Papers Maximum Separation
“Maximum Separation” 태그가 달린 논문 12편 · 필터 해제
Multi-Objective Multidisciplinary Optimization of Wave Energy Converter Array Layout and Controls
This study utilizes multidisciplinary design optimization (MDO) to design an array of heaving wave energy converters (WECs) for grid-scale energy production with decision variables and parameters chosen from the coupled …
Maximum SeparationContrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data
Major solar flares are abrupt surges in the Sun's magnetic flux, presenting significant risks to technological infrastructure. In view of this, effectively predicting major flares from solar active region magnetic field …
Maximum SeparationRepresentation LearningSolar Flare PredictionTime Series+1Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning
Online continual learning requires the models to learn from constant, endless streams of data. While significant efforts have been made in this field, most were focused on mitigating the catastrophic forgetting issue to …
Continual LearningMaximum SeparationLightweight Uncertainty Quantification with Simplex Semantic Segmentation for Terrain Traversability
For navigation of robots, image segmentation is an important component to determining a terrain's traversability. For safe and efficient navigation, it is key to assess the uncertainty of the predicted segments. Current …
Image SegmentationMaximum SeparationSegmentationSemantic Segmentation+1Separation Power of Equivariant Neural Networks
The separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy for its expressivity. Indeed, knowing the separation power of a family of models…
Maximum SeparationTargeted Representation Alignment for Open-World Semi-Supervised Learning
Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data while unlabeled samples are not only from the labeled known categories but also from novel categories pre…
Maximum SeparationOpen-World Semi-Supervised LearningEnhancing Classification with Hierarchical Scalable Query on Fusion Transformer
Real-world vision based applications require fine-grained classification for various area of interest like e-commerce, mobile applications, warehouse management, etc. where reducing the severity of mistakes and improving…
ClassificationHierarchical Multi-label ClassificationMaximum SeparationComplexity of Representations in Deep Learning
Deep neural networks use multiple layers of functions to map an object represented by an input vector progressively to different representations, and with sufficient training, eventually to a single score for each class …
BenchmarkingDeep LearningMaximum SeparationMaximum Class Separation as Inductive Bias in One Matrix
Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not equipped with this inductive bias and t…
Inductive BiasLong-tail LearningMaximum SeparationOpen Set Learning+1Resilience in Optical Wireless Systems
High reliability and availability of communication services is a key requirement that needs to be ensured by service providers. Since the direct line-of-sight (LOS) beam is prone to blockage in indoor optical wireless co…
Maximum SeparationA Selection of Giant Radio Sources from NVSS
Results of the application of pattern recognition techniques to the problem of identifying Giant Radio Sources (GRS) from the data in the NVSS catalog are presented and issues affecting the process are explored. Decision…
ARCMaximum SeparationA new estimate of mutual information based measure of dependence between two variables: properties and fast implementation
This article proposes a new method to estimate an existing mutual information based dependence measure using histogram density estimates. Finding a suitable bin length for histogram is an open problem. We propose a new w…
Maximum Separation