Deep Learning
Transformers & LLMs
Attention mechanisms, scaling behavior, and the practical engineering of training and serving large models efficiently.
AttentionLinformerFine-tuningInference efficiency
Focused on the mechanics of transformer architectures — attention patterns, positional encodings, efficient variants like Linformer, and efficient inference — and on applying them both to language and to sequences where order and locality carry different meaning than in text.