Search Results for author:
Found 8 papers, 4 papers with code
Semantic Novelty Detection in Natural Language Descriptions
This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example.
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution
To initiate a systematic research in this important area of 'dealing with novelties', we introduce 'NoveltyTask', a multi-stage task to evaluate a system's performance on pipelined novelty 'detection' and 'accommodation' tasks.
Human Activity Recognition in an Open World
Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples.
Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities
Much of the existing work on text novelty detection has been studied at the topic level, i. e., identifying whether the topic of a document or a sentence is novel or not.
AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances.
Self-Initiated Open World Learning for Autonomous AI Agents
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can learn by themselves in a self-motivated and self-supervised manner rather than being retrained periodically on the initiation of human engineers using expanded training data.
Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Handwriting Recognition with Novelty
This paper introduces an agent-centric approach to handle novelty in the visual recognition domain of handwriting recognition (HWR).
