pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning
Authors: Anahid Jalali, Clemens Heistracher, Alexander Schindler, Bernhard Haslhofer, Tanja Nemeth, Robert Glawar, Wilfried Sihn, Peter De Boer
Date: 2019-04-16 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Predicting unscheduled breakdowns of plasma etching equipment can reduce
maintenance costs and production losses in the semiconductor industry. However,
plasma etching is a complex procedure and it is hard to capture all relevant
equipment properties and behaviors in a single physical model. Machine learning
offers an alternative for predicting upcoming machine failures based on
relevant data points. In this paper, we describe three different machine
learning tasks that can be used for that purpose: (i) predicting
Time-To-Failure (TTF), (ii) predicting health state, and (iii) predicting TTF
intervals of an equipment. Our results show that trained machine learning
models can outperform benchmarks resembling human judgments in all three tasks.
This suggests that machine learning offers a viable alternative to currently
deployed plasma etching equipment maintenance strategies and decision making
processes.
Semantically Aligned Bias Reducing Zero Shot Learning
Authors: Akanksha Paul, Narayanan C. Krishnan, Prateek Munjal
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Zero-Shot Learning

Zero shot learning (ZSL) aims to recognize unseen classes by exploiting
semantic relationships between seen and unseen classes. Two major problems
faced by ZSL algorithms are the hubness problem and the bias towards the seen
classes. Existing ZSL methods focus on only one of these problems in the
conventional and generalized ZSL setting. In this work, we propose a novel
approach, Semantically Aligned Bias Reducing (SABR) ZSL, which focuses on
solving both the problems. It overcomes the hubness problem by learning a
latent space that preserves the semantic relationship between the labels while
encoding the discriminating information about the classes. Further, we also
propose ways to reduce the bias of the seen classes through a simple
cross-validation process in the inductive setting and a novel weak transfer
constraint in the transductive setting. Extensive experiments on three
benchmark datasets suggest that the proposed model significantly outperforms
existing state-of-the-art algorithms by ~1.5-9% in the conventional ZSL setting
and by ~2-14% in the generalized ZSL for both the inductive and transductive
settings.
LBVCNN: Local Binary Volume Convolutional Neural Network for Facial Expression Recognition from Image Sequences
Authors: Sudhakar Kumawat, Manisha Verma, Shanmuganathan Raman
Date: 2019-04-16 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Recognizing facial expressions is one of the central problems in computer
vision. Temporal image sequences have useful spatio-temporal features for
recognizing expressions. In this paper, we propose a new 3D Convolution Neural
Network (CNN) that can be trained end-to-end for facial expression recognition
on temporal image sequences without using facial landmarks. More specifically,
a novel 3D convolutional layer that we call Local Binary Volume (LBV) layer is
proposed. The LBV layer, when used with our newly proposed LBVCNN network,
achieve comparable results compared to state-of-the-art landmark-based or
without landmark-based models on image sequences from CK+, Oulu-CASIA, and UNBC
McMaster shoulder pain datasets. Furthermore, our LBV layer reduces the number
of trainable parameters by a significant amount when compared to a conventional
3D convolutional layer. As a matter of fact, when compared to a 3x3x3
conventional 3D convolutional layer, the LBV layer uses 27 times less trainable
parameters.
SparseMask: Differentiable Connectivity Learning for Dense Image Prediction
Authors: Huikai Wu, Junge Zhang, Kaiqi Huang
Date: 2019-04-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Decoder, Prediction

In this paper, we aim at automatically searching an efficient network architecture for dense image prediction. Particularly, we follow the encoder-decoder style and focus on designing a connectivity structure for the decoder. To achieve that, we design a densely connected network with learnable connections, named Fully Dense Network, which contains a large set of possible final connectivity structures. We then employ gradient descent to search the optimal connectivity from the dense connections. The search process is guided by a novel loss function, which pushes the weight of each connection to be binary and the connections to be sparse. The discovered connectivity achieves competitive results on two segmentation datasets, while runs more than three times faster and requires less than half parameters compared to the state-of-the-art methods. An extensive experiment shows that the discovered connectivity is compatible with various backbones and generalizes well to other dense image prediction tasks.
Learning a Local Symmetry with Neural-Networks
Authors: Aurélien Decelle, Victor Martin-Mayor, Beatriz Seoane
Date: 2019-04-16 00:00:00

