pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Authors: Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang
Date: 2018-11-28 00:00:00
Tasks: Stochastic Optimization

Difference of convex (DC) functions cover a broad family of non-convex and
possibly non-smooth and non-differentiable functions, and have wide
applications in machine learning and statistics. Although deterministic
algorithms for DC functions have been extensively studied, stochastic
optimization that is more suitable for learning with big data remains
under-explored. In this paper, we propose new stochastic optimization
algorithms and study their first-order convergence theories for solving a broad
family of DC functions. We improve the existing algorithms and theories of
stochastic optimization for DC functions from both practical and theoretical
perspectives. On the practical side, our algorithm is more user-friendly
without requiring a large mini-batch size and more efficient by saving
unnecessary computations. On the theoretical side, our convergence analysis
does not necessarily require the involved functions to be smooth with Lipschitz
continuous gradient. Instead, the convergence rate of the proposed stochastic
algorithm is automatically adaptive to the H\"{o}lder continuity of the
gradient of one component function. Moreover, we extend the proposed stochastic
algorithms for DC functions to solve problems with a general non-convex
non-differentiable regularizer, which does not necessarily have a DC
decomposition but enjoys an efficient proximal mapping. To the best of our
knowledge, this is the first work that gives the first non-asymptotic
convergence for solving non-convex optimization whose objective has a general
non-convex non-differentiable regularizer.
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data
Authors: Yutong Bai, Qing Liu, Lingxi Xie, Weichao Qiu, Yan Zheng, Alan Yuille
Date: 2018-11-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Clustering, Object, Semantic Part Detection

Detecting semantic parts of an object is a challenging task in computer vision, particularly because it is hard to construct large annotated datasets due to the difficulty of annotating semantic parts. In this paper we present an approach which learns from a small training dataset of annotated semantic parts, where the object is seen from a limited range of viewpoints, but generalizes to detect semantic parts from a much larger range of viewpoints. Our approach is based on a matching algorithm for finding accurate spatial correspondence between two images, which enables semantic parts annotated on one image to be transplanted to another. In particular, this enables images in the training dataset to be matched to a virtual 3D model of the object (for simplicity, we assume that the object viewpoint can be estimated by standard techniques). Then a clustering algorithm is used to annotate the semantic parts of the 3D virtual model. This virtual 3D model can be used to synthesize annotated images from a large range of viewpoint. These can be matched to images in the test set, using the same matching algorithm, to detect semantic parts in novel viewpoints of the object. Our algorithm is very simple, intuitive, and contains very few parameters. We evaluate our approach in the car subclass of the VehicleSemanticPart dataset. We show it outperforms standard deep network approaches and, in particular, performs much better on novel viewpoints. For facilitating the future research, code is available: https://github.com/ytongbai/SemanticPartDetection
Unsupervised Meta-Learning For Few-Shot Image Classification
Authors: Siavash Khodadadeh, Ladislau Bölöni, Mubarak Shah
Date: 2018-11-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Classification, Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Inductive Bias, Meta-Learning, One-Shot Learning, Unsupervised Few-Shot Image Classification, Unsupervised Few-Shot Learning, Video Classification

Few-shot or one-shot learning of classifiers requires a significant inductive bias towards the type of task to be learned. One way to acquire this is by meta-learning on tasks similar to the target task. In this paper, we propose UMTRA, an algorithm that performs unsupervised, model-agnostic meta-learning for classification tasks. The meta-learning step of UMTRA is performed on a flat collection of unlabeled images. While we assume that these images can be grouped into a diverse set of classes and are relevant to the target task, no explicit information about the classes or any labels are needed. UMTRA uses random sampling and augmentation to create synthetic training tasks for meta-learning phase. Labels are only needed at the final target task learning step, and they can be as little as one sample per class. On the Omniglot and Mini-Imagenet few-shot learning benchmarks, UMTRA outperforms every tested approach based on unsupervised learning of representations, while alternating for the best performance with the recent CACTUs algorithm. Compared to supervised model-agnostic meta-learning approaches, UMTRA trades off some classification accuracy for a reduction in the required labels of several orders of magnitude.
Disease phenotyping using deep learning: A diabetes case study
Authors: Sina Rashidian, Janos Hajagos, Richard Moffitt, Fusheng Wang, Xinyu Dong, Kayley Abell-Hart, Kimberly Noel, Rajarsi Gupta, Mathew Tharakan, Veena Lingam, Joel Saltz, Mary Saltz
Date: 2018-11-28 00:00:00
Tasks: Decision Making, Deep Learning

