1,587 research outputs found
ARDA: Automatic Relational Data Augmentation for Machine Learning
Automatic machine learning (\AML) is a family of techniques to automate the
process of training predictive models, aiming to both improve performance and
make machine learning more accessible. While many recent works have focused on
aspects of the machine learning pipeline like model selection, hyperparameter
tuning, and feature selection, relatively few works have focused on automatic
data augmentation. Automatic data augmentation involves finding new features
relevant to the user's predictive task with minimal ``human-in-the-loop''
involvement.
We present \system, an end-to-end system that takes as input a dataset and a
data repository, and outputs an augmented data set such that training a
predictive model on this augmented dataset results in improved performance. Our
system has two distinct components: (1) a framework to search and join data
with the input data, based on various attributes of the input, and (2) an
efficient feature selection algorithm that prunes out noisy or irrelevant
features from the resulting join. We perform an extensive empirical evaluation
of different system components and benchmark our feature selection algorithm on
real-world datasets
Skeleton-based Relational Reasoning for Group Activity Analysis
Research on group activity recognition mostly leans on the standard
two-stream approach (RGB and Optical Flow) as their input features. Few have
explored explicit pose information, with none using it directly to reason about
the persons interactions. In this paper, we leverage the skeleton information
to learn the interactions between the individuals straight from it. With our
proposed method GIRN, multiple relationship types are inferred from independent
modules, that describe the relations between the body joints pair-by-pair.
Additionally to the joints relations, we also experiment with the previously
unexplored relationship between individuals and relevant objects (e.g.
volleyball). The individuals distinct relations are then merged through an
attention mechanism, that gives more importance to those individuals more
relevant for distinguishing the group activity. We evaluate our method in the
Volleyball dataset, obtaining competitive results to the state-of-the-art. Our
experiments demonstrate the potential of skeleton-based approaches for modeling
multi-person interactions.Comment: 26 pages, 5 figures, accepted manuscript in Elsevier Pattern
Recognition, minor writing revisions and new reference
Confluence of Vision and Natural Language Processing for Cross-media Semantic Relations Extraction
In this dissertation, we focus on extracting and understanding semantically meaningful relationships between data items of various modalities; especially relations between images and natural language. We explore the ideas and techniques to integrate such cross-media semantic relations for machine understanding of large heterogeneous datasets, made available through the expansion of the World Wide Web. The datasets collected from social media websites, news media outlets and blogging platforms usually contain multiple modalities of data. Intelligent systems are needed to automatically make sense out of these datasets and present them in such a way that humans can find the relevant pieces of information or get a summary of the available material. Such systems have to process multiple modalities of data such as images, text, linguistic features, and structured data in reference to each other. For example, image and video search and retrieval engines are required to understand the relations between visual and textual data so that they can provide relevant answers in the form of images and videos to the users\u27 queries presented in the form of text. We emphasize the automatic extraction of semantic topics or concepts from the data available in any form such as images, free-flowing text or metadata. These semantic concepts/topics become the basis of semantic relations across heterogeneous data types, e.g., visual and textual data. A classic problem involving image-text relations is the automatic generation of textual descriptions of images. This problem is the main focus of our work. In many cases, large amount of text is associated with images. Deep exploration of linguistic features of such text is required to fully utilize the semantic information encoded in it. A news dataset involving images and news articles is an example of this scenario. We devise frameworks for automatic news image description generation based on the semantic relations of images, as well as semantic understanding of linguistic features of the news articles
Adaptive sliding windows for improved estimation of data center resource utilization
Accurate prediction of data center resource utilization is required for capacity planning, job scheduling, energy saving, workload placement, and load balancing to utilize the resources efficiently. However, accurately predicting those resources is challenging due to dynamic workloads, heterogeneous infrastructures, and multi-tenant co-hosted applications. Existing prediction methods use fixed size observation windows which cannot produce accurate results because of not being adaptively adjusted to capture local trends in the most recent data. Therefore, those methods train on large fixed sliding windows using an irrelevant large number of observations yielding to inaccurate estimations or fall for inaccuracy due to degradation of estimations with short windows on quick changing trends. In this paper we propose a deep learning-based adaptive window size selection method, dynamically limiting the sliding window size to capture the trend for the latest resource utilization, then build an estimation model for each trend period. We evaluate the proposed method against multiple baseline and state-of-the-art methods, using real data-center workload data sets. The experimental evaluation shows that the proposed solution outperforms those state-of-the-art approaches and yields 16 to 54% improved prediction accuracy compared to the baseline methods.This work is partially supported by the European ResearchCouncil (ERC) under the EU Horizon 2020 programme(GA 639595), the Spanish Ministry of Economy, Industry andCompetitiveness (TIN2015-65316-P and IJCI2016-27485), theGeneralitat de Catalunya, Spain (2014-SGR-1051) and Universityof the Punjab, Pakistan. The statements made herein are solelythe responsibility of the authors.Peer ReviewedPostprint (published version
Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: beyond edge weights in psychological networks
Uncertainty over model structures poses a challenge
for many approaches exploring effect strength parameters at
system-level. Monte Carlo methods for full Bayesian model
averaging over model structures require considerable computational
resources, whereas bootstrapped graphical lasso and its
approximations offer scalable alternatives with lower complexity.
Although the computational efficiency of graphical lasso based
approaches has prompted growing number of applications, the
restrictive assumptions of this approach are frequently ignored,
such as its lack of coping with interactions. We demonstrate
using an artificial and a real-world example that full Bayesian
averaging using Bayesian networks provides detailed estimates
through posterior distributions for structural and parametric
uncertainties and it is a feasible alternative, which is routinely
applicable in mid-sized biomedical problems with hundreds of
variables. We compare Bayesian estimates with corresponding
frequentist quantities from bootstrapped graphical lasso using
pairwise Markov Random Fields, discussing also their interpretational
differences. We present results using synthetic data from
an artificial model and using the UK Biobank data set to explore
a psychopathological network centered around depression (this
research has been conducted using the UK Biobank Resource
under Application Number 1602)
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