We explore the capacity of neural networks to detect a symmetry with complex local and non-local patterns : the gauge symmetry Z 2 . This symmetry is present in physical problems from topological transitions to QCD, and controls the computational hardness of instances of spin-glasses. Here, we show how to design a neural network, and a dataset, able to learn this symmetry and to find compressed latent representations of the gauge orbits. Our method pays special attention to system-wrapping loops, the so-called Polyakov loops, known to be particularly relevant for computational complexity.
Applying Partial-ACO to Large-scale Vehicle Fleet Optimisation
Authors: Darren M. Chitty, Elizabeth Wanner, Rakhi Parmar, Peter R. Lewis
Date: 2019-04-16 00:00:00
Tasks: Scheduling

Optimisation of fleets of commercial vehicles with regards scheduling tasks
from various locations to vehicles can result in considerably lower fleet
traversal times. This has significant benefits including reduced expenses for
the company and more importantly, a reduction in the degree of road use and
hence vehicular emissions. Exact optimisation methods fail to scale to real
commercial problem instances, thus meta-heuristics are more suitable. Ant
Colony Optimisation (ACO) generally provides good solutions on small to medium
problem sizes. However, commercial fleet optimisation problems are typically
large and complex, in which ACO fails to scale well. Partial-ACO is a new ACO
variant designed to scale to larger problem instances. Therefore this paper
investigates the application of Partial-ACO on the problem of fleet
optimisation, demonstrating the capacity of Partial-ACO to successfully scale
to larger problems. Indeed, for real-world fleet optimisation problems supplied
by a Birmingham based company with up to 298 jobs and 32 vehicles, Partial-ACO
can improve upon their fleet traversal times by over 44%. Moreover, Partial-ACO
demonstrates its ability to scale with considerably improved results over
standard ACO and competitive results against a Genetic Algorithm.
HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learning
Authors: Oguzhan Gencoglu, Mark van Gils, Esin Guldogan, Chamin Morikawa, Mehmet Süzen, Mathias Gruber, Jussi Leinonen, Heikki Huttunen
Date: 2019-04-16 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, speech-recognition, Speech Recognition

Recent advancements in machine learning research, i.e., deep learning,
introduced methods that excel conventional algorithms as well as humans in
several complex tasks, ranging from detection of objects in images and speech
recognition to playing difficult strategic games. However, the current
methodology of machine learning research and consequently, implementations of
the real-world applications of such algorithms, seems to have a recurring
HARKing (Hypothesizing After the Results are Known) issue. In this work, we
elaborate on the algorithmic, economic and social reasons and consequences of
this phenomenon. We present examples from current common practices of
conducting machine learning research (e.g. avoidance of reporting negative
results) and failure of generalization ability of the proposed algorithms and
datasets in actual real-life usage. Furthermore, a potential future trajectory
of machine learning research and development from the perspective of
accountable, unbiased, ethical and privacy-aware algorithmic decision making is
discussed. We would like to emphasize that with this discussion we neither
claim to provide an exhaustive argumentation nor blame any specific institution
or individual on the raised issues. This is simply a discussion put forth by
us, insiders of the machine learning field, reflecting on us.
Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings
Authors: Zhaoning Li, Qi Li, Xiaotian Zou, Jiangtao Ren
Date: 2019-04-16 00:00:00
Tasks: Feature Engineering

Causality extraction from natural language texts is a challenging open problem in artificial intelligence. Existing methods utilize patterns, constraints, and machine learning techniques to extract causality, heavily depending on domain knowledge and requiring considerable human effort and time for feature engineering. In this paper, we formulate causality extraction as a sequence labeling problem based on a novel causality tagging scheme. On this basis, we propose a neural causality extractor with the BiLSTM-CRF model as the backbone, named SCITE (Self-attentive BiLSTM-CRF wIth Transferred Embeddings), which can directly extract cause and effect without extracting candidate causal pairs and identifying their relations separately. To address the problem of data insufficiency, we transfer contextual string embeddings, also known as Flair embeddings, which are trained on a large corpus in our task. In addition, to improve the performance of causality extraction, we introduce a multihead self-attention mechanism into SCITE to learn the dependencies between causal words. We evaluate our method on a public dataset, and experimental results demonstrate that our method achieves significant and consistent improvement compared to baselines.
Compressed Indexes for Fast Search of Semantic Data
Authors: Raffaele Perego, Giulio Ermanno Pibiri, Rossano Venturini
Date: 2019-04-16 00:00:00

