pwc-archive/papers-with-abstracts huggingface dataset:
Max-C and Min-D Projection Autoassociative Fuzzy Morphological Memories: Theory and an Application for Face Recognition
Authors: Alex Santana dos Santos, Marcos Eduardo Valle
Date: 2019-02-11 00:00:00
Tasks: Face Recognition, Retrieval

Max-C and min-D projection autoassociative fuzzy morphological memories (max-C and min-D PAFMMs) are two layer feedforward fuzzy morphological neural networks able to implement an associative memory designed for the storage and retrieval of finite fuzzy sets or vectors on a hypercube. In this paper we address the main features of these autoassociative memories, which include unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and an excellent tolerance to either dilative noise or erosive noise. Particular attention is given to the so-called PAFMM of Zadeh which, besides performing no floating-point operations, exhibit the largest noise tolerance among max-C and min-D PAFMMs. Computational experiments reveal that Zadeh's max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with strong potential for face recognition.
Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval
Authors: Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi
Date: 2019-02-11 00:00:00
Tasks: Attribute, Deep Hashing, Face Image Retrieval, Image Retrieval, Retrieval

With benefits of fast query speed and low storage cost, hashing-based image
retrieval approaches have garnered considerable attention from the research
community. In this paper, we propose a novel Error-Corrected Deep Cross Modal
Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain
facial attributes as an input query to retrieve relevant face images from the
database. In this architecture, we generate compact hash codes using an
end-to-end deep learning module, which effectively captures the inherent
relationships between the face and attribute modality. We also integrate our
deep learning module with forward error correction codes to further reduce the
distance between different modalities of the same subject. Specifically, the
properties of deep hashing and forward error correction codes are exploited to
design a cross modal hashing framework with high retrieval performance.
Experimental results using two standard datasets with facial attributes-image
modalities indicate that our CMH-ECC face image retrieval model outperforms
most of the current attribute-based face image retrieval approaches.
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Authors: Jaeyoung Lee, Aravind Balakrishnan, Ashish Gaurav, Krzysztof Czarnecki, Sean Sedwards
Date: 2019-02-11 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Motion Planning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Machine learning can provide efficient solutions to the complex problems
encountered in autonomous driving, but ensuring their safety remains a
challenge. A number of authors have attempted to address this issue, but there
are few publicly-available tools to adequately explore the trade-offs between
functionality, scalability, and safety.
  We thus present WiseMove, a software framework to investigate safe deep
reinforcement learning in the context of motion planning for autonomous
driving. WiseMove adopts a modular learning architecture that suits our current
research questions and can be adapted to new technologies and new questions. We
present the details of WiseMove, demonstrate its use on a common traffic
scenario, and describe how we use it in our ongoing safe learning research.
Using Embeddings to Correct for Unobserved Confounding in Networks
Authors: Victor Veitch, Yixin Wang, David M. Blei
Date: 2019-02-11 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Causal Inference, valid

We consider causal inference in the presence of unobserved confounding. We study the case where a proxy is available for the unobserved confounding in the form of a network connecting the units. For example, the link structure of a social network carries information about its members. We show how to effectively use the proxy to do causal inference. The main idea is to reduce the causal estimation problem to a semi-supervised prediction of both the treatments and outcomes. Networks admit high-quality embedding models that can be used for this semi-supervised prediction. We show that the method yields valid inferences under suitable (weak) conditions on the quality of the predictive model. We validate the method with experiments on a semi-synthetic social network dataset. Code is available at github.com/vveitch/causal-network-embeddings.
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models
Authors: Ehsan Amid, Manfred K. Warmuth
Date: 2019-02-11 00:00:00
Tasks: parameter estimation

