| Nome |
# |
|
Deep Reservoir Computing: A Critical Experimental Analysis, file e0d6c92c-a523-fcf8-e053-d805fe0aa794
|
2.290
|
|
Echo State Property of Deep Reservoir Computing Networks, file e0d6c92c-7ad6-fcf8-e053-d805fe0aa794
|
1.653
|
|
Design of deep echo state networks, file e0d6c92c-7cb6-fcf8-e053-d805fe0aa794
|
1.077
|
|
Human activity recognition using multisensor data fusion based on Reservoir Computing, file e0d6c92c-8d78-fcf8-e053-d805fe0aa794
|
913
|
|
An experimental characterization of reservoir computing in ambient assisted living applications, file e0d6c92c-b082-fcf8-e053-d805fe0aa794
|
653
|
|
A learning system for automatic Berg Balance Scale score estimation, file e0d6c92c-5e24-fcf8-e053-d805fe0aa794
|
604
|
|
Local Lyapunov exponents of deep echo state networks, file e0d6c92c-7cba-fcf8-e053-d805fe0aa794
|
448
|
|
Robotic Ubiquitous Cognitive Ecology for Smart Homes, file e0d6c92c-be17-fcf8-e053-d805fe0aa794
|
381
|
|
Tree Echo State Networks, file e0d6c92c-5c55-fcf8-e053-d805fe0aa794
|
360
|
|
Generative Kernels for Tree-Structured Data, file e0d6c92c-e546-fcf8-e053-d805fe0aa794
|
355
|
|
Prediction of the Italian electricity price for smart grid applications, file e0d6c92c-68f2-fcf8-e053-d805fe0aa794
|
302
|
|
Embeddings and representation learning for structured data, file e0d6c92d-cecb-fcf8-e053-d805fe0aa794
|
291
|
|
An ambient intelligence approach for learning in smart robotic environments, file e0d6c930-72fe-fcf8-e053-d805fe0aa794
|
235
|
|
Compositional generative mapping for tree-structured data - Part II: Topographic projection model, file e0d6c92c-e541-fcf8-e053-d805fe0aa794
|
227
|
|
A cognitive robotic ecology approach to self-configuring and evolving AAL systems, file e0d6c92c-8bb2-fcf8-e053-d805fe0aa794
|
184
|
|
Localizing Tortoise Nests by Neural Networks, file e0d6c928-5b7f-fcf8-e053-d805fe0aa794
|
180
|
|
Deep Reservoir Neural Networks for Trees, file e0d6c92c-7375-fcf8-e053-d805fe0aa794
|
168
|
|
A Preliminary Application of Echo State Networks to Emotion Recognition, file e0d6c927-17a2-fcf8-e053-d805fe0aa794
|
155
|
|
Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems, file e0d6c926-4482-fcf8-e053-d805fe0aa794
|
148
|
|
A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor, file e0d6c92b-3717-fcf8-e053-d805fe0aa794
|
111
|
|
Preliminary experimental analysis of Reservoir Computing approach for balance assessment, file e0d6c926-fef4-fcf8-e053-d805fe0aa794
|
77
|
|
Reservoir Topology in Deep Echo State Networks, file e0d6c92d-cd1a-fcf8-e053-d805fe0aa794
|
57
|
|
Smart environments and context-awareness for lifestyle management in a healthy active ageing framework, file e0d6c92c-1366-fcf8-e053-d805fe0aa794
|
48
|
|
Reliability and human factors in Ambient Assisted Living environments, file e0d6c92b-f954-fcf8-e053-d805fe0aa794
|
20
|
|
Modeling of the acute toxicity of benzene derivatives by complementary QSAR methods, file e0d6c926-2236-fcf8-e053-d805fe0aa794
|
18
|
|
In silico modeling of biochemical pathways, file e0d6c932-32ff-fcf8-e053-d805fe0aa794
|
11
|
|
Tree edit distance learning via adaptive symbol embeddings, file e0d6c930-bd0c-fcf8-e053-d805fe0aa794
|
9
|
|
Continuous Blood Pressure Estimation Through Optimized Echo State Networks, file e0d6c92d-cd24-fcf8-e053-d805fe0aa794
|
7
|
|
A Preliminary Investigation of Machine Learning Approaches for Mobility Monitoring from Smartphone Data, file e0d6c92f-6012-fcf8-e053-d805fe0aa794
|
7
|
|
Wild animals' biologging through machine learning models, file e0d6c92b-d86c-fcf8-e053-d805fe0aa794
|
5
|
|
Ring Reservoir Neural Networks for Graphs, file e0d6c92f-a7b1-fcf8-e053-d805fe0aa794
|
5
|
|
Addressing heterophily in node classification with graph echo state networks, file ded15a9b-57c0-4227-b2ce-34b40073c86b
|
4
|
|
Deep Reservoir Computing, file e0d6c931-afb8-fcf8-e053-d805fe0aa794
|
4
|
|
On the effectiveness of Gated Echo State Networks for data exhibiting long-term dependencies, file e0d6c931-b543-fcf8-e053-d805fe0aa794
|
4
|
|
Prediction of Chemical-Physical Properties by Neural Networks for Structures, file e0d6c926-1235-fcf8-e053-d805fe0aa794
|
3
|
|
null, file e0d6c92c-f788-fcf8-e053-d805fe0aa794
|
3
|
|
Prediction of the Italian electricity price for smart grid applications, file e0d6c930-9cfc-fcf8-e053-d805fe0aa794
|
3
|
|
Phase Transition Adaptation, file e0d6c931-de1a-fcf8-e053-d805fe0aa794
|
3
|
|
Recursive Self-organizing Network Models, file e0d6c925-f1e4-fcf8-e053-d805fe0aa794
|
2
|
|
null, file e0d6c92c-1369-fcf8-e053-d805fe0aa794
|
2
|
|
Pyramidal Reservoir Graph Neural Network, file e0d6c932-1de4-fcf8-e053-d805fe0aa794
|
2
|
|
Hierarchical Dynamics in Deep Echo State Networks, file b1525f34-a196-41ab-8e95-eaaac889391d
|
1
|
|
Predicting Physical-Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks, file e0d6c926-17f4-fcf8-e053-d805fe0aa794
|
1
|
|
On the Need of Machine Learning as a Service for the Internet of Things, file e0d6c929-ea50-fcf8-e053-d805fe0aa794
|
1
|
|
Contextual graph markov model: A deep and generative approach to graph processing, file e0d6c930-76f1-fcf8-e053-d805fe0aa794
|
1
|
| Totale |
11.033 |