Results 11 to 20 of about 312,321 (283)

Intelligent algorithms of construction of public transport routes [PDF]

E3S Web of Conferences, 2023
Today, in public transport planning systems, it is relevant to a search for a possible route with a minimum time. The aim of the work is the development of intelligent algorithms for constructing public transport routes, the development of programs, and ...
Ismailov Mirxalil, Ziyadullaev Davron, Muhamediyeva Dilnoz, Gazieva Rano, Dzholdasbaeva Aksulu, Aynaqulov Sharofiddin   +5 more
doaj   +1 more source

A new approach to seasonal energy consumption forecasting using temporal convolutional networks

Results in Engineering, 2023
There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature.
Abdul Khalique Shaikh, Amril Nazir, Nadia Khalique, Abdul Salam Shah, Naresh Adhikari   +4 more
doaj   +1 more source

Recurrent Neural Network Grammars [PDF]

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure. We explain efficient inference procedures that allow application to both parsing and language modeling. Experiments show that they provide better parsing in English than any single previously published supervised generative model and better
Dyer, Chris, Kuncoro, Adhiguna, Ballesteros, Miguel, Smith, Noah A.   +3 more
openaire   +3 more sources

Temporal-Kernel Recurrent Neural Networks [PDF]

Neural Networks, 2010
A Recurrent Neural Network (RNN) is a powerful connectionist model that can be applied to many challenging sequential problems, including problems that naturally arise in language and speech. However, RNNs are extremely hard to train on problems that have long-term dependencies, where it is necessary to remember events for many timesteps before using ...
Sutskever, Ilya, Hinton, Geoffrey
openaire   +2 more sources

An attractor-based complexity measurement for Boolean recurrent neural networks. [PDF]

PLoS ONE, 2014
We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics.
Jérémie Cabessa, Alessandro E P Villa
doaj   +1 more source

Reinforcement Learning for Central Pattern Generation in Dynamical Recurrent Neural Networks

Frontiers in Computational Neuroscience, 2022
Lifetime learning, or the change (or acquisition) of behaviors during a lifetime, based on experience, is a hallmark of living organisms. Multiple mechanisms may be involved, but biological neural circuits have repeatedly demonstrated a vital role in the
Jason A. Yoder, Cooper B. Anderson, Cehong Wang, Eduardo J. Izquierdo   +3 more
doaj   +1 more source

Stability and Synchronization of Switched Multi-Rate Recurrent Neural Networks

IEEE Access, 2021
Several designs of recurrent neural networks have been proposed in the literature involving different clock times. However, the stability and synchronization of this kind of system have not been studied.
Victoria Ruiz, Ernesto Aranda-Escolastico, Julian Salt, Sebastian Dormido   +3 more
doaj   +1 more source

A Deep Multi-Task Learning Approach for Bioelectrical Signal Analysis

Mathematics, 2023
Deep learning is a promising technique for bioelectrical signal analysis, as it can automatically discover hidden features from raw data without substantial domain knowledge.
Jishu K. Medhi, Pusheng Ren, Mengsha Hu, Xuhui Chen   +3 more
doaj   +1 more source

Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks

Sensors, 2021
Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently.
Ke Zang, Wenqi Wu, Wei Luo
doaj   +1 more source

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control [PDF]

, 2012
It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for ...
A Banerjee, A Litwin-Kumar, C Miall, C van Vreeswijk, CA Skarda, CV Buhusi, D Bueti, D Durstewitz, D Durstewitz, D Sussillo, DA Crowe, Dean V Buonomano, DJ Watts, DV Buonomano, DV Buonomano, DV Buonomano, DV Buonomano, DV Buonomano, E Pastalkova, EM Izhikevich, G Boffetta, G Fagiolo, H Jaeger, H Jaeger, H Kantz, H Merchant, H Sompolinsky, J Coull, JF Medina, JF Medina, JJ Hopfield, JK Liu, JX Li, K Rajan, M London, M Monteforte, M Rabinovich, MA Long, MB Ahrens, MD Mauk, MM Churchland, MS Goldman, MS Matell, N Brunel, P Janssen, P Simen, RB Ivry, RB Ivry, RHR Hahnloser, RM Church, Rodrigo Laje, S Ganguli, S Song, XJ Wang   +53 more
core   +2 more sources