Transformer Megatron Decepticons
Transformer models are being extensively investigated for various sequence processing tasks, moving beyond natural language processing to encompass time series forecasting, image recognition, and scientific computing applications like solving partial differential equations. Current research focuses on improving efficiency (e.g., through mixed-precision quantization and optimized architectures), enhancing generalization capabilities (particularly to longer sequences), and understanding the underlying mechanisms of in-context learning. These advancements have significant implications for diverse fields, improving the accuracy and efficiency of numerous applications while simultaneously deepening our theoretical understanding of these powerful models.
Papers - Page 28
FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification
Anthony Zhou, Amir Barati FarimaniLarge-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
Byung-Hoon Kim, Jungwon Choi, EungGu Yun, Kyungsang Kim, Xiang Li, Juho LeeImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation
Tong Nie, Guoyang Qin, Wei Ma, Yuewen Mei, Jian Sun
Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers
Vukasin Bozic, Danilo Dordevic, Daniele Coppola, Joseph Thommes, Sidak Pal SinghAdvancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers
Staphord Bengesi, Hoda El-Sayed, Md Kamruzzaman Sarker, Yao Houkpati, John Irungu, Timothy Oladunni