9 citations · 17 across the 5 of their papers we have counts for
5 papers
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
Dongxia Wu, Tsuyoshi Idé, Aurélie Lozano +5
We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level c…
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation
Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan +3
Protein function annotation is an important yet challenging task in biology. Recent deep learning advancements show significant potential for accurate function prediction by learni…
Structure-Informed Protein Language Model
Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan +3
Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. However, traditional protein language model…
Generalized Kalman Smoothing: Modeling and Algorithms
A. Y. Aravkin, J. V. Burke, L. Ljung +2
State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions,…
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
Vikas Sindhwani, Ha Quang Minh, Aurelie Lozano
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed…