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Found 7 papers, 1 papers with code
Stanford MLab at SemEval 2022 Task 7: Tree- and Transformer-Based Methods for Clarification Plausibility
In this paper, we detail the methods we used to determine the idiomaticity and plausibility of candidate words or phrases into an instructional text as part of the SemEval Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts.
DiffusionSat: A Generative Foundation Model for Satellite Imagery
Our method outperforms previous state-of-the-art methods for satellite image generation and is the first large-scale $\textit{generative}$ foundation model for satellite imagery.
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs.
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks.
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
Stanford MLab at SemEval-2021 Task 8: 48 Hours Is All You Need
This paper presents our system for the Quantity span identification, Unit of measurement identification and Value modifier classification subtasks of the MeasEval 2021 task.
Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings
This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction.
