pwc-archive/papers-with-abstracts huggingface dataset:
Trick or TReAT: Thematic Reinforcement for Artistic Typography
Authors: Purva Tendulkar, Kalpesh Krishna, Ramprasaath R. Selvaraju, Devi Parikh
Date: 2019-03-19 00:00:00

An approach to make text visually appealing and memorable is semantic
reinforcement - the use of visual cues alluding to the context or theme in
which the word is being used to reinforce the message (e.g., Google Doodles).
We present a computational approach for semantic reinforcement called TReAT -
Thematic Reinforcement for Artistic Typography. Given an input word (e.g. exam)
and a theme (e.g. education), the individual letters of the input word are
replaced by cliparts relevant to the theme which visually resemble the letters
- adding creative context to the potentially boring input word. We use an
unsupervised approach to learn a latent space to represent letters and cliparts
and compute similarities between the two. Human studies show that participants
can reliably recognize the word as well as the theme in our outputs (TReATs)
and find them more creative compared to meaningful baselines.
Self-Weighted Multiview Metric Learning by Maximizing the Cross Correlations
Authors: Huibing Wang, Jinjia Peng, Xianping Fu
Date: 2019-03-19 00:00:00
Tasks: Face Recognition, Image Retrieval, Metric Learning, Retrieval

With the development of multimedia time, one sample can always be described
from multiple views which contain compatible and complementary information.
Most algorithms cannot take information from multiple views into considerations
and fail to achieve desirable performance in most situations. For many
applications, such as image retrieval, face recognition, etc., an appropriate
distance metric can better reflect the similarities between various samples.
Therefore, how to construct a good distance metric learning methods which can
deal with multiview data has been an important topic during the last decade. In
this paper, we proposed a novel algorithm named Self-weighted Multiview Metric
Learning (SM2L) which can finish this task by maximizing the cross correlations
between different views. Furthermore, because multiple views have different
contributions to the learning procedure of SM2L, we adopt a self-weighted
learning framework to assign multiple views with different weights. Various
experiments on benchmark datasets can verify the performance of our proposed
method.
Estimation of crowd density applying wavelet transform and machine learning
Authors: Koki Nagao, Daichi Yanagisawa, Katsuhiro Nishinari
Date: 2019-03-19 00:00:00
Tasks: BIG-bench Machine Learning, Management

We conducted a simple experiment in which one pedestrian passed through a
crowded area and measured the body-rotational angular velocity with commercial
tablets. Then, we developed a new method for predicting crowd density by
applying the continuous wavelet transform and machine learning to the data
obtained in the experiment. We found that the accuracy of prediction using
angular velocity data was as high as that using raw velocity data. Therefore,
we concluded that angular velocity has relationship with crowd density and we
could estimate crowd density by angular velocity. Our research will contribute
to management of safety and comfort of pedestrians by developing an easy way to
measure crowd density.
Dynamic Deep Networks for Retinal Vessel Segmentation
Authors: Aashis Khanal, Rolando Estrada
Date: 2019-03-19 00:00:00
Tasks: Retinal Vessel Segmentation

Segmenting the retinal vasculature entails a trade-off between how much of
the overall vascular structure we identify vs. how precisely we segment
individual vessels. In particular, state-of-the-art methods tend to
under-segment faint vessels, as well as pixels that lie on the edges of thicker
vessels. Thus, they underestimate the width of individual vessels, as well as
the ratio of large to small vessels. More generally, many crucial
bio-markers---including the artery-vein (AV) ratio, branching angles, number of
bifurcation, fractal dimension, tortuosity, vascular length-to-diameter ratio
and wall-to-lumen length---require precise measurements of individual vessels.
To address this limitation, we propose a novel, stochastic training scheme for
deep neural networks that better classifies the faint, ambiguous regions of the
image. Our approach relies on two key innovations. First, we train our deep
networks with dynamic weights that fluctuate during each training iteration.
This stochastic approach forces the network to learn a mapping that robustly
balances precision and recall. Second, we decouple the segmentation process
into two steps. In the first half of our pipeline, we estimate the likelihood
of every pixel and then use these likelihoods to segment pixels that are
clearly vessel or background. In the latter part of our pipeline, we use a
second network to classify the ambiguous regions in the image. Our proposed
method obtained state-of-the-art results on five retinal datasets---DRIVE,
STARE, CHASE-DB, AV-WIDE, and VEVIO---by learning a robust balance between
false positive and false negative rates. In addition, we are the first to
report segmentation results on the AV-WIDE dataset, and we have made the
ground-truth annotations for this dataset publicly available.
Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise
Authors: Yuefeng Chen, Qing Wang
Date: 2019-03-19 00:00:00
Tasks: Visual Tracking