Expectation-Maximization (EM) is a prominent approach for parameter estimation of hidden (aka latent) variable models. Given the full batch of data, EM forms an upper-bound of the negative log-likelihood of the model at each iteration and updates to the minimizer of this upper-bound. We first provide a "model level" interpretation of the EM upper-bound as sum of relative entropy divergences to a set of singleton models, induced by the set of observations. Our alternative motivation unifies the "observation level" and the "model level" view of the EM. As a result, we formulate an online version of the EM algorithm by adding an analogous inertia term which corresponds to the relative entropy divergence to the old model. Our motivation is more widely applicable than the previous approaches and leads to simple online updates for mixture of exponential distributions, hidden Markov models, and the first known online update for Kalman filters. Additionally, the finite sample form of the inertia term lets us derive online updates when there is no closed-form solution. Finally, we extend the analysis to the distributed setting where we motivate a systematic way of combining multiple hidden variable models. Experimentally, we validate the results on synthetic as well as real-world datasets.
On Residual Networks Learning a Perturbation from Identity
Authors: Michael Hauser
Date: 2019-02-11 00:00:00

The purpose of this work is to test and study the hypothesis that residual
networks are learning a perturbation from identity. Residual networks are
enormously important deep learning models, with many theories attempting to
explain how they function; learning a perturbation from identity is one such
theory. In order to answer this question, the magnitudes of the perturbations
are measured in both an absolute sense as well as in a scaled sense, with each
form having its relative benefits and drawbacks. Additionally, a stopping rule
is developed that can be used to decide the depth of the residual network based
on the average perturbation magnitude being less than a given epsilon. With
this analysis a better understanding of how residual networks process and
transform data from input to output is formed. Parallel experiments are
conducted on MNIST as well as CIFAR10 for various sized residual networks with
between 6 and 300 residual blocks. It is found that, in this setting, the
average scaled perturbation magnitude is roughly inversely proportional to
increasing the number of residual blocks, and from this it follows that for
sufficiently large residual networks, they are learning a perturbation from
identity.
Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation
Authors: Balamurali Murugesan, Kaushik Sarveswaran, Sharath M. Shankaranarayana, Keerthi Ram, Mohanasankar Sivaprakasam
Date: 2019-02-11 00:00:00
Tasks: Contour Detection, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is a primary task in many medical applications. Recently, many deep networks derived from U-Net have been extensively used in various medical image segmentation tasks. However, in most of the cases, networks similar to U-net produce coarse and non-smooth segmentations with lots of discontinuities. To improve and refine the performance of U-Net like networks, we propose the use of parallel decoders which along with performing the mask predictions also perform contour prediction and distance map estimation. The contour and distance map aid in ensuring smoothness in the segmentation predictions. To facilitate joint training of three tasks, we propose a novel architecture called Psi-Net with a single encoder and three parallel decoders (thus having a shape of $\Psi$), one decoder to learns the segmentation mask prediction and other two decoders to learn the auxiliary tasks of contour detection and distance map estimation. The learning of these auxiliary tasks helps in capturing the shape and the boundary information. We also propose a new joint loss function for the proposed architecture. The loss function consists of a weighted combination of Negative Log likelihood and Mean Square Error loss. We have used two publicly available datasets: 1) Origa dataset for the task of optic cup and disc segmentation and 2) Endovis segment dataset for the task of polyp segmentation to evaluate our model. We have conducted extensive experiments using our network to show our model gives better results in terms of segmentation, boundary and shape metrics.
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
Authors: Alex Wang, Kyunghyun Cho
Date: 2019-02-11 00:00:00
Proceeding: WS 2019 6
Tasks: Language Modeling, Language Modelling

We show that BERT (Devlin et al., 2018) is a Markov random field language
model. This formulation gives way to a natural procedure to sample sentences
from BERT. We generate from BERT and find that it can produce high-quality,
fluent generations. Compared to the generations of a traditional left-to-right
language model, BERT generates sentences that are more diverse but of slightly
worse quality.
Adversarial Generation of Time-Frequency Features with application in audio synthesis
Authors: Andrés Marafioti, Nicki Holighaus, Nathanaël Perraudin, Piotr Majdak
Date: 2019-02-11 00:00:00
Proceeding: 36th International Conference on Machine Learning 2019 6
Tasks: Audio Generation, Audio Synthesis, Generative Adversarial Network, Time Series, Time Series Analysis

