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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: recurrent neural networks ×Clear all

14 claims from 10 papers

1. Computer Science › Neural Networks and Applications

   Deep learning

   LeCun, Bengio and Hinton · Nature · 2015

   This review describes deep learning, its use of backpropagation, and its reported improvements in speech, image and other tasks, via convolutional and recurrent networks.

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   1. UncheckedDeep learning lets models built from many processing layers learn representations of data at several levels of abstraction.“Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.”

2. Computer Science › Advanced Neural Network Applications

   Neural Architecture Search with Reinforcement Learning

   Zoph and Le · arXiv (Cornell University) · 2016

   The authors train a recurrent network with reinforcement learning to design neural network architectures, which match or beat human-designed ones on CIFAR-10 and Penn Treebank.

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   1. UncheckedA recurrent cell designed by the authors' search method reaches a Penn Treebank test perplexity of 62.4, 3.6 better than the previous best model.“Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model.”

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

   Unified rational protein engineering with sequence-based deep representation learning

   Alley, Khimulya, Biswas, AlQuraishi and Church · Nature Methods · 2019

   The authors trained deep learning on unlabelled amino-acid sequences to make a protein representation, UniRep, and report it predicts stability and function competitively and improves efficiency in a protein engineering task.

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   1. UncheckedSimple models built on UniRep, a learned summary of protein sequences, are said to work across many tasks and on unseen regions of sequence space.“We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space.”
   2. UncheckedA model trained on unlabelled protein sequences predicts protein stability and mutant function about as well as leading existing methods.“Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods.”
   3. UncheckedThe paper states that UniRep, a learned summary of protein sequences, cut the effort needed in one protein engineering task by about a hundredfold.“UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task.”

4. Neuroscience › Memory and Neural Mechanisms

   Vector-based navigation using grid-like representations in artificial agents

   Banino, Barry, Uría et al. · Nature · 2018

   Researchers trained a recurrent network to path-integrate, producing grid-like units, then used these in a reinforcement learning agent that navigated challenging mazes better than comparison agents and an expert human.

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   1. UncheckedArtificial agents with grid-like representations could take shortcuts to goals, a behaviour reminiscent of that seen in mammals.“Furthermore, grid-like representations enabled agents to conduct shortcut behaviours reminiscent of those performed by mammals.”
   2. UncheckedGrid-like patterns that emerged in a trained artificial agent gave it a distance-based sense of space and the vector operations needed to navigate well.“Our findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation.”

5. Neuroscience › Motor Control and Adaptation

   A neural network that finds a naturalistic solution for the production of muscle activity

   Sussillo, Churchland, Kaufman and Shenoy · Nature Neuroscience · 2015

   Recurrent neural networks trained to reproduce reaching monkeys' muscle activity found a simple oscillator-like solution that closely resembled motor cortex recordings from the same monkeys.

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   1. UncheckedIn trained neural network models of reaching, the natural solution was a low-dimensional oscillator producing the multiphasic muscle commands needed.“Analysis of trained models revealed that the natural dynamical solution was a low-dimensional oscillator that generated the necessary multiphasic commands.”
   2. UncheckedSimulated networks matched monkey motor cortex recordings only when they were optimised to find simple solutions, according to the paper.“Notably, data and simulations agreed only when models were optimized to find simple solutions.”

6. Neuroscience › Memory and Neural Mechanisms

   Emergence of grid-like representations by training recurrent neural networks to perform spatial localization

   Cueva and Wei · arXiv (Cornell University) · 2018

   The authors trained recurrent neural networks to navigate 2D arenas from velocity inputs, and report that units resembling entorhinal grid cells, border cells and band-like cells emerged.

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   1. UncheckedRecurrent neural networks trained to navigate 2D arenas from velocity inputs developed grid-like, border-like and band-like spatial response patterns.“Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells.”

7. Neuroscience › Neural dynamics and brain function

   A diverse range of factors affect the nature of neural representations underlying short-term memory

   Orhan and Ma · Nature Neuroscience · 2019

   The authors trained recurrent neural networks on short-term memory tasks under varied circuit and task conditions to see when sequential or persistent activity emerges, and report a spectrum of solutions.

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   1. UncheckedIn trained recurrent networks, sequential and nearly persistent memory activity appear as points on one spectrum, depending on the conditions.“We show that both sequential and nearly persistent solutions are part of a spectrum that emerges naturally in trained networks under different conditions.”

8. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Temperature forecasting by deep learning methods

   Gong, Langguth, Ji et al. · Geoscientific model development · 2022

   Two deep learning models, ConvLSTM and SAVP, forecast hourly 2 m temperature over Europe for 12 hours from ERA5 data and beat persistence, though they remain less powerful than contemporary weather models.

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   1. UncheckedAdding 850 hPa temperature as a predictor improved the deep learning models' 2 m temperature forecasts, as did using a larger spatial domain.“Including the 850 hPa temperature as an additional predictor enhances the forecast quality, and the model also benefits from a larger spatial domain.”

9. 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.”

10. Computer Science › Speech Recognition and Synthesis

    Deep Neural Networks for Automatic Speaker Recognition Do Not Learn Supra-Segmental Temporal Features

    Neururer, Dellwo and Stadelmann · Zurich Open Repository and Archive (University of Zurich) · 2023

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    1. Unchecked“We find that a variety of CNN- and RNN-based neural network architectures for speaker recognition do not model SST to any sufficient degree, even when forced.”