Recurrent signal flow enables recycling of limited computational resources over time, and so might boost the performance of a physically finite brain or model. Here we show: (1) Recurrent convolutional neural network models outperform feed- forward convolutional models matched in the...
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by samp...
Deep belief net Convolutional neural network Recurrent neural network Reinforcement learning to neural network Use cases Driverless vehicles Virtual assistants chatbots Medical research Facial recognition Robotics robot is controlled by remote Robotics is a branch of engineering that involves the conception, d...
Long short term memory (LSTM) is a type of recurrent neural networks (RNN), which allows modelling temporal dynamic behaviour by incorporating feedback connections in their architecture35. Our exploratory and reward-oblivious models are both LSTM models with four units. We used a single layer LSTM...
Our results are directly applicable to infinite-width limit of neural networks that admit a kernel description (including feedforward, convolutional and recurrent neural networks)13,55,56,57,58,59, and explain their inductive bias towards simple functions35,51,60,61,62,63,64. We also note a ...
aEmergence of hierarchical structure mirroring linguistic composition in a recurrent neural network Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network[translate] aIF YOU LIKE COME AND TALK WITH ME 如果您喜欢来并且与我谈话[translate] ...
A recurrent neural network is able to explain variance in human prediction errors whereas the Rescorla-Wagner model performs less well. The Rescorla-Wagner prediction associations do however explain more variance in human reading times. Moreover, the Rescorla-Wagner model associations explain more ...
Finally, the accuracy to predict subjective creakiness via a recurrent neural network classifier was best when the traces of all the considered psychoacoustic features were included as predictors. Introduction Researchers interested in phonation and its contrastive role in different languages often draw ...
Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism Neuron, 70 (2011), pp. 863-885, 10.1016/j.neuron.2011.05.002 View PDFView articleView in ScopusGoogle Scholar Sanders et al., 2015 S.J. Sanders, X. He, A...
Such destabilization can occur when inhibition from other items in WM effectively suppresses the recurrent excitatory activity necessary to maintain stable peaks. Similarly, increased recall variance in the SS3 condition could be due to an increase in peak movement caused by the presence of other ...