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1,428 claims from 888 papers are on the record. 46 have been checked so far; the other 1,382 have no check with a result yet.

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Status: Unchecked ×Keyword: multi-task learning ×Clear all

10 claims from 6 papers

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Learning protein sequence embeddings using information from structure

   Bepler and Berger · PubMed · 2019

   The paper trains LSTM models to turn protein sequences into structure-informed embeddings, which predict structural similarity and improve transmembrane domain prediction.

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   1. UncheckedThe authors' multi-task model predicted protein structural similarity better than other sequence-based methods and a top structure-based alignment method.“We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead

   Chen, Han, Gong et al. · arXiv (Cornell University) · 2023

   FengWu is an AI weather forecasting system trained on 39 years of ERA5 data; in 2018 hindcasts it beat GraphCast on most predictands and extended skilful forecasts to 10.75 days.

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   1. UncheckedThe authors report that each forecasting step of FengWu takes only about 600 milliseconds to run on NVIDIA Tesla A100 hardware.“In addition, the inference cost of each iteration is merely 600ms on NVIDIA Tesla A100 hardware.”
   2. UncheckedThe paper suggests FengWu, an AI model, extends skilful global medium-range weather forecasts to 10.75 days, judged by z500 ACC above 0.6.“The results suggest that FengWu can significantly improve the forecast skill and extend the skillful global medium-range weather forecast out to 10.75 days lead (with ACC of z500 > 0.6) for the first time.”

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time

   Chen, Han, Ling et al. · Communications Earth & Environment · 2025

   The paper presents FengWu, an AI global medium-range forecasting system, and FengWu-Ensemble, a diffusion-based probabilistic version, which the authors report outperform leading physics-based and AI forecasts.

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   1. UncheckedFengWu-Ensemble, an AI ensemble forecast system, is reported to outperform the ECMWF Integrated Forecasting System Ensemble on several variables and metrics.“Comparative evaluations against the Integrated Forecasting System Ensemble show that FengWu-Ensemble achieves superior performance across multiple meteorological variables and evaluation metrics.”
   2. Unchecked“These enhancements allow FengWu to outperform deterministic forecasts produced by European Centre for Medium-Range Weather Forecasts High-Resolution Model, Pangu-Weather, and GraphCast.”
   3. UncheckedA deep learning weather model, FengWu, is reported to beat other machine learning models and the ECMWF model, giving accurate global forecasts beyond ten days.“A new deep learning-based global medium-range weather forecasting model outperforms existing machine learning models and the European Centre for Medium-Range Weather Forecasts model, producing accurate global weather forecasts beyond ten days.”

4. Materials Science › Machine Learning in Materials Science

   Leveraging language representation for materials exploration and discovery

   Qu, Xie, Ciesielski, Porter, Toberer and Ertekin · npj Computational Materials · 2024

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   1. Unchecked“The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share inform…

5. Computer Science › Topic Modeling

   Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

   Hsieh, Li, Yeh et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes.”

6. Computer Science › Advanced Neural Network Applications

   Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization

   Chen, Zuo, Chen et al. · arXiv (Cornell University) · 2021

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   1. Unchecked“In particular, we observe a phase transition phenomenon: As the compression ratio increases, generalization performance of the winning tickets first improves then deteriorates after a certain threshold.”
   2. Unchecked“Our experiments on the GLUE benchmark show that the super tickets improve single task fine-tuning by $0.9$ points on BERT-base and $1.0$ points on BERT-large, in terms of task-average score.”