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Found 10 papers, 2 papers with code
Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text
The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing.
On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis
In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective.
Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter
Vaccine hesitancy continues to be a main challenge for public health officials during the COVID-19 pandemic.
Toward Super-Resolution for Appearance-Based Gaze Estimation
The proposed method consistently outperforms the state-of-the-art, particularly in scenarios involving low-resolution or degraded images.
Interpretable Few-shot Learning with Online Attribute Selection
In this paper, we propose an inherently interpretable model for FSL based on human-friendly attributes.
Interpretable Concept-based Prototypical Networks for Few-Shot Learning
In this paper, we propose a method for FSL based on a set of human-interpretable concepts.
Measuring Cognitive Status from Speech in a Smart Home Environment
We then present an overview of the preliminary results from pilot studies on active and passive smart home speech sensing for the measurement of cognitive health, and conclude with some recommendations and challenge statements for the next wave of work in this area, to help overcome both technical and ethical barriers to success.
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space.
Recent advances in machine learning have brought opportunities for the ever-increasing use of AI in the real world.
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models
In this work, we propose a method for offering local explanations on risk in reinforcement learning.
Cause and Effect: Hierarchical Concept-based Explanation of Neural Networks
We propose a framework to check the existence of a causal relationship between a concept (or its negation) and task classes.
