Results 61 to 70 of about 228,260 (299)

An Adaptive Probailistic Approach to Goal-Level Imitation Learning [PDF]

, 2010
Imitation learning has been recognized as a promising technique to teach robots advanced skills. It is based on the idea that robots could learn new behaviors by observ- ing and imitating the behaviors of other skilled actors.
Dindo, Haris, Schillaci, Guido, Guido Schillaci, Haris Dindo, Schillaci, G.   +4 more
core   +1 more source

The human gut microbiome across the life course

FEBS Letters, EarlyView.
Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero, Basak Bahcivanci, Antonietta Hayhoe, Animesh Acharjee, Ezgi Özkurt   +4 more
wiley   +1 more source

Stimulus representation and the timing of reward-prediction errors in models of the dopamine system [PDF]

, 2008
The phasic firing of dopamine neurons has been theorized to encode a reward-prediction error as formalized by the temporal-difference (TD) algorithm in reinforcement learning.
Sutton, Richard S., Kehoe, E. James, Ludvig, Elliot Andrew   +2 more
core   +1 more source

Graph Tree Networks: a graph representation learning framework

, 2023
Fang, XiaoGraph Neural Networks (GNNs) have been successfully applied in many areas to solve real-world problems. Among various architectures of GNNs, the class of spatial-based convolutional GNNs (Conv-GNNs) has gained particular attention due to its ...
Wu, Nan
core   +1 more source

Network Representation Learning Enhanced Recommendation Algorithm

IEEE Access, 2019
With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional recommendation algorithms.
Qiang Wang, Yonghong Yu, Haiyan Gao, Li Zhang, Yang Cao, Lin Mao, Kaiqi Dou, Wenye Ni   +7 more
doaj   +1 more source

Modulation of Homer1 EVH1 domain internal dynamics by putative autism‐associated mutations

FEBS Letters, EarlyView.
The putative autism‐associated M65I and S97L variants of the EVH1 domain of the postsynaptic scaffold protein Homer1 do not exhibit substantial changes in their overall structure or partner binding. Both of them, but especially the M65I variant, show altered internal dynamics relative to the wild‐type domain on the μs‐ms timescale, indicated by the ...
Fanni Farkas, Brigitta Maruzs, Zsófia E. Kálmán, Tomas Klumpler, Gyula Batta, Bálint Péterfia, Zoltán Gáspári   +6 more
wiley   +1 more source

TriNE: Network Representation Learning for Tripartite Heterogeneous Networks [PDF]

2020 IEEE International Conference on Knowledge Graph (ICKG), 2020
In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entities. We argue that tripartite networks are common in real world applications, and the essential challenge of the representation learning is the heterogeneous relations between
Zhabiz Gharibshah, Xingquan Zhu 0001
openaire   +2 more sources

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Network Alignment by Representation Learning on Structure and Attribute

, 2019
Network alignment is the task of recognizing similar network nodes across different networks, which has many applications in various domains. As traditional network alignment methods based on matrix factorization do not scale to large graphs, a variety ...
Tong, VV, Duong, CT, Huynh Quyet, T, Sattar, A, Thanh Trung, Huynh, Nguyen, QVH   +5 more
core   +1 more source

A Hybrid Approach to Service Recommendation Based on Network Representation Learning

IEEE Access, 2019
Network representation learning has attracted much attention as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network information.
Hao Wu, Hanyu Zhang, Peng He, Cheng Zeng, Yan Zhang   +4 more
doaj   +1 more source