Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this pa-per, we explore…
Abstract Visual Reasoning with Tangram Shapes
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build a richly…
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation
The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challenging phenomenon for…
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box…
ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding. This work presents a platform to enable similar success stories…
Emulating Fast Processes in Climate Models
Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating…
Machine-learned climate model corrections from a global storm-resolving model
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution ( (cid:38) 50 km) than is optimal for accurately resolving important…
Improving the predictions of ML-corrected climate models with novelty detection
While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than…
Modeling the Machine Learning Multiverse
Amid mounting concern about the reliability and credibility of machine learning research, we present a principled framework for making robust and generalizable claims: the multiverse analysis . Our…
Ask4Help: Learning to Leverage an Expert for Embodied Tasks
Embodied AI agents continue to become more capable every year with the advent of new models, environments, and benchmarks, but are still far away from being performant and reliable enough to be…