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1,251 claims from 786 papers are on the record. 46 have been checked so far; the other 1,205 have no check with a result yet.

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Status: Unchecked ×Keyword: computational efficiency ×Clear all

6 claims from 5 papers

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

   Fast and accurate protein structure search with Foldseek

   van Kempen, Kim, Tumescheit et al. · Nature Biotechnology · 2023

   The paper presents Foldseek, a tool that searches large protein structure databases by turning 3D structure into sequences over a structural alphabet, aiming to remove a growing search bottleneck.

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   1. UncheckedFoldseek searches protein structures four to five orders of magnitude faster than Dali, TM-align and CE, at 86%, 88% and 133% of their sensitivities.“Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of Dali, TM-align and CE, respectively.”

2. Computer Science › Advanced Neural Network Applications

   ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

   Zhang, Zhou, Lin and Sun · arXiv (Cornell University) · 2017

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   1. UncheckedShuffleNet uses pointwise group convolution and channel shuffle to cut computation substantially while keeping accuracy.“The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy.”
   2. UncheckedShuffleNet is reported to have a 7.8% lower absolute top-1 error than MobileNet on ImageNet classification at a 40 MFLOPs computation budget.“Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs.”

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

   Single-sequence protein structure prediction using a language model and deep learning

   Chowdhury, Bouatta, Biswas et al. · Nature Biotechnology · 2022

   The authors built RGN2, a deep-learning system with a protein language model that predicts structure from a single sequence, aimed at cases where alignment-based tools such as AlphaFold2 struggle.

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   1. UncheckedOn average, RGN2 predicted structures of orphan proteins and some designed proteins better than AlphaFold2 and RoseTTAFold, using up to a millionfold less compute time.“On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 10 6 -fold reduction in compute time.”

4. Environmental Science › Climate variability and models

   A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate

   Cresswell‐Clay, Liu, Durran et al. · AGU Advances · 2025

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   1. Unchecked“DLESyM simulations equal or exceed key metrics of seasonal and interannual variability--such as tropical cyclogenesis over the range of observed intensities, the cycle of the Indian Summer monsoon, and the climatology of mid-latitude blocking events--when co…

5. Computer Science › Topic Modeling

   RWKV: Reinventing RNNs for the Transformer Era

   Peng, Alcaide, Anthony et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference.”