pwc-archive/papers-with-abstracts huggingface dataset:
Large Scale Holistic Video Understanding
Authors: Ali Diba, Mohsen Fayyaz, Vivek Sharma, Manohar Paluri, Jurgen Gall, Rainer Stiefelhagen, Luc van Gool
Date: 2019-04-25 00:00:00
Proceeding: ECCV 2020 8
Tasks: Action Classification, Action Recognition, Multi-Task Learning, Representation Learning, Temporal Action Localization, Video Captioning, Video Classification, Video Recognition, Video Understanding

Video recognition has been advanced in recent years by benchmarks with rich annotations. However, research is still mainly limited to human action or sports recognition - focusing on a highly specific video understanding task and thus leaving a significant gap towards describing the overall content of a video. We fill this gap by presenting a large-scale "Holistic Video Understanding Dataset"~(HVU). HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene. HVU contains approx.~572k videos in total with 9 million annotations for training, validation, and test set spanning over 3142 labels. HVU encompasses semantic aspects defined on categories of scenes, objects, actions, events, attributes, and concepts which naturally captures the real-world scenarios. We demonstrate the generalization capability of HVU on three challenging tasks: 1.) Video classification, 2.) Video captioning and 3.) Video clustering tasks. In particular for video classification, we introduce a new spatio-temporal deep neural network architecture called "Holistic Appearance and Temporal Network"~(HATNet) that builds on fusing 2D and 3D architectures into one by combining intermediate representations of appearance and temporal cues. HATNet focuses on the multi-label and multi-task learning problem and is trained in an end-to-end manner. Via our experiments, we validate the idea that holistic representation learning is complementary, and can play a key role in enabling many real-world applications.
META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation
Authors: Mingde Zhao, Sitao Luan, Ian Porada, Xiao-Wen Chang, Doina Precup
Date: 2019-04-25 00:00:00
Tasks: Meta-Learning, Reinforcement Learning

Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that learn the value of a given policy, as well as algorithms which learn how to improve policies. TD-learning with eligibility traces provides a way to boost sample efficiency by temporal credit assignment, i.e. deciding which portion of a reward should be assigned to predecessor states that occurred at different previous times, controlled by a parameter $\lambda$. However, tuning this parameter can be time-consuming, and not tuning it can lead to inefficient learning. For better sample efficiency of TD-learning, we propose a meta-learning method for adjusting the eligibility trace parameter, in a state-dependent manner. The adaptation is achieved with the help of auxiliary learners that learn distributional information about the update targets online, incurring roughly the same computational complexity per step as the usual value learner. Our approach can be used both in on-policy and off-policy learning. We prove that, under some assumptions, the proposed method improves the overall quality of the update targets, by minimizing the overall target error. This method can be viewed as a plugin to assist prediction with function approximation by meta-learning feature (observation)-based $\lambda$ online, or even in the control case to assist policy improvement. Our empirical evaluation demonstrates significant performance improvements, as well as improved robustness of the proposed algorithm to learning rate variation.
Time Series Simulation by Conditional Generative Adversarial Net
Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto
Date: 2019-04-25 00:00:00
Tasks: Time Series, Time Series Analysis

Generative Adversarial Net (GAN) has been proven to be a powerful machine
learning tool in image data analysis and generation. In this paper, we propose
to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time
series data. The conditions can be both categorical and continuous variables
containing different kinds of auxiliary information. Our simulation studies
show that CGAN is able to learn different kinds of normal and heavy tail
distributions, as well as dependent structures of different time series and it
can further generate conditional predictive distributions consistent with the
training data distributions. We also provide an in-depth discussion on the
rationale of GAN and the neural network as hierarchical splines to draw a clear
connection with the existing statistical method for distribution generation. In
practice, CGAN has a wide range of applications in the market risk and
counterparty risk analysis: it can be applied to learn the historical data and
generate scenarios for the calculation of Value-at-Risk (VaR) and Expected
Shortfall (ES) and predict the movement of the market risk factors. We present
a real data analysis including a backtesting to demonstrate CGAN is able to
outperform the Historic Simulation, a popular method in market risk analysis
for the calculation of VaR. CGAN can also be applied in the economic time
series modeling and forecasting, and an example of hypothetical shock analysis
for economic models and the generation of potential CCAR scenarios by CGAN is
given at the end of the paper.
Bayesian Search for Robust Optima
Authors: Nicholas D. Sanders, Richard M. Everson, Jonathan E. Fieldsend, Alma A. M. Rahat
Date: 2019-04-25 00:00:00
Tasks: Bayesian Optimisation