Time-frequency (TF) representations provide powerful and intuitive features for the analysis of time series such as audio. But still, generative modeling of audio in the TF domain is a subtle matter. Consequently, neural audio synthesis widely relies on directly modeling the waveform and previous attempts at unconditionally synthesizing audio from neurally generated invertible TF features still struggle to produce audio at satisfying quality. In this article, focusing on the short-time Fourier transform, we discuss the challenges that arise in audio synthesis based on generated invertible TF features and how to overcome them. We demonstrate the potential of deliberate generative TF modeling by training a generative adversarial network (GAN) on short-time Fourier features. We show that by applying our guidelines, our TF-based network was able to outperform a state-of-the-art GAN generating waveforms directly, despite the similar architecture in the two networks.
Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition
Authors: Xiao-Hui Yang, Li Tian, Yun-Mei Chen, Li-Jun Yang, Shuang Xu, Wen-Ming Wu
Date: 2019-02-09 00:00:00
Tasks: Classification, General Classification, Sparse Representation-based Classification

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.
Iteratively reweighted penalty alternating minimization methods with continuation for image deblurring
Authors: Tao Sun, Dongsheng Li, Hao Jiang, Zhe Quan
Date: 2019-02-09 00:00:00
Tasks: Deblurring, Image Deblurring

In this paper, we consider a class of nonconvex problems with linear
constraints appearing frequently in the area of image processing. We solve this
problem by the penalty method and propose the iteratively reweighted
alternating minimization algorithm. To speed up the algorithm, we also apply
the continuation strategy to the penalty parameter. A convergence result is
proved for the algorithm. Compared with the nonconvex ADMM, the proposed
algorithm enjoys both theoretical and computational advantages like weaker
convergence requirements and faster speed. Numerical results demonstrate the
efficiency of the proposed algorithm.
Automatic Inspection of Utility Scale Solar Power Plants using Deep Learning
Authors: Alekh Karkada Ashok, Chandan G, Adithya Bhat, Kausthubh Karnataki, Ganesh Shankar
Date: 2018-12-20 00:00:00

Solar energy has the potential to become the backbone energy source for the
world. Utility scale solar power plants (more than 50 MW) could have more than
100K individual solar modules and be spread over more than 200 acres of land.
Traditionally methods of monitoring each module become too costly in the
utility scale. We demonstrate an alternative using the recent advances in deep
learning to automatically analyze drone footage. We show that this can be a
quick and reliable alternative. We show that it can save huge amounts of power
and the impact the developing world hugely.
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography
Authors: Rony Gelman
Date: 2019-01-26 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

Deep learning has been successfully applied to a variety of image
classification tasks. There has been keen interest to apply deep learning in
the medical domain, particularly specialties that heavily utilize imaging, such
as ophthalmology. One issue that may hinder application of deep learning to the
medical domain is the vast amount of data necessary to train deep neural
networks (DNNs). Because of regulatory and privacy issues associated with
medicine, and the generally proprietary nature of data in medical domains,
obtaining large datasets to train DNNs is a challenge, particularly in the
ophthalmology domain.
  Transfer learning is a technique developed to address the issue of applying
DNNs for domains with limited data. Prior reports on transfer learning have
examined custom networks to fully train or used a particular DNN for transfer
learning. However, to the best of my knowledge, no work has systematically
examined a suite of DNNs for transfer learning for classification of diabetic
retinopathy, diabetic macular edema, and two key features of age-related
macular degeneration. This work attempts to investigate transfer learning for
classification of these ophthalmic conditions. Part I gives a condensed
overview of neural networks and the DNNs under evaluation. Part II gives the
reader the necessary background concerning diabetic retinopathy and prior work
on classification using retinal fundus photographs. The methodology and results
of transfer learning for diabetic retinopathy classification are presented,
showing that transfer learning towards this domain is feasible, with promising
accuracy. Part III gives an overview of diabetic macular edema, choroidal
neovascularization and drusen (features associated with age-related macular
degeneration), and presents results for transfer learning evaluation using
optical coherence tomography to classify these entities.
An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks
Authors: M. Hanefi Calp
Date: 2019-02-05 00:00:00

