Chin-Jou Li

Many-shot in-context learning has recently shown promise as an alternative to finetuning, with the major advantage that the same model can be served for multiple tasks. However, this shifts the computational burden from training-time to inference-time, making deployment of many-shot ICL challenging to justify in-practice. This cost is further increased if a custom demonstration set is retrieved for each inference example. We present Dynamic Block-Sparse Attention, a training-free framework for retrieval-based many-shot in-context learning. By combining carefully designed block-sparse attention and retrieval of cached groups of demonstrations, we achieve comparable per-example latency to finetuning while maintaining on average >95% of the best method’s accuracy across strong ICL and finetuning baselines. We hope that this will further enable the deployment of many-shot ICL at scale.

Accurate classification of epileptic seizure types through seizure semiology analysis demands significant clinical expertise. While previous studies have employed various action recognition modules, the scarcity of labeled clinical videos has hindered the deployment of larger models. In this study, we explore unlabeled data to pretrain a transformer-based model with contrastive loss, taking advantage of the information that circumvents the need for additional annotation from medical professionals. We maximize the similarity between embeddings from the same patient and video while minimizing those from different patients and videos. Subsequently, a classification head was finetuned to distinguishing temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (exTLE), achieving a 5-fold accuracy of 0.93 and an F1 score of 0.88 on the video level (N = 57). Our results outperformed other state-of-the-art seizure classification models, demonstrating the efficacy of our approach. This suggests potential applications in clinical practice, where unlabeled data could serve as a valuable aid in improving seizure classification accuracy and patient care.