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1,390 claims from 864 papers are on the record. 46 have been checked so far; the other 1,344 have no check with a result yet.

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Status: Unchecked ×Keyword: unsupervised learning ×Clear all

8 claims from 5 papers

1. Neuroscience › Visual perception and processing mechanisms

   Emergence of simple-cell receptive field properties by learning a sparse code for natural images

   Olshausen and Field · Nature · 1996

   A learning algorithm that seeks sparse linear codes for natural images develops localized, oriented, bandpass receptive fields like those of simple cells in primary visual cortex.

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   1. UncheckedA sparse code learned from natural images is described as a more efficient representation because its outputs are more statistically independent.“The resulting sparse image code provides a more efficient representation for later stages of processing because it possesses a higher degree of statistical independence among its outputs.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

   Rives, Meier, Sercu et al. · Proceedings of the National Academy of Sciences · 2021

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   1. Unchecked“We find that without prior knowledge, information emerges in the learned representations on fundamental properties of proteins such as secondary structure, contacts, and biological activity.”
   2. Unchecked“Unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure and improves state-of-the-art features for long-range contact prediction.”

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

   ProtGPT2 is a deep unsupervised language model for protein design

   Ferruz, Schmidt and Höcker · Nature Communications · 2022

   The authors describe ProtGPT2, a language model trained on protein sequences that generates new protein sequences resembling natural ones, yet distantly related to them and covering unexplored regions of protein space.

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   1. UncheckedProteins generated by the ProtGPT2 language model show natural amino acid propensities, and disorder predictions suggest 88% are globular, like natural sequences.“The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences.”
   2. UncheckedDatabase searches suggest ProtGPT2's generated protein sequences are only distantly related to natural ones and sample unexplored regions of protein space.“Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space.”

4. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Language models enable zero-shot prediction of the effects of mutations on protein function

   Meier, Rao, Verkuil, Liu, Sercu and Rives · bioRxiv (Cold Spring Harbor Laboratory) · 2021

   The paper shows that protein language models can predict how sequence changes affect function without task-specific training, rather than fitting a new model to each family of related sequences.

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   1. UncheckedProtein language models, used zero-shot with no experimental data or extra training, predict the functional effects of sequence variation at state-of-the-art level.“We show that using only zero-shot inference, without any supervision from experimental data or additional training, protein language models capture the functional effects of sequence variation, performing at state-of-the-art.”

5. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

   Rives, Meier, Sercu et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2019

   A deep language model trained without labels on 250 million protein sequences learns representations that encode biological properties, structure and function, and support state-of-the-art predictions.

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   1. UncheckedA protein language model's internal representations are organised across scales, from amino acid chemistry up to distant evolutionary relationships between proteins.“The learned representation space has a multi-scale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins.”
   2. UncheckedA protein language model trained only on sequences is reported to encode secondary and tertiary structure, readable by simple linear projections.“Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections.”