66 citations · 132 across the 20 of their papers we have counts for
20 papers
Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
Nabin Giri, Steven Farrell, Kristofer E. Bouchard
Multimodal models that jointly reason over protein sequences, structures, and function annotations within a unified representation hold immense potential for integrating multimodal…
Perspectives for self-driving labs in synthetic biology
Hector Garcia Martin, Tijana Radivojevic, Jeremy Zucker +19
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs co…
Compressed Predictive Information Coding
Rui Meng, Tianyi Luo, Kristofer Bouchard
Unsupervised learning plays an important role in many fields, such as artificial intelligence, machine learning, and neuroscience. Compared to static data, methods for extracting l…
Learning from learning machines: a new generation of AI technology to meet the needs of science
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin +33
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science cre…
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model
Rui Meng, Kristofer Bouchard
Many modern time-series datasets contain large numbers of output response variables sampled for prolonged periods of time. For example, in neuroscience, the activities of 100s-1000…
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network
Rui Meng, Herbie Lee, Kristofer Bouchard
This paper presents an efficient variational inference framework for deriving a family of structured gaussian process regression network (SGPRN) models. The key idea is to incorpor…