10 papers
Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines
Eduardo de Conto, Blaise Genest, Arvind Easwaran +2
Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instan…
Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran
(Flat) Reinforcement Learning (RL) agents face significant challenges in environments with sparse rewards that require long-horizon reasoning. A compelling approach to improve samp…
Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection
Xiang Fang, Arvind Easwaran, Blaise Genest
Out-of-distribution (OOD) detection attempts to distinguish outlier samples to prevent models trained on the in-distribution (ID) dataset from producing unavailable outputs. Most O…
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…
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…