Evidential Deep Learning (EDL), grounded in Evidence Theory and Subjective Logic (SL), provides a robust framework to estimate uncertainty for out-of-distribution (OOD) detection alongside traditional classification probabilities.

While tabular classification has traditionally relied on from-scratch training, a recent breakthrough called prior-data fitted networks (PFNs) challenges this approach. Similar to large language models, PFNs make use of pretraining and in-context learning to achieve strong performance on new tasks in a single forward pass.

Diffusion models have demonstrated remarkable success in various domains, including molecular generation.

Models like GPT -4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex tasks in real clinical settings.

We revisit this scenario as an experimental case study in Section 6.

Finally, we conclude by highlighting the significant applications of long-tailed learning and identifying several promising future directions.