Characterization of a patient clinical phenotype is central to biomedical
informatics. ICD codes, assigned to inpatient encounters by coders, is
important for population health and cohort discovery when clinical information
is limited. While ICD codes are assigned to patients by professionals trained
and certified in coding there is substantial variability in coding. We present
a methodology that uses deep learning methods to model coder decision making
and that predicts ICD codes. Our approach predicts codes based on demographics,
lab results, and medications, as well as codes from previous encounters. We are
able to predict existing codes with high accuracy for all three of the test
cases we investigated: diabetes, acute renal failure, and chronic kidney
disease. We employed a panel of clinicians, in a blinded manner, to assess
ground truth and compared the predictions of coders, model and clinicians. When
disparities between the model prediction and coder assigned codes were
reviewed, our model outperformed coder assigned ICD codes.
2D/3D Megavoltage Image Registration Using Convolutional Neural Networks
Authors: Hector N. B. Pinheiro, Tsang Ing Ren, Stefan Scheib, Armel Rosselet, Stefan Thieme-Marti
Date: 2018-11-28 00:00:00
Tasks: Image Registration

We presented a 2D/3D MV image registration method based on a Convolutional
Neural Network. Most of the traditional image registration method
intensity-based, which use optimization algorithms to maximize the similarity
between to images. Although these methods can achieve good results for
kilovoltage images, the same does not occur for megavoltage images due to the
lower image quality. Also, these methods most of the times do not present a
good capture range. To deal with this problem, we propose the use of
Convolutional Neural Network. The experiments were performed using a dataset of
50 brain images. The results showed to be promising compared to traditional
image registration methods.
Unrepresentative video data: A review and evaluation
Authors: Georgios Mastorakis
Date: 2018-11-28 00:00:00
Tasks: Action Recognition, Temporal Action Localization

It is well known that the quality and quantity of training data are
significant factors which affect the development and performance of machine
intelligence algorithms. Without representative data, neither scientists nor
algorithms would be able to accurately capture the visual details of objects,
actions or scenes. An evaluation methodology which filters data quality does
not yet exist, and currently, the validation of the data depends solely on
human factor. This study reviews several public datasets and discusses their
limitations and issues regarding quality, feasibility, adaptation and
availability of training data. A simple approach to evaluate (i.e.
automatically "clean" samples) training data is proposed with the use of real
events recorded on the YouTube platform. This study focuses on action
recognition data and particularly on human fall detection datasets. However,
the limitations described in this paper apply in virtually all datasets.
Phase Collaborative Network for Two-Phase Medical Image Segmentation
Authors: Huangjie Zheng, Lingxi Xie, Tianwei Ni, Ya zhang, Yan-Feng Wang, Qi Tian, Elliot K. Fishman, Alan L. Yuille
Date: 2018-11-28 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Organ Segmentation, Semantic Segmentation, Vocal Bursts Valence Prediction