Many expensive black-box optimisation problems are sensitive to their inputs. In these problems it makes more sense to locate a region of good designs, than a single-possibly fragile-optimal design. Expensive black-box functions can be optimised effectively with Bayesian optimisation, where a Gaussian process is a popular choice as a prior over the expensive function. We propose a method for robust optimisation using Bayesian optimisation to find a region of design space in which the expensive function's performance is relatively insensitive to the inputs whilst retaining a good quality. This is achieved by sampling realisations from a Gaussian process that is modelling the expensive function, and evaluating the improvement for each realisation. The expectation of these improvements can be optimised cheaply with an evolutionary algorithm to determine the next location at which to evaluate the expensive function. We describe an efficient process to locate the optimum expected improvement. We show empirically that evaluating the expensive function at the location in the candidate uncertainty region about which the model is most uncertain, or at random, yield the best convergence in contrast to exploitative schemes. We illustrate our method on six test functions in two, five, and ten dimensions, and demonstrate that it is able to outperform two state-of-the-art approaches from the literature. We also demonstrate our method one two real-world problems in 4 and 8 dimensions, which involve training robot arms to push objects onto targets.
Assistive System in Conversational Agent for Health Coaching: The CoachAI Approach
Authors: Ahmed Fadhil
Date: 2019-04-25 00:00:00
Tasks: Chatbot

With increasing physicians' workload and patients' needs for care, there is a
need for technology that facilitates physicians work and performs continues
follow-up with patients. Existing approaches focus merely on improving
patient's condition, and none have considered managing physician's workload.
This paper presents an initial evaluation of a conversational agent assisted
coaching platform intended to manage physicians' fatigue and provide continuous
follow-up to patients. We highlight the approach adapted to build the chatbot
dialogue and the coaching platform. We will particularly discuss the activity
recommender algorithms used to suggest insights about patients' condition and
activities based on previously collected data. The paper makes three
contributions: (1) present the conversational agent as an assistive virtual
coach, (2) decrease physicians workload and continuous follow up with patients,
all by handling some repetitive physician tasks and performing initial follow
up with the patient, (3) present the activity recommender that tracks previous
activities and patient information and provides useful insights about possible
activity and patient match to the coach. Future work focuses on integrating the
recommender model with the CoachAI platform and test the prototype with
patient's in collaboration with an ambulatory clinic.
DynamoNet: Dynamic Action and Motion Network
Authors: Ali Diba, Vivek Sharma, Luc van Gool, Rainer Stiefelhagen
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Recognition, Classification, General Classification, Multi-Task Learning, Self-Supervised Learning, Temporal Action Localization, Video Classification

In this paper, we are interested in self-supervised learning the motion cues
in videos using dynamic motion filters for a better motion representation to
finally boost human action recognition in particular. Thus far, the vision
community has focused on spatio-temporal approaches using standard filters,
rather we here propose dynamic filters that adaptively learn the video-specific
internal motion representation by predicting the short-term future frames. We
name this new motion representation, as dynamic motion representation (DMR) and
is embedded inside of 3D convolutional network as a new layer, which captures
the visual appearance and motion dynamics throughout entire video clip via
end-to-end network learning. Simultaneously, we utilize these motion
representation to enrich video classification. We have designed the frame
prediction task as an auxiliary task to empower the classification problem.
With these overall objectives, to this end, we introduce a novel unified
spatio-temporal 3D-CNN architecture (DynamoNet) that jointly optimizes the
video classification and learning motion representation by predicting future
frames as a multi-task learning problem. We conduct experiments on challenging
human action datasets: Kinetics 400, UCF101, HMDB51. The experiments using the
proposed DynamoNet show promising results on all the datasets.
Prediction with Expert Advice: a PDE Perspective
Authors: Nadejda Drenska, Robert V. Kohn
Date: 2019-04-25 00:00:00
Tasks: Math, Prediction

This work addresses a classic problem of online prediction with expert
advice. We assume an adversarial opponent, and we consider both the
finite-horizon and random-stopping versions of this zero-sum, two-person game.
Focusing on an appropriate continuum limit and using methods from optimal
control, we characterize the value of the game as the viscosity solution of a
certain nonlinear partial differential equation. The analysis also reveals the
predictor's and the opponent's minimax optimal strategies. Our work provides,
in particular, a continuum perspective on recent work of Gravin, Peres, and
Sivan (Proc SODA 2016). Our techniques are similar to those of Kohn and Serfaty
(Comm Pure Appl Math 2010), where scaling limits of some two-person games led
to elliptic or parabolic PDEs.
Deep Constrained Dominant Sets for Person Re-identification
Authors: Leulseged Tesfaye Alemu, Marcello Pelillo, Mubarak Shah
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Clustering, Constrained Clustering, Image Retrieval, Person Re-Identification, Retrieval

