TimesFM is a decoder-only foundation model trained in a supervised way. It has a very high out-of-the-box zero-shot performance and can be used with any context size.
This paper runs a 48-week long clinical trial with Type 2 diabetic patients to assess the effectiveness of a digital health platform .
LEAD is an explainable AI approach that perturbs synthetic critical samples to generate consistent, sparse and robust explanations for disease classification.
This paper is a search based but fast counterfactual generation method for textual data. It proposes three operators to conduct the search in an anytime algorithm. Therefore, the more time it gets the better qulaity countefactual it delivers.
This paper introduces new constraints in the optimization method to generate realistic and feasible counterfactual explanations.
Since evaluation of counterfactual explanations is a difficult task, his paper tries to automate the process by fine-tuning large language models (LLMs) on human ratings for counterfactuals.
Using transformer based variational autoencoder and Gumbrl-Softmax, TABCF produces counterdactuals the are not biased towards changing the continuous features more often than the categorical features.
This paper proposes a DAG-GNN based VAE to reflect the inter-feature relationships in the produced counterfactuals.
This paper introduces a technique to produce synthetic counterfactual images to train a model free of bias.
CountER tells us under what conditions a chosen item would not be recommended to a person anymore.