Recently a category of tracking methods based on "tracking-by-detection" is
widely used in visual tracking problem. Most of these methods update the
classifier online using the samples generated by the tracker to handle the
appearance changes. However, the self-updating scheme makes these methods
suffer from drifting problem because of the incorrect labels of weak
classifiers in training samples. In this paper, we split the class labels into
true labels and noise labels and model them by sparse representation. A novel
dynamic classifier selection method, robust to noisy training data, is
proposed. Moreover, we apply the proposed classifier selection algorithm to
visual tracking by integrating a part based online boosting framework. We have
evaluated our proposed method on 12 challenging sequences involving severe
occlusions, significant illumination changes and large pose variations. Both
the qualitative and quantitative evaluations demonstrate that our approach
tracks objects accurately and robustly and outperforms state-of-the-art
trackers.
Random Pairwise Shapelets Forest
Authors: Mohan Shi, Zhihai Wang, Jodong Yuan, Haiyang Liu
Date: 2019-03-19 00:00:00
Tasks: Time Series, Time Series Analysis

Shapelet is a discriminative subsequence of time series. An advanced
shapelet-based method is to embed shapelet into accurate and fast random
forest. However, it shows several limitations. First, random shapelet forest
requires a large training cost for split threshold searching. Second, a single
shapelet provides limited information for only one branch of the decision tree,
resulting in insufficient accuracy and interpretability. Third, randomized
ensemble causes interpretability declining. For that, this paper presents
Random Pairwise Shapelets Forest (RPSF). RPSF combines a pair of shapelets from
different classes to construct random forest. It omits threshold searching to
be more efficient, includes more information for each node of the forest to be
more effective. Moreover, a discriminability metric, Decomposed Mean Decrease
Impurity (DMDI), is proposed to identify influential region for every class.
Extensive experiments show RPSF improves the accuracy and training speed of
shapelet-based forest. Case studies demonstrate the interpretability of our
method.
Differentially Private Consensus-Based Distributed Optimization
Authors: Mehrdad Showkatbakhsh, Can Karakus, Suhas Diggavi
Date: 2019-03-19 00:00:00
Tasks: Distributed Optimization

Data privacy is an important concern in learning, when datasets contain
sensitive information about individuals. This paper considers consensus-based
distributed optimization under data privacy constraints. Consensus-based
optimization consists of a set of computational nodes arranged in a graph, each
having a local objective that depends on their local data, where in every step
nodes take a linear combination of their neighbors' messages, as well as taking
a new gradient step. Since the algorithm requires exchanging messages that
depend on local data, private information gets leaked at every step. Taking
$(\epsilon, \delta)$-differential privacy (DP) as our criterion, we consider
the strategy where the nodes add random noise to their messages before
broadcasting it, and show that the method achieves convergence with a bounded
mean-squared error, while satisfying $(\epsilon, \delta)$-DP. By relaxing the
more stringent $\epsilon$-DP requirement in previous work, we strengthen a
known convergence result in the literature. We conclude the paper with
numerical results demonstrating the effectiveness of our methods for mean
estimation.
Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks
Authors: Junkai Sun, Junbo Zhang, Qiaofei Li, Xiuwen Yi, Yuxuan Liang, Yu Zheng
Date: 2019-03-19 00:00:00

Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public safety. However, it is very challenging because of interactions and spatial correlations between different regions. In addition, it is affected by many factors: i) multiple temporal correlations among different time intervals: closeness, period, trend; ii) complex external influential factors: weather, events; iii) meta features: time of the day, day of the week, and so on. In this paper, we formulate crowd flow forecasting in irregular regions as a spatio-temporal graph (STG) prediction problem in which each node represents a region with time-varying flows. By extending graph convolution to handle the spatial information, we propose using spatial graph convolution to build a multi-view graph convolutional network (MVGCN) for the crowd flow forecasting problem, where different views can capture different factors as mentioned above. We evaluate MVGCN using four real-world datasets (taxicabs and bikes) and extensive experimental results show that our approach outperforms the adaptations of state-of-the-art methods. And we have developed a crowd flow forecasting system for irregular regions that can now be used internally.
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Authors: Kun Yi, Jianxin Wu
Date: 2019-03-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Classification, Learning with noisy labels

Deep learning has achieved excellent performance in various computer vision
tasks, but requires a lot of training examples with clean labels. It is easy to
collect a dataset with noisy labels, but such noise makes networks overfit
seriously and accuracies drop dramatically. To address this problem, we propose
an end-to-end framework called PENCIL, which can update both network parameters
and label estimations as label distributions. PENCIL is independent of the
backbone network structure and does not need an auxiliary clean dataset or
prior information about noise, thus it is more general and robust than existing
methods and is easy to apply. PENCIL outperforms previous state-of-the-art
methods by large margins on both synthetic and real-world datasets with
different noise types and noise rates. Experiments show that PENCIL is robust
on clean datasets, too.
Cloze-driven Pretraining of Self-attention Networks
Authors: Alexei Baevski, Sergey Edunov, Yinhan Liu, Luke Zettlemoyer, Michael Auli
Date: 2019-03-19 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Constituency Parsing, Named Entity Recognition (NER), NER, Sentiment Analysis, Text Classification