In this work, we propose an end-to-end constrained clustering scheme to tackle the person re-identification (re-id) problem. Deep neural networks (DNN) have recently proven to be effective on person re-identification task. In particular, rather than leveraging solely a probe-gallery similarity, diffusing the similarities among the gallery images in an end-to-end manner has proven to be effective in yielding a robust probe-gallery affinity. However, existing methods do not apply probe image as a constraint, and are prone to noise propagation during the similarity diffusion process. To overcome this, we propose an intriguing scheme which treats person-image retrieval problem as a {\em constrained clustering optimization} problem, called deep constrained dominant sets (DCDS). Given a probe and gallery images, we re-formulate person re-id problem as finding a constrained cluster, where the probe image is taken as a constraint (seed) and each cluster corresponds to a set of images corresponding to the same person. By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images. We further enhance the performance by integrating an auxiliary net alongside DCDS, which employs a multi-scale Resnet. To validate the effectiveness of our method we present experiments on several benchmark datasets and show that the proposed method can outperform state-of-the-art methods.
Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework
Authors: Haoran Wang, Xun Yu Zhou
Date: 2019-04-25 00:00:00
Tasks: Continuous Control, Portfolio Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We approach the continuous-time mean-variance (MV) portfolio selection with reinforcement learning (RL). The problem is to achieve the best tradeoff between exploration and exploitation, and is formulated as an entropy-regularized, relaxed stochastic control problem. We prove that the optimal feedback policy for this problem must be Gaussian, with time-decaying variance. We then establish connections between the entropy-regularized MV and the classical MV, including the solvability equivalence and the convergence as exploration weighting parameter decays to zero. Finally, we prove a policy improvement theorem, based on which we devise an implementable RL algorithm. We find that our algorithm outperforms both an adaptive control based method and a deep neural networks based algorithm by a large margin in our simulations.
Identifying short-term interests from mobile app adoption pattern
Authors: Bharat Gaind, Nitish Varshney, Shubham Goel, Akash Mondal
Date: 2019-04-25 00:00:00

With the increase in an average user's dependence on their mobile devices,
the reliance on collecting his browsing history from mobile browsers has also
increased. This browsing history is highly utilized in the advertising industry
for providing targeted ads in the purview of inferring his short-term interests
and pushing relevant ads. However, the major limitation of such an extraction
from mobile browsers is that they reset when the browser is closed or when the
device is shut down/restarted; thus rendering existing methods to identify the
user's short-term interests on mobile devices users, ineffective. In this
paper, we propose an alternative method to identify such short-term interests
by analysing their mobile app adoption (installation/uninstallation) patterns
over a period of time. Such a method can be highly effective in pinpointing the
user's ephemeral inclinations like buying/renting an apartment, buying/selling
a car or a sudden increased interest in shopping (possibly due to a recent
salary bonus, he received). Subsequently, these derived interests are also used
for targeted experiments. Our experiments result in up to 93.68% higher
click-through rate in comparison to the ads shown without any user-interest
knowledge. Also, up to 51% higher revenue in the long term is expected as a
result of the application of our proposed algorithm.
Decentralized Multi-Task Learning Based on Extreme Learning Machines
Authors: Yu Ye, Ming Xiao, Mikael Skoglund
Date: 2019-04-25 00:00:00
Tasks: Multi-Task Learning

In multi-task learning (MTL), related tasks learn jointly to improve
generalization performance. To exploit the high learning speed of extreme
learning machines (ELMs), we apply the ELM framework to the MTL problem, where
the output weights of ELMs for all the tasks are learned collaboratively. We
first present the ELM based MTL problem in the centralized setting, which is
solved by the proposed MTL-ELM algorithm. Due to the fact that many data sets
of different tasks are geo-distributed, decentralized machine learning is
studied. We formulate the decentralized MTL problem based on ELM as majorized
multi-block optimization with coupled bi-convex objective functions. To solve
the problem, we propose the DMTL-ELM algorithm, which is a hybrid Jacobian and
Gauss-Seidel Proximal multi-block alternating direction method of multipliers
(ADMM). Further, to reduce the computation load of DMTL-ELM, DMTL-ELM with
first-order approximation (FO-DMTL-ELM) is presented. Theoretical analysis
shows that the convergence to the stationary point of DMTL-ELM and FO-DMTL-ELM
can be guaranteed conditionally. Through simulations, we demonstrate the
convergence of proposed MTL-ELM, DMTL-ELM, and FO-DMTL-ELM algorithms, and also
show that they can outperform existing MTL methods. Moreover, by adjusting the
dimension of hidden feature space, there exists a trade-off between
communication load and learning accuracy for DMTL-ELM.
Communication trade-offs for synchronized distributed SGD with large step size
Authors: Kumar Kshitij Patel, Aymeric Dieuleveut
Date: 2019-04-25 00:00:00

