MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

Metric Methods in Data Mining

2011 ◽  
pp. 1-31 ◽  
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

scholarly journals Explanation-Based Human Debugging of NLP Models: A Survey

2021 ◽  
Vol 9 ◽  
pp. 1508-1528
Author(s):  
Piyawat Lertvittayakumjorn ◽  
Francesca Toni
Keyword(s):  
Learning Model ◽  
Training Data ◽  
Three Dimensions ◽  
Future Research ◽  
Experimental Setting ◽  
Open Problems ◽  
Research Directions ◽  
Machine Learning Model ◽  
Future Research Directions ◽  
Deep Learning Model

Abstract Debugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if we have no clue about how the model actually works. In this survey, we review papers that exploit explanations to enable humans to give feedback and debug NLP models. We call this problem explanation-based human debugging (EBHD). In particular, we categorize and discuss existing work along three dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect the feedback providers, and highlight open problems that could be future research directions.

scholarly journals Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities

2021 ◽  
Vol 14 ◽  
Author(s):  
Andrea Cherubini ◽  
David Navarro-Alarcon
Keyword(s):  
Integration Method ◽  
Control Level ◽  
Direct Interaction ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Heterogeneous Sensors ◽  
Challenges And Opportunities ◽  
Potential Applications ◽  
Future Research Directions

The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formulation of the sensor-servo problem, and then, present its most common approaches: vision-based, touch-based, audio-based, and distance-based control. Afterwards, we discuss and formalize the methods that integrate heterogeneous sensors at the control level. The surveyed body of literature is classified according to various factors such as: sensor type, sensor integration method, and application domain. Finally, we discuss open problems, potential applications, and future research directions.

scholarly journals Topological phases in ring resonators: recent progress and future prospects

Nanophotonics ◽  
2020 ◽  
Vol 9 (15) ◽  
pp. 4473-4487 ◽  
Author(s):  
Daniel Leykam ◽  
Luqi Yuan
Keyword(s):  
Ring Resonator ◽  
Ring Resonators ◽  
Future Research ◽  
Device Performance ◽  
Open Problems ◽  
Research Directions ◽  
Optical Isolation ◽  
Potential Applications ◽  
Future Research Directions ◽  
Light Generation

AbstractTopological photonics has emerged as a novel paradigm for the design of electromagnetic systems from microwaves to nanophotonics. Studies to date have largely focused on the demonstration of fundamental concepts, such as nonreciprocity and waveguiding protected against fabrication disorder. Moving forward, there is a pressing need to identify applications where topological designs can lead to useful improvements in device performance. Here, we review applications of topological photonics to ring resonator–based systems, including one- and two-dimensional resonator arrays, and dynamically modulated resonators. We evaluate potential applications such as quantum light generation, disorder-robust delay lines, and optical isolation, as well as future research directions and open problems that need to be addressed.

scholarly journals A Survey of Opponent Modeling in Adversarial Domains

2022 ◽  
Vol 73 ◽  
pp. 277-327
Author(s):  
Samer Nashed ◽  
Shlomo Zilberstein
Keyword(s):  
Method Comparison ◽  
Future Research ◽  
Comprehensive Overview ◽  
Open Problems ◽  
Research Directions ◽  
Opponent Modeling ◽  
Modeling Systems ◽  
Future Research Directions ◽  
Partially Observable ◽  
New Framework

Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.

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