The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...

Abstract: The exploration of quantum advantages with Quantum Neural Networks (QNNs) is an exciting endeavor. Recurrent neural networks, the widely used framework in deep learning, suffer from the ...

Abstract: Shifts in data distribution across time can strongly affect early classification of time-series data. When decoding behavior from neural activity, early detection of behavior may help in ...

Autoregressive Transformers have become the leading approach for sequence modeling due to their strong in-context learning and parallelizable training enabled by softmax attention. However, softmax ...

‘Ridiculous prices’ blamed for slump in Las Vegas visitor numbers The Salt Path scandal has killed the middle-class fantasy of escapism Man rescues Texas family from floodwaters: ‘It was pure panic’ ...

Finance and Business Sector, Institute of Public Administration, Riyadh, Saudi Arabia. This paper seeks to forecast the daily closing prices of advanced global stock markets by employing machine ...

1 Cognitive Computational Neuroscience Group, Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany 2 Neuroscience Lab, University Hospital Erlangen, ...

This important study provides a new perspective on why preparatory activity occurs before the onset of movement. The authors report that when there is a cost on the inputs, the optimal inputs should ...

"With BigDL, now we can train the recurrent neural networks (RNNs) more neatly, such as the long short-term memory (LSTM) and the gated recurrent unit (GRU). To demonstrate the end-to-end RNN training ...