In real-world practice, medical images acquired in different phases possess complementary information, {\em e.g.}, radiologists often refer to both arterial and venous scans in order to make the diagnosis. However, in medical image analysis, fusing prediction from two phases is often difficult, because (i) there is a domain gap between two phases, and (ii) the semantic labels are not pixel-wise corresponded even for images scanned from the same patient. This paper studies organ segmentation in two-phase CT scans. We propose Phase Collaborative Network (PCN), an end-to-end framework that contains both generative and discriminative modules. PCN can be mathematically explained to formulate phase-to-phase and data-to-label relations jointly. Experiments are performed on a two-phase CT dataset, on which PCN outperforms the baselines working with one-phase data by a large margin, and we empirically verify that the gain comes from inter-phase collaboration. Besides, PCN transfers well to two public single-phase datasets, demonstrating its potential applications.
The SWAG Algorithm; a Mathematical Approach that Outperforms Traditional Deep Learning. Theory and Implementation
Authors: Saeid Safaei, Vahid Safaei, Solmazi Safaei, Zerotti Woods, Hamid R. Arabnia, Juan B. Gutierrez
Date: 2018-11-28 00:00:00
Tasks: Learning Theory

The performance of artificial neural networks (ANNs) is influenced by weight
initialization, the nature of activation functions, and their architecture.
There is a wide range of activation functions that are traditionally used to
train a neural network, e.g. sigmoid, tanh, and Rectified Linear Unit (ReLU). A
widespread practice is to use the same type of activation function in all
neurons in a given layer. In this manuscript, we present a type of neural
network in which the activation functions in every layer form a polynomial
basis; we name this method SWAG after the initials of the last names of the
authors. We tested SWAG on three complex highly non-linear functions as well as
the MNIST handwriting data set. SWAG outperforms and converges faster than the
state of the art performance in fully connected neural networks. Given the low
computational complexity of SWAG, and the fact that it was capable of solving
problems current architectures cannot, it has the potential to change the way
that we approach deep learning.
Cartoon-to-real: An Approach to Translate Cartoon to Realistic Images using GAN
Authors: K M Arefeen Sultan, Labiba Kanij Rupty, Nahidul Islam Pranto, Sayed Khan Shuvo, Mohammad Imrul Jubair
Date: 2018-11-28 00:00:00
Tasks: Image-to-Image Translation, Translation

We propose a method to translate cartoon images to real world images using
Generative Aderserial Network (GAN). Existing GAN-based image-to-image
translation methods which are trained on paired datasets are impractical as the
data is difficult to accumulate. Therefore, in this paper we exploit the
Cycle-Consistent Adversarial Networks (CycleGAN) method for images translation
which needs an unpaired dataset. By applying CycleGAN we show that our model is
able to generate meaningful real world images from cartoon images. However, we
implement another state of the art technique $-$ Deep Analogy $-$ to compare
the performance of our approach.
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Authors: Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick
Date: 2018-11-28 00:00:00

Until recently, transcriptomics was limited to bulk RNA sequencing, obscuring
the underlying expression patterns of individual cells in favor of a global
average. Thanks to technological advances, we can now profile gene expression
across thousands or millions of individual cells in parallel. This new type of
data has led to the intriguing discovery that individual cell profiles can
reflect the imprint of time or dynamic processes. However, synthesizing this
information to reconstruct dynamic biological phenomena from data that are
noisy, heterogenous, and sparse---and from processes that may unfold
asynchronously---poses a complex computational and statistical challenge. Here,
we develop a full generative model for probabilistically reconstructing trees
of cellular differentiation from single-cell RNA-seq data. Specifically, we
extend the framework of the classical Dirichlet diffusion tree to
simultaneously infer branch topology and latent cell states along continuous
trajectories over the full tree. In tandem, we construct a novel Markov chain
Monte Carlo sampler that interleaves Metropolis-Hastings and message passing to
leverage model structure for efficient inference. Finally, we demonstrate that
these techniques can recover latent trajectories from simulated single-cell
transcriptomes. While this work is motivated by cellular differentiation, we
derive a tractable model that provides flexible densities for any data (coupled
with an appropriate noise model) that arise from continuous evolution along a
latent nonparametric tree.
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
Authors: Steven McDonagh, Sarah Parisot, Fengwei Zhou, Xing Zhang, Ales Leonardis, Zhenguo Li, Gregory Slabaugh
Date: 2018-11-28 00:00:00
Tasks: Color Constancy, Few-Shot Camera-Adaptive Color Constancy, Meta-Learning

