Results 91 to 100 of about 3,974,691 (210)
Homeostatic plasticity improves signal propagation in continuous time recurrent neural networks
, 2007 Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node saturation effects in these networks can leave them insensitive to input and
Williams, Hywel, Noble, Jason
core +1 more source
Recurrent Neural Networks for Temporal Data Processing [PDF]
, 2011 The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory.
core +1 more source
Grounded Recurrent Neural Networks
CoRR, 2017 In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process "grounding"). The approach is particularly well-suited for extracting large numbers of concepts from text.
Ankit Vani +2 more
openaire +2 more sources
Segmental Recurrent Neural Networks
, 2015 We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments. Representations of the input segments (i.e., contiguous subsequences of the input) are computed by encoding their constituent tokens using bidirectional recurrent
Lingpeng Kong, Chris Dyer, Noah A. Smith
openaire +2 more sources
Temporal overdrive recurrent neural network [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics. The proposed network is composed by several recurrent groups of neurons that are trained to separately adapt to each timescale, in order to improve the system identification process.
Filippo Maria Bianchi +3 more
openaire +5 more sources
Adaptive spatiotemporal neural networks through complementary hybridization
Nature CommunicationsProcessing spatiotemporal data sources with both high spatial dimension and rich temporal information is a ubiquitous need in machine intelligence. Recurrent neural networks in the machine learning domain and bio-inspired spiking neural networks in the ...
Yujie Wu +7 more
doaj +1 more source
JuTISI (Jurnal Teknik Informatika dan Sistem Informasi), 2020 Nowadays internet access is getting easier to get. Because of the ease of access to the internet, almost all internet users have social media. Social media is widely used by users to call out their opinions or even to make complaints about a matter and ...
Ivan Nathaniel Husada, Hapnes Toba
doaj +1 more source
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 ...
Ilya Sutskever, Geoffrey E. Hinton
openaire +3 more sources
Real-time classification of hand movements as a basis for intuitive control of grasp neuroprostheses
Current Directions in Biomedical Engineering, 2020 This paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury.
Amelin Dmitry +6 more
doaj +1 more source
PNNARMA model: an alternative to phenomenological models in chemical reactors [PDF]
, 2001 This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose.
J.M. Zaldı́var +5 more
core +1 more source