1 code implementation • EMNLP 2021 • Nianzu Ma, Alexander Politowicz, Sahisnu Mazumder, Jiahua Chen, Bing Liu, Eric Robertson, Scott Grigsby
This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example.
no code implementations • 8 May 2023 • Neeraj Varshney, Himanshu Gupta, Eric Robertson, Bing Liu, Chitta Baral
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.
no code implementations • 23 Dec 2022 • Derek S. Prijatelj, Samuel Grieggs, Jin Huang, Dawei Du, Ameya Shringi, Christopher Funk, Adam Kaufman, Eric Robertson, Walter J. Scheirer
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.
1 code implementation • 31 Oct 2022 • Nianzu Ma, Sahisnu Mazumder, Alexander Politowicz, Bing Liu, Eric Robertson, Scott Grigsby
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.
no code implementations • 17 Mar 2022 • Bing Liu, Sahisnu Mazumder, Eric Robertson, Scott Grigsby
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.
no code implementations • 21 Oct 2021 • Bing Liu, Eric Robertson, Scott Grigsby, Sahisnu Mazumder
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.
1 code implementation • 6 Sep 2021 • Sepideh Esmaeilpour, Bing Liu, Eric Robertson, Lei Shu
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
1 code implementation • 13 May 2021 • Derek S. Prijatelj, Samuel Grieggs, Futoshi Yumoto, Eric Robertson, Walter J. Scheirer
This paper introduces an agent-centric approach to handle novelty in the visual recognition domain of handwriting recognition (HWR).