Digital camera pipelines employ color constancy methods to estimate an
unknown scene illuminant, in order to re-illuminate images as if they were
acquired under an achromatic light source. Fully-supervised learning approaches
exhibit state-of-the-art estimation accuracy with camera-specific labelled
training imagery. Resulting models typically suffer from domain gaps and fail
to generalise across imaging devices. In this work, we propose a new approach
that affords fast adaptation to previously unseen cameras, and robustness to
changes in capture device by leveraging annotated samples across different
cameras and datasets. We present a general approach that utilizes the concept
of color temperature to frame color constancy as a set of distinct, homogeneous
few-shot regression tasks, each associated with an intuitive physical meaning.
We integrate this novel formulation within a meta-learning framework, enabling
fast generalisation to previously unseen cameras using only handfuls of camera
specific training samples. Consequently, the time spent for data collection and
annotation substantially diminishes in practice whenever a new sensor is used.
To quantify this gain, we evaluate our pipeline on three publicly available
datasets comprising 12 different cameras and diverse scene content. Our
approach delivers competitive results both qualitatively and quantitatively
while requiring a small fraction of the camera-specific samples compared to
standard approaches.
A Scoring Method for Driving Safety Credit Using Trajectory Data
Authors: Wenfu Wang, Weijie Yang, An Chen, Zhijie Pan
Date: 2018-11-28 00:00:00
Tasks: Credit score, General Classification

Urban traffic systems worldwide are suffering from severe traffic safety
problems. Traffic safety is affected by many complex factors, and heavily
related to all drivers' behaviors involved in traffic system. Drivers with
aggressive driving behaviors increase the risk of traffic accidents. In order
to manage the safety level of traffic system, we propose Driving Safety Credit
inspired by credit score in financial security field, and design a scoring
method using trajectory data and violation records. First, we extract driving
habits, aggressive driving behaviors and traffic violation behaviors from
driver's trajectories and traffic violation records. Next, we train a
classification model to filtered out irrelevant features. And at last, we score
each driver with selected features. We verify our proposed scoring method using
40 days of traffic simulation, and proves the effectiveness of our scoring
method.
AI based Safety System for Employees of Manufacturing Industries in Developing Countries
Authors: Abhisek Das, Satanik Panda, Suman Datta, Soumitra Naskar, Pratep Misra, Tanushyam Chattopadhyay
Date: 2018-11-28 00:00:00
Tasks: Transfer Learning

In this paper authors are going to present a Markov Decision Process (MDP)
based algorithm in Industrial Internet of Things (IIoT) as a safety compliance
layer for human in loop system. Though some industries are moving towards
Industry 4.0 and attempting to automate the systems as much as possible by
using robots, still human in loop systems are very common in developing
countries like India. When ever there is a need for human machine interaction,
there is a scope of health hazard. In this work we have developed a system for
one such industry using MDP. The proposed algorithm used in this system learned
the probability of state transition from experience as well as the system is
adaptable to new changes by incorporating the concept of transfer learning. The
system was evaluated on the data set obtained from 39 sensors connected to a
computer numerically controlled (CNC) machine pushing data every second in a
24x7 scenario. The state changes are typically instructed by a human which
subsequently lead to some intentional or unintentional mistakes and errors. The
proposed system raises an alarm for the operator to warn which he may or may
not overlook depending on his own perception about the present condition of the
system. Repeated ignorance of the operator for a particular state transition
warning guides the system to retrain the model. We observed 95.61% alarms
raised by the said system are taken care of by the operator. 3.2% alarms are
coming from the changes in the system which in turn used to retrain the model
and 1.19% alarms are false alarms. We could not compute the error coming from
the mistake performed by the human operator as there is no ground truth
available for that.
Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network
Authors: Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão, Derick Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton Pifano, Wagner Meira Jr., Thomas B. Schön, Antonio Luiz Ribeiro
Date: 2018-11-28 00:00:00
Tasks: Diagnostic, Electrocardiography (ECG)

