18 citations · 32 across the 18 of their papers we have counts for
6 papers · 1 filter
Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection
Xiang Fang, Arvind Easwaran, Blaise Genest +1
This paper focuses on a significant yet challenging task: out-of-distribution detection (OOD detection), which aims to distinguish and reject test samples with semantic shifts, so…
Your Data Is Not Perfect: Towards Cross-Domain Out-of-Distribution Detection in Class-Imbalanced Data
Xiang Fang, Arvind Easwaran, Blaise Genest +1
Previous OOD detection systems only focus on the semantic gap between ID and OOD samples. Besides the semantic gap, we are faced with two additional gaps: the domain gap between so…
Methods for Mitigating Uncertainty in Real-Time Operations of a Connected Microgrid
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran +2
In this paper, we compare the effectiveness of a two-stage control strategy for the energy management system (EMS) of a grid-connected microgrid under uncertain solar irradiance an…
Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection
Xiang Fang, Arvind Easwaran, Blaise Genest
Out-of-distribution (OOD) detection targets to detect and reject test samples with semantic shifts, to prevent models trained on in-distribution (ID) dataset from producing unrelia…
Vanilla Gradient Descent for Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran +1
Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, es…
Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
Eduardo de Conto, Blaise Genest, Arvind Easwaran
The development of digital twins (DTs) for physical systems increasingly leverages artificial intelligence (AI), particularly for combining data from different sources or for creat…