A5065995677- Domain Adaptation and Few-Shot Learning
- Multimodal Machine Learning Applications
- RNA and protein synthesis mechanisms
- Machine Learning and Data Classification
- Advanced Neural Network Applications
- Influenza Virus Research Studies
- COVID-19 epidemiological studies
- Genomics and Phylogenetic Studies
- Language and cultural evolution
- Neonatal and fetal brain pathology
- Data Stream Mining Techniques
- Vibrio bacteria research studies
New York Consortium in Evolutionary Primatology
2025
Harvard University Press
2021-2023
Boston Children's Hospital
2021-2023
More than three billion years of evolution have produced an image biology encoded into the space natural proteins. Here we show that language models trained at scale on evolutionary data can generate functional proteins are far away from known We present ESM3, a frontier multimodal generative model reasons over sequence, structure, and function ESM3 follow complex prompts combining its modalities is highly responsive to alignment improve fidelity. prompted fluorescent Among generations...
Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learning classify objects by training on nonrepeating video frames in temporal order (online stream learning), models that learn well shuffled datasets catastrophically forget old upon new stimuli. We propose a continual algorithm, compositional replay using memory blocks (CRUMB), which mitigates forgetting replaying...
The ongoing Yemen cholera outbreak has been deemed one of the worst outbreaks in history, with over a million people impacted and thousands dead. Triggered by civil war, shaped various political, environmental, epidemiological factors continues to worsen. While several effective treatments, untimely inefficient distribution existing medicines primary cause mortality. With hope facilitating resource allocation, mathematical models have created track Yemeni identify at-risk administrative...
Our brains extract durable, generalizable knowledge from transient experiences of the world. Artificial neural networks come nowhere close to this ability. When tasked with learning classify objects by training on non-repeating video frames in temporal order (online stream learning), models that learn well shuffled datasets catastrophically forget old upon new stimuli. We propose a continual algorithm, Compositional Replay Using Memory Blocks (CRUMB), which mitigates forgetting replaying...