computational. We
propose to explore ways in which we can integrate or map these perspectives,
and link them with the skills or capabilities required by them, for humans and
AI systems. Finally, we argue that in order to understand the dynamics of
tasks, we have to explore the relevance of autonomy and generality of AI
systems for the automation or alteration of the workplace.
Quality Diversity Through Surprise
Authors: Daniele Gravina, Antonios Liapis, Georgios N. Yannakakis
Date: 2018-07-06 00:00:00
Tasks: Diversity, Robot Navigation

Quality diversity is a recent family of evolutionary search algorithms which
focus on finding several well-performing (quality) yet different (diversity)
solutions with the aim to maintain an appropriate balance between divergence
and convergence during search. While quality diversity has already delivered
promising results in complex problems, the capacity of divergent search
variants for quality diversity remains largely unexplored. Inspired by the
notion of surprise as an effective driver of divergent search and its
orthogonal nature to novelty this paper investigates the impact of the former
to quality diversity performance. For that purpose we introduce three new
quality diversity algorithms which employ surprise as a diversity measure,
either on its own or combined with novelty, and compare their performance
against novelty search with local competition, the state of the art quality
diversity algorithm. The algorithms are tested in a robot navigation task
across 60 highly deceptive mazes. Our findings suggest that allowing surprise
and novelty to operate synergistically for divergence and in combination with
local competition leads to quality diversity algorithms of significantly higher
efficiency, speed and robustness.
Shannon entropy for intuitionistic fuzzy information
Authors: Vasile Patrascu
Date: 2018-07-02 00:00:00

The paper presents an extension of Shannon fuzzy entropy for intuitionistic
fuzzy one. Firstly, we presented a new formula for calculating the distance and
similarity of intuitionistic fuzzy information. Then, we constructed measures
for information features like score, certainty and uncertainty. Also, a new
concept was introduced, namely escort fuzzy information. Then, using the escort
fuzzy information, Shannon's formula for intuitionistic fuzzy information was
obtained. It should be underlined that Shannon's entropy for intuitionistic
fuzzy information verifies the four defining conditions of intuitionistic fuzzy
uncertainty. The measures of its two components were also identified: fuzziness
(ambiguity) and incompleteness (ignorance).
Accelerated physical emulation of Bayesian inference in spiking neural networks
Authors: Akos F. Kungl, Sebastian Schmitt, Johann Klähn, Paul Müller, Andreas Baumbach, Dominik Dold, Alexander Kugele, Nico Gürtler, Luziwei Leng, Eric Müller, Christoph Koke, Mitja Kleider, Christian Mauch, Oliver Breitwieser, Maurice Güttler, Dan Husmann, Kai Husmann, Joscha Ilmberger, Andreas Hartel, Vitali Karasenko, Andreas Grübl, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici
Date: 2018-07-06 00:00:00
Tasks: Bayesian Inference

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.
Natural Language Processing for Information Extraction
Authors: Sonit Singh
Date: 2018-07-06 00:00:00
Tasks: Articles, coreference-resolution, Coreference Resolution, Entity Linking, Information Retrieval, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Understanding, Question Answering, Relation Extraction, Retrieval, Text Summarization, Translation

With rise of digital age, there is an explosion of information in the form of
news, articles, social media, and so on. Much of this data lies in unstructured
form and manually managing and effectively making use of it is tedious, boring
and labor intensive. This explosion of information and need for more
sophisticated and efficient information handling tools gives rise to
Information Extraction(IE) and Information Retrieval(IR) technology.
Information Extraction systems takes natural language text as input and
produces structured information specified by certain criteria, that is relevant
to a particular application. Various sub-tasks of IE such as Named Entity
Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction,
Knowledge Base reasoning forms the building blocks of various high end Natural
Language Processing (NLP) tasks such as Machine Translation, Question-Answering
System, Natural Language Understanding, Text Summarization and Digital
Assistants like Siri, Cortana and Google Now. This paper introduces Information
Extraction technology, its various sub-tasks, highlights state-of-the-art
research in various IE subtasks, current challenges and future research
directions.
A Structured Prediction Approach for Label Ranking
Authors: Anna Korba, Alexandre Garcia, Florence d'Alché Buc
Date: 2018-07-06 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Prediction, regression, Structured Prediction

We propose to solve a label ranking problem as a structured output regression
task. We adopt a least square surrogate loss approach that solves a supervised
learning problem in two steps: the regression step in a well-chosen feature
space and the pre-image step. We use specific feature maps/embeddings for
ranking data, which convert any ranking/permutation into a vector
representation. These embeddings are all well-tailored for our approach, either
by resulting in consistent estimators, or by solving trivially the pre-image
problem which is often the bottleneck in structured prediction. We also propose
their natural extension to the case of partial rankings and prove their
efficiency on real-world datasets.