Synchronous mini-batch SGD is state-of-the-art for large-scale distributed
machine learning. However, in practice, its convergence is bottlenecked by slow
communication rounds between worker nodes. A natural solution to reduce
communication is to use the \emph{`local-SGD'} model in which the workers train
their model independently and synchronize every once in a while. This algorithm
improves the computation-communication trade-off but its convergence is not
understood very well. We propose a non-asymptotic error analysis, which enables
comparison to \emph{one-shot averaging} i.e., a single communication round
among independent workers, and \emph{mini-batch averaging} i.e., communicating
at every step. We also provide adaptive lower bounds on the communication
frequency for large step-sizes ($ t^{-\alpha} $, $ \alpha\in (1/2 , 1 ) $) and
show that \emph{Local-SGD} reduces communication by a factor of
$O\Big(\frac{\sqrt{T}}{P^{3/2}}\Big)$, with $T$ the total number of gradients
and $P$ machines.
Breast Cancer Classification with Ultrasound Images Based on SLIC
Authors: Zhihao Fang, Wanyi Zhang, He Ma
Date: 2019-04-25 00:00:00
Tasks: Cancer Classification, Classification, Clustering, General Classification

Ultrasound image diagnosis of breast tumors has been widely used in recent years. However, there are some problems of it, for instance, poor quality, intense noise and uneven echo distribution, which has created a huge obstacle to diagnosis. To overcome these problems, we propose a novel method, a breast cancer classification with ultrasound images based on SLIC (BCCUI). We first utilize the Region of Interest (ROI) extraction based on Simple Linear Iterative Clustering (SLIC) algorithm and region growing algorithm to extract the ROI at the super-pixel level. Next, the features of ROI are extracted. Furthermore, the Support Vector Machine (SVM) classifier is applied. The calculation states that the accuracy of this segment algorithm is up to 88.00% and the sensitivity of the algorithm is up to 92.05%, which proves that the classifier presents in this paper has certain research meaning and applied worthiness.
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Authors: Adrian V. Dalca, Evan Yu, Polina Golland, Bruce Fischl, Mert R. Sabuncu, Juan Eugenio Iglesias
Date: 2019-04-25 00:00:00
Tasks: Brain Image Segmentation, Brain Segmentation, Deep Learning, Few-Shot Semantic Segmentation, GPU, Image Registration, MRI segmentation, Segmentation, Zero Shot Segmentation

Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset. To develop a deep learning-based segmentation model for a new image dataset (e.g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without the need for any manually segmented images. Our experiments include thousands of brain MRI scans and demonstrate that the proposed method achieves good accuracy for a brain MRI segmentation task for different MRI contrasts, requiring only approximately 15 seconds at test time on a GPU. The code is freely available at http://voxelmorph.mit.edu.
Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning
Authors: Wei Shen, Zhenhuan Yang, Yiming Ying, Xiaoming Yuan
Date: 2019-04-25 00:00:00
Tasks: Generalization Bounds, Metric Learning

In this paper we study the stability and its trade-off with optimization
error for stochastic gradient descent (SGD) algorithms in the pairwise learning
setting. Pairwise learning refers to a learning task which involves a loss
function depending on pairs of instances among which notable examples are
bipartite ranking, metric learning, area under ROC (AUC) maximization and
minimum error entropy (MEE) principle. Our contribution is twofold. Firstly, we
establish the stability results of SGD for pairwise learning in the convex,
strongly convex and non-convex settings, from which generalization bounds can
be naturally derived. Secondly, we establish the trade-off between stability
and optimization error of SGD algorithms for pairwise learning. This is
achieved by lower-bounding the sum of stability and optimization error by the
minimax statistical error over a prescribed class of pairwise loss functions.
From this fundamental trade-off, we obtain lower bounds for the optimization
error of SGD algorithms and the excess expected risk over a class of pairwise
losses. In addition, we illustrate our stability results by giving some
specific examples of AUC maximization, metric learning and MEE.
JPEG XT Image Compression with Hue Compensation for Two-Layer HDR Coding
Authors: Hiroyuki Kobayashi, Hitoshi Kiya
Date: 2019-04-25 00:00:00
Tasks: Image Compression, Tone Mapping

We propose a novel JPEG XT image compression with hue compensation for
two-layer HDR coding. LDR images produced from JPEG XT bitstreams have some
distortion in hue due to tone mapping operations. In order to suppress the
color distortion, we apply a novel hue compensation method based on the
maximally saturated colors. Moreover, the bitstreams generated by using the
proposed method are fully compatible with the JPEG XT standard. In an
experiment, the proposed method is demonstrated not only to produce images with
small hue degradation but also to maintain well-mapped luminance, in terms of
three kinds of criterion: TMQI, hue value in CIEDE2000, and the maximally
saturated color on the constant-hue plane.