Today, many public or private institutions provide professional food service
for personnels working in their own organizations. Regarding the planning of
the said service, there are some obstacles due to the fact that the number of
the personnel working in the institutions is generally high and the personnel
are out of the institution due to personal or institutional reasons. Because of
this, it is difficult to determine the daily food demand, and this causes cost,
time and labor loss for the institutions. Statistical or heuristic methods are
used to remove or at least minimize these losses. In this study, an artificial
intelligence model was proposed, which estimates the daily food demand quantity
using artificial neural networks for businesses. The data are obtained from a
refectory database of a private institution with a capacity of 110 people
serving daily meals and serving at different levels, covering the last two
years (2016-2018). The model was created using the MATLAB package program. The
performance of the model was determinde by the Regression values, the Mean
Absolute Percentage Error (MAPE) and the Mean Squared Error (MSE). In the
training of the ANN model, feed forward back propagation network architecture
is used. The best model obtained as a result of the experiments is a
multi-layer (8-10-10-1) structure with a training R ratio of 0,9948, a testing
R ratio of 0,9830 and an error rate of 0,003783, respectively. Experimental
results demonstrated that the model has low error rate, high performance and
positive effect of using artificial neural networks for demand estimating.
WideDTA: prediction of drug-target binding affinity
Authors: Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
Date: 2019-02-04 00:00:00
Tasks: Deep Learning, Drug Discovery, Prediction

Motivation: Prediction of the interaction affinity between proteins and
compounds is a major challenge in the drug discovery process. WideDTA is a
deep-learning based prediction model that employs chemical and biological
textual sequence information to predict binding affinity.
  Results: WideDTA uses four text-based information sources, namely the protein
sequence, ligand SMILES, protein domains and motifs, and maximum common
substructure words to predict binding affinity. WideDTA outperformed one of the
state of the art deep learning methods for drug-target binding affinity
prediction, DeepDTA on the KIBA dataset with a statistical significance. This
indicates that the word-based sequence representation adapted by WideDTA is a
promising alternative to the character-based sequence representation approach
in deep learning models for binding affinity prediction, such as the one used
in DeepDTA. In addition, the results showed that, given the protein sequence
and ligand SMILES, the inclusion of protein domain and motif information as
well as ligand maximum common substructure words do not provide additional
useful information for the deep learning model. Interestingly, however, using
only domain and motif information to represent proteins achieved similar
performance to using the full protein sequence, suggesting that important
binding relevant information is contained within the protein motifs and
domains.
Applying Quantum Hardware to non-Scientific Problems: Grover's Algorithm and Rule-based Algorithmic Music Composition
Authors: Alexis Kirke
Date: 2019-02-02 00:00:00

Of all novel computing methods, quantum computation (QC) is currently the most likely to move from the realm of the unconventional into the conventional. As a result some initial work has been done on applications of QC outside of science: for example music. The small amount of arts research done in hardware or with actual physical systems has not utilized any of the advantages of quantum computation (QC): the main advantage being the potential speed increase of quantum algorithms. This paper introduces a way of utilizing Grover's algorithm - which has been shown to provide a quadratic speed-up over its classical equivalent - in algorithmic rule-based music composition. The system introduced - qgMuse - is simple but scalable. Example melodies are composed using qgMuse using the ibmqx4 quantum hardware. The paper concludes with discussion on how such an approach can grow with the improvement of quantum computer hardware and software.
Evaluating Older Users' Experiences with Commercial Dialogue Systems: Implications for Future Design and Development
Authors: Libby Ferland, Thomas Huffstutler, Jacob Rice, Joan Zheng, Shi Ni, Maria Gini
Date: 2019-01-30 00:00:00

Understanding the needs of a variety of distinct user groups is vital in
designing effective, desirable dialogue systems that will be adopted by the
largest possible segment of the population. Despite the increasing popularity
of dialogue systems in both mobile and home formats, user studies remain
relatively infrequent and often sample a segment of the user population that is
not representative of the needs of the potential user population as a whole.
This is especially the case for users who may be more reluctant adopters, such
as older adults.
  In this paper we discuss the results of a recent user study performed over a
large population of age 50 and over adults in the Midwestern United States that
have experience using a variety of commercial dialogue systems. We show the
common preferences, use cases, and feature gaps identified by older adult users
in interacting with these systems. Based on these results, we propose a new,
robust user modeling framework that addresses common issues facing older adult
users, which can then be generalized to the wider user population.