We present a new approach for pretraining a bi-directional transformer model
that provides significant performance gains across a variety of language
understanding problems. Our model solves a cloze-style word reconstruction
task, where each word is ablated and must be predicted given the rest of the
text. Experiments demonstrate large performance gains on GLUE and new state of
the art results on NER as well as constituency parsing benchmarks, consistent
with the concurrently introduced BERT model. We also present a detailed
analysis of a number of factors that contribute to effective pretraining,
including data domain and size, model capacity, and variations on the cloze
objective.
Semiparametric Methods for Exposure Misclassification in Propensity Score-Based Time-to-Event Data Analysis
Authors: Yingrui Yang, Molin Wang
Date: 2019-03-19 00:00:00
Tasks: Epidemiology

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassification-caused bias in the estimation of exposure-outcome associations. We also discuss the asymptotic properties and derive the asymptotic variances of the proposed estimators. We conduct a simulation study to evaluate the performance of the proposed estimators in various settings. As an illustration, we apply our method to correct for the misclassification-caused bias in estimating the association of PM2.5 level with lung cancer mortality using a nationwide prospective cohort, the Nurses' Health Study (NHS). The proposed methodology can be applied using our user-friendly R function published online.
Neural Networks for Lorenz Map Prediction: A Trip Through Time
Authors: Denisa Roberts
Date: 2019-03-18 00:00:00

In this article the Lorenz dynamical system is revived and revisited and the current state of the art results for one step ahead forecasting for the Lorenz trajectories are published. Multitask learning is shown to help learning the hard to learn z trajectory. The article is a reflection upon the evolution of neural networks with respect to the prediction performance on this canonical task.
Lemotif: An Affective Visual Journal Using Deep Neural Networks
Authors: X. Alice Li, Devi Parikh
Date: 2019-03-18 00:00:00
Tasks: Image Generation

We present Lemotif, an integrated natural language processing and image generation system that uses machine learning to (1) parse a text-based input journal entry describing the user's day for salient themes and emotions and (2) visualize the detected themes and emotions in creative and appealing image motifs. Synthesizing approaches from artificial intelligence and psychology, Lemotif acts as an affective visual journal, encouraging users to regularly write and reflect on their daily experiences through visual reinforcement. By making patterns in emotions and their sources more apparent, Lemotif aims to help users better understand their emotional lives, identify opportunities for action, and track the effectiveness of behavioral changes over time. We verify via human studies that prospective users prefer motifs generated by Lemotif over corresponding baselines, find the motifs representative of their journal entries, and think they would be more likely to journal regularly using a Lemotif-based app.
Deep Reinforcement Learning with Decorrelation
Authors: Borislav Mavrin, Hengshuai Yao, Linglong Kong
Date: 2019-03-18 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, quantile regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (DQN) achieves remarkable success in computer games by learning deeply encoded representation from convolution networks. In this paper, we propose a simple yet very effective method for representation learning with DRL algorithms. Our key insight is that features learned by DRL algorithms are highly correlated, which interferes with learning. By adding a regularized loss that penalizes correlation in latent features (with only slight computation), we decorrelate features represented by deep neural networks incrementally. On 49 Atari games, with the same regularization factor, our decorrelation algorithms perform $70\%$ in terms of human-normalized scores, which is $40\%$ better than DQN. In particular, ours performs better than DQN on 39 games with 4 close ties and lost only slightly on $6$ games. Empirical results also show that the decorrelation method applies to Quantile Regression DQN (QR-DQN) and significantly boosts performance. Further experiments on the losing games show that our decorelation algorithms can win over DQN and QR-DQN with a fined tuned regularization factor.
Hierarchical Routing Mixture of Experts
Authors: Wenbo Zhao, Yang Gao, Shahan Ali Memon, Bhiksha Raj, Rita Singh
Date: 2019-03-18 00:00:00
Tasks: Mixture-of-Experts, regression