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60% less space and speeding up query execution by a factor of 2-81x.
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning
Authors: Pedro Hermosilla, Tobias Ritschel, Timo Ropinski
Date: 2019-04-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Denoising, valid

We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i. e., deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes a na\"ive extension of unsupervised image denoisers to 3D point clouds impractical. Overcoming this, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on a manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data.
Speech Denoising by Accumulating Per-Frequency Modeling Fluctuations
Authors: Michael Michelashvili, Lior Wolf
Date: 2019-04-16 00:00:00
Tasks: Audio Denoising, Speech Denoising

We present a method for audio denoising that combines processing done in both the time domain and the time-frequency domain. Given a noisy audio clip, the method trains a deep neural network to fit this signal. Since the fitting is only partly successful and is able to better capture the underlying clean signal than the noise, the output of the network helps to disentangle the clean audio from the rest of the signal. This is done by accumulating a fitting score per time-frequency bin and applying the time-frequency domain filtering based on the obtained scores. The method is completely unsupervised and only trains on the specific audio clip that is being denoised. Our experiments demonstrate favorable performance in comparison to the literature methods. Our code and samples are available at github.com/mosheman5/DNP and as supplementary. Index Terms: Audio denoising; Unsupervised learning
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Authors: Yongcheng Liu, Bin Fan, Shiming Xiang, Chunhong Pan
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, Point Cloud Classification, Relation

Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration for point cloud analysis. The key to RS-CNN is learning from relation, i.e., the geometric topology constraint among points. Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others. In this way, an inductive local representation with explicit reasoning about the spatial layout of points can be obtained, which leads to much shape awareness and robustness. With this convolution as a basic operator, RS-CNN, a hierarchical architecture can be developed to achieve contextual shape-aware learning for point cloud analysis. Extensive experiments on challenging benchmarks across three tasks verify RS-CNN achieves the state of the arts.
Detecting the Unexpected via Image Resynthesis
Authors: Krzysztof Lis, Krishna Nakka, Pascal Fua, Mathieu Salzmann
Date: 2019-04-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Resynthesis, Semantic Segmentation

Classical semantic segmentation methods, including the recent deep learning
ones, assume that all classes observed at test time have been seen during
training. In this paper, we tackle the more realistic scenario where unexpected
objects of unknown classes can appear at test time. The main trends in this
area either leverage the notion of prediction uncertainty to flag the regions
with low confidence as unknown, or rely on autoencoders and highlight
poorly-decoded regions. Having observed that, in both cases, the detected
regions typically do not correspond to unexpected objects, in this paper, we
introduce a drastically different strategy: It relies on the intuition that the
network will produce spurious labels in regions depicting unexpected objects.
Therefore, resynthesizing the image from the resulting semantic map will yield
significant appearance differences with respect to the input image. In other
words, we translate the problem of detecting unknown classes to one of
identifying poorly-resynthesized image regions. We show that this outperforms
both uncertainty- and autoencoder-based methods.
Risk Bounds for Learning Multiple Components with Permutation-Invariant Losses
Authors: Fabien Lauer
Date: 2019-04-16 00:00:00
Tasks: Clustering, Quantization

This paper proposes a simple approach to derive efficient error bounds for learning multiple components with sparsity-inducing regularization. We show that for such regularization schemes, known decompositions of the Rademacher complexity over the components can be used in a more efficient manner to result in tighter bounds without too much effort. We give examples of application to switching regression and center-based clustering/vector quantization. Then, the complete workflow is illustrated on the problem of subspace clustering, for which decomposition results were not previously available. For all these problems, the proposed approach yields risk bounds with mild dependencies on the number of components and completely removes this dependence for nonconvex regularization schemes that could not be handled by previous methods.
Patch alignment manifold matting
Authors: Xuelong. Li, Kang Liu, Yongsheng Dong, DaCheng Tao
Date: 2019-04-16 00:00:00
Tasks: Image Matting

