7 citations · 34 across the 25 of their papers we have counts for
4 papers · 1 filter
Hybrid Random Features
Krzysztof Choromanski, Haoxian Chen, Han Lin +10
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel es…
Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models
Onur Kara, Arijit Sehanobish, Hector H Corzo
Transformers are state-of-the-art deep learning models that are composed of stacked attention and point-wise, fully connected layers designed for handling sequential data. Transfor…
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
Krzysztof Choromanski, Han Lin, Haoxian Chen +7
In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable…
Learning Full Configuration Interaction Electron Correlations with Deep Learning
Hector H. Corzo, Arijit Sehanobish, Onur Kara
In this report, we present a deep learning framework termed the Electron Correlation Potential Neural Network (eCPNN) that can learn succinct and compact potential functions. These…