In regression tasks the distribution of the data is often too complex to be
fitted by a single model. In contrast, partition-based models are developed
where data is divided and fitted by local models. These models partition the
input space and do not leverage the input-output dependency of
multimodal-distributed data, and strong local models are needed to make good
predictions. Addressing these problems, we propose a binary tree-structured
hierarchical routing mixture of experts (HRME) model that has classifiers as
non-leaf node experts and simple regression models as leaf node experts. The
classifier nodes jointly soft-partition the input-output space based on the
natural separateness of multimodal data. This enables simple leaf experts to be
effective for prediction. Further, we develop a probabilistic framework for the
HRME model, and propose a recursive Expectation-Maximization (EM) based
algorithm to learn both the tree structure and the expert models. Experiments
on a collection of regression tasks validate the effectiveness of our method
compared to a variety of other regression models.
Galaxy classification: A machine learning analysis of GAMA catalogue data
Authors: Aleke Nolte, Lingyu Wang, Maciej Bilicki, Benne Holwerda, Michael Biehl
Date: 2019-03-18 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Quantization

We present a machine learning analysis of five labelled galaxy catalogues
from the Galaxy And Mass Assembly (GAMA): The SersicCatVIKING and
SersicCatUKIDSS catalogues containing morphological features, the
GaussFitSimple catalogue containing spectroscopic features, the MagPhys
catalogue including physical parameters for galaxies, and the Lambdar
catalogue, which contains photometric measurements. Extending work previously
presented at the ESANN 2018 conference - in an analysis based on Generalized
Relevance Matrix Learning Vector Quantization and Random Forests - we find that
neither the data from the individual catalogues nor a combined dataset based on
all 5 catalogues fully supports the visual-inspection-based galaxy
classification scheme employed to categorise the galaxies. In particular, only
one class, the Little Blue Spheroids, is consistently separable from the other
classes. To aid further insight into the nature of the employed visual-based
classification scheme with respect to physical and morphological features, we
present the galaxy parameters that are discriminative for the achieved class
distinctions.
Pairwise Comparisons with Flexible Time-Dynamics
Authors: Lucas Maystre, Victor Kristof, Matthias Grossglauser
Date: 2019-03-18 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. We achieve this by replacing the static parameters of a class of popular pairwise-comparison models by continuous-time Gaussian processes; the covariance function of these processes enables expressive dynamics. We develop an efficient inference algorithm that computes an approximate Bayesian posterior distribution. Despite the flexbility of our model, our inference algorithm requires only a few linear-time iterations over the data and can take advantage of modern multiprocessor computer architectures. We apply our model to several historical databases of sports outcomes and find that our approach outperforms competing approaches in terms of predictive performance, scales to millions of observations, and generates compelling visualizations that help in understanding and interpreting the data.
Learning with Sets in Multiple Instance Regression Applied to Remote Sensing
Authors: Thomas Uriot
Date: 2019-03-18 00:00:00
Tasks: regression

In this paper, we propose a novel approach to tackle the multiple instance regression (MIR) problem. This problem arises when the data is a collection of bags, where each bag is made of multiple instances corresponding to the same unique real-valued label. Our goal is to train a regression model which maps the instances of an unseen bag to its unique label. This MIR setting is common to remote sensing applications where there is high variability in the measurements and low geographical variability in the quantity being estimated. Our approach, in contrast to most competing methods, does not make the assumption that there exists a prime instance responsible for the label in each bag. Instead, we treat each bag as a set (i.e, an unordered sequence) of instances and learn to map each bag to its unique label by using all the instances in each bag. This is done by implementing an order-invariant operation characterized by a particular type of attention mechanism. This method is very flexible as it does not require domain knowledge nor does it make any assumptions about the distribution of the instances within each bag. We test our algorithm on five real world datasets and outperform previous state-of-the-art on three of the datasets. In addition, we augment our feature space by adding the moments of each feature for each bag, as extra features, and show that while the first moments lead to higher accuracy, there is a diminishing return.
Learning to Augment Synthetic Images for Sim2Real Policy Transfer
Authors: Alexander Pashevich, Robin Strudel, Igor Kalevatykh, Ivan Laptev, Cordelia Schmid
Date: 2019-03-18 00:00:00
Tasks: Object Localization

Vision and learning have made significant progress that could improve robotics policies for complex tasks and environments. Learning deep neural networks for image understanding, however, requires large amounts of domain-specific visual data. While collecting such data from real robots is possible, such an approach limits the scalability as learning policies typically requires thousands of trials. In this work we attempt to learn manipulation policies in simulated environments. Simulators enable scalability and provide access to the underlying world state during training. Policies learned in simulators, however, do not transfer well to real scenes given the domain gap between real and synthetic data. We follow recent work on domain randomization and augment synthetic images with sequences of random transformations. Our main contribution is to optimize the augmentation strategy for sim2real transfer and to enable domain-independent policy learning. We design an efficient search for depth image augmentations using object localization as a proxy task. Given the resulting sequence of random transformations, we use it to augment synthetic depth images during policy learning. Our augmentation strategy is policy-independent and enables policy learning with no real images. We demonstrate our approach to significantly improve accuracy on three manipulation tasks evaluated on a real robot.