Image matting is generally modeled as a space transform from the color space
to the alpha space. By estimating the alpha factor of the model, the foreground
of an image can be extracted. However, there is some dimensional information
redundancy in the alpha space. It usually leads to the misjudgments of some
pixels near the boundary between the foreground and the background. In this
paper, a manifold matting framework named Patch Alignment Manifold Matting is
proposed for image matting. In particular, we first propose a part modeling of
color space in the local image patch. We then perform whole alignment
optimization for approximating the alpha results using subspace reconstructing
error. Furthermore, we utilize Nesterov's algorithm to solve the optimization
problem. Finally, we apply some manifold learning methods in the framework, and
obtain several image matting methods, such as named ISOMAP matting and its
derived Cascade ISOMAP matting. The experimental results reveal that the
manifold matting framework and its two examples are effective when compared
with several representative matting methods.
ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data
Authors: Taban Eslami, Vahid Mirjalili, Alvis Fong, Angela Laird, Fahad Saeed
Date: 2019-04-16 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Diagnostic

Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous
disorders that are notoriously difficult to diagnose, especially in children.
The current psychiatric diagnostic process is based purely on the behavioural
observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing
of drugs due to misdiagnosis. In order to move the field towards more
quantitative fashion, we need advanced and scalable machine learning
infrastructure that will allow us to identify reliable biomarkers of mental
health disorders. In this paper, we propose a framework called ASD-DiagNet for
classifying subjects with ASD from healthy subjects by using only fMRI data. We
designed and implemented a joint learning procedure using an autoencoder and a
single layer perceptron which results in improved quality of extracted features
and optimized parameters for the model. Further, we designed and implemented a
data augmentation strategy, based on linear interpolation on available feature
vectors, that allows us to produce synthetic datasets needed for training of
machine learning models. The proposed approach is evaluated on a public dataset
provided by Autism Brain Imaging Data Exchange including 1035 subjects coming
from 17 different brain imaging centers. Our machine learning model outperforms
other state of the art methods from 13 imaging centers with increase in
classification accuracy up to 20% with maximum accuracy of 80%. The machine
learning technique presented in this paper, in addition to yielding better
quality, gives enormous advantages in terms of execution time (40 minutes vs. 6
hours on other methods). The implemented code is available as GPL license on
GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).
Investigating the Effect of Attributes on User Trust in Social Media
Authors: Jamal Al Qundus, Adrian Paschke
Date: 2019-04-16 00:00:00
Tasks: Attribute

One main challenge in social media is to identify trustworthy information. If
we cannot recognize information as trustworthy, that information may become
useless or be lost. Opposite, we could consume wrong or fake information with
major consequences. How does a user handle the information provided before
consuming it? Are the comments on a post, the author or votes essential for
taking such a decision? Are these attributes considered together and which
attribute is more important? To answer these questions, we developed a trust
model to support knowledge sharing of user content in social media. This trust
model is based on the dimensions of stability, quality, and credibility. Each
dimension contains metrics (user role, user IQ, votes, etc.) that are important
to the user based on data analysis. We present in this paper, an evaluation of
the proposed trust model using conjoint analysis (CA) as an evaluation method.
The results obtained from 348 responses, validate the trust model. A trust
degree translator interprets the content as very trusted, trusted, untrusted,
and very untrusted based on the calculated value of trust. Furthermore, the
results show different importance for each dimension: stability 24%,
credibility 35% and quality 41%.
On the Mathematical Understanding of ResNet with Feynman Path Integral
Authors: Minghao Yin, Xiu Li, Yongbing Zhang, Shiqi Wang
Date: 2019-04-16 00:00:00

In this paper, we aim to understand Residual Network (ResNet) in a
scientifically sound way by providing a bridge between ResNet and Feynman path
integral. In particular, we prove that the effect of residual block is
equivalent to partial differential equation, and the ResNet transforming
process can be equivalently converted to Feynman path integral. These
conclusions greatly help us mathematically understand the advantage of ResNet
in addressing the gradient vanishing issue. More importantly, our analyses
offer a path integral view of ResNet, and demonstrate that the output of
certain network can be obtained by adding contributions of all paths. Moreover,
the contribution of each path is proportional to e^{-S}, where S is the action
given by time integral of Lagrangian L. This lays the solid foundation in the
understanding of ResNet, and provides insights in the future design of
convolutional neural network architecture. Based on these results, we have
designed the network using partial differential operators, which further
validates our theoritical analyses.