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Found 8 papers, 4 papers with code
Egocentric Hierarchical Visual Semantics
We achieve this goal by implementing an algorithm which, for any object, recursively recognizes its visual genus and its visual differentia.
Concept-level Debugging of Part-Prototype Networks
We propose ProtoPDebug, an effective concept-level debugger for ProtoPNets in which a human supervisor, guided by the model's explanations, supplies feedback in the form of what part-prototypes must be forgotten or kept, and the model is fine-tuned to align with this supervision.
Lifelong Personal Context Recognition
We focus on the development of AIs which live in lifelong symbiosis with a human.
Toward a Unified Framework for Debugging Concept-based Models
In this paper, we tackle interactive debugging of "gray-box" concept-based models (CBMs).
Streaming and Learning the Personal Context
The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency.
Interactive Label Cleaning with Example-based Explanations
We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples.
Human-in-the-loop Handling of Knowledge Drift
Motivated by this, we introduce TRCKD, a novel approach that combines automated drift detection and adaptation with an interactive stage in which the user is asked to disambiguate between different kinds of KD.
Learning in the Wild with Incremental Skeptical Gaussian Processes
The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI.
