Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Machine-learned climate model corrections from a global storm-resolving model: Performance across the annual cycle
One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections to the prognosed model tendencies, such that the climate model…
I can’t believe there’s no images! : Learning Visual Tasks Using Only Language Supervision
Many high-level skills that are required for computer vision tasks, such as parsing questions, comparing and contrasting semantics, and writing descriptions, are also required in other domains such…
Pace v0.1: A python-based performance-portable implementation of the FV3 dynamical core
Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software…
Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations
Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution global atmosphere model with real…
Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction
Extracting temporal relationships between pairs of events in texts is a crucial yet challenging problem for natural language understanding. Depending on the distance between the events, models must…
Efficient Methods for Natural Language Processing: A Survey
Getting the most out of limited resources allows advances in natural language processing (NLP) research and practice while being con-servative with resources. Those resources may be data, time,…
MetaICL: Learning to Learn In Context
We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set…
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks
Retrieval-augmented generation models have shown state-of-the-art performance across many knowledge-intensive NLP tasks such as open-domain question answering and fact verification. These models are…
Robust fine-tuning of zero-shot models
Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset).…
Noisy Channel Language Model Prompting for Few-Shot Text Classification
We introduce a noisy channel approach for language model prompting in few-shot text classification. Instead of computing the likelihood of the label given the input (referred as direct models),…