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Found 8 papers, 0 papers with code
Cobweb: An Incremental and Hierarchical Model of Human-Like Category Learning
Cobweb, a human like category learning system, differs from other incremental categorization models in constructing hierarchically organized cognitive tree-like structures using the category utility measure.
Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure
Detecting elevated intracranial pressure (ICP) is crucial in diagnosing and managing various neurological conditions.
Avoiding Catastrophic Forgetting in Visual Classification Using Human Concept Formation
Deep neural networks have excelled in machine learning, particularly in vision tasks, however, they often suffer from catastrophic forgetting when learning new tasks sequentially.
VAL: Interactive Task Learning with GPT Dialog Parsing
By using LLMs only for specific tasks--such as predicate and argument selection--within an algorithmic framework, VAL reaps the benefits of LLMs to support interactive learning of hierarchical task knowledge from natural language.
Interactive Learning of Hierarchical Tasks from Dialog with GPT
We present a system for interpretable, symbolic, interactive task learning from dialog using a GPT model as a conversational front-end.
Efficient Induction of Language Models Via Probabilistic Concept Formation
The framework builds on Cobweb, an early system for constructing taxonomic hierarchies of probabilistic concepts that used a tabular, attribute-value encoding of training cases and concepts, making it unsuitable for sequential input like language.
MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples
Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside.
Convolutional Cobweb: A Model of Incremental Learning from 2D Images
This paper presents a new concept formation approach that supports the ability to incrementally learn and predict labels for visual images.