We present a model for predicting electrocardiogram (ECG) abnormalities in
short-duration 12-lead ECG signals which outperformed medical doctors on the
4th year of their cardiology residency. Such exams can provide a full
evaluation of heart activity and have not been studied in previous end-to-end
machine learning papers. Using the database of a large telehealth network, we
built a novel dataset with more than 2 million ECG tracings, orders of
magnitude larger than those used in previous studies. Moreover, our dataset is
more realistic, as it consist of 12-lead ECGs recorded during standard
in-clinics exams. Using this data, we trained a residual neural network with 9
convolutional layers to map 7 to 10 second ECG signals to 6 classes of ECG
abnormalities. Future work should extend these results to cover a large range
of ECG abnormalities, which could improve the accessibility of this diagnostic
tool and avoid wrong diagnosis from medical doctors.
A comparison of cluster algorithms as applied to unsupervised surveys
Authors: Kathleen Campbell Garwood, Ph. D., Arpit Arun Dhobale
Date: 2018-11-28 00:00:00
Tasks: Clustering, Survey

When considering answering important questions with data, unsupervised data
offers extensive insight opportunity and unique challenges. This study
considers student survey data with a specific goal of clustering students into
like groups with underlying concept of identifying different poverty levels.
Fuzzy logic is considered during the data cleaning and organizing phase helping
to create a logical dependent variable for analysis comparison. Using multiple
data reduction techniques, the survey was reduced and cleaned. Finally,
multiple clustering techniques (k-means, k-modes, and hierarchical clustering)
are applied and compared. Though each method has strengths, the goal was to
identify which was most viable when applied to survey data and specifically
when trying to identify the most impoverished students.
Particle Probability Hypothesis Density Filter based on Pairwise Markov Chains
Authors: Jiangyi Liu, Chunping Wang, Wei Wang
Date: 2018-11-28 00:00:00

Most multi-target tracking filters assume that one target and its observation
follow a Hidden Markov Chain (HMC) model, but the implicit independence
assumption of HMC model is invalid in many practical applications, and a
Pairwise Markov Chain (PMC) model is more universally suitable than traditional
HMC model. A particle probability hypothesis density filter based on PMC model
(PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system.
Simulation results show the effectiveness of PF-PMC-PHD filter, and that the
tracking performance of PF-PMC-PHD filter is superior to the particle PHD
filter based on HMC model in a scenario where we kept the local physical
properties of nonlinear and Gaussian HMC models while relaxing their
independence assumption.
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration
Authors: Marco Cavallo, Çağatay Demiralp
Date: 2018-11-28 00:00:00
Tasks: Dimensionality Reduction

Dimensionality reduction is a common method for analyzing and visualizing
high-dimensional data. However, reasoning dynamically about the results of a
dimensionality reduction is difficult. Dimensionality-reduction algorithms use
complex optimizations to reduce the number of dimensions of a dataset, but
these new dimensions often lack a clear relation to the initial data
dimensions, thus making them difficult to interpret. Here we propose a visual
interaction framework to improve dimensionality-reduction based exploratory
data analysis. We introduce two interaction techniques, forward projection and
backward projection, for dynamically reasoning about dimensionally reduced
data. We also contribute two visualization techniques, prolines and feasibility
maps, to facilitate the effective use of the proposed interactions. We apply
our framework to PCA and autoencoder-based dimensionality reductions. Through
data-exploration examples, we demonstrate how our visual interactions can
improve the use of dimensionality reduction in exploratory data analysis.