Results 61 to 70 of about 7,883,935 (303)
Learning discriminative region representation for person retrieval
, 2022 Region-level representation learning plays a key role in providing discriminative information for person retrieval. Current methods rely on heuristically coarse-grained region strips or directly borrow pixel-level annotations from pretrained human ...
Yu, X. +7 more
core +1 more source
Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection
Applied Sciences, 2023 Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of ...
Dawei Luo +3 more
doaj +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Learning Equivariant Representations
CoRR, 2020 State-of-the-art deep learning systems often require large amounts of data and computation. For this reason, leveraging known or unknown structure of the data is paramount. Convolutional neural networks (CNNs) are successful examples of this principle, their defining characteristic being the shift-equivariance.
openaire +2 more sources
Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
Time representation in reinforcement learning models of the basal ganglia [PDF]
, 2013 Reinforcement learning (RL) models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection.
Sebastian eDupraz +29 more
core +1 more source
Gaussian Embedding of Temporal Networks
IEEE Access, 2023 Representing the nodes of continuous-time temporal graphs in a low-dimensional latent space has wide-ranging applications, from prediction to visualization.
Raphael Romero +4 more
doaj +1 more source
Meta-learning of Textual Representations [PDF]
, 2020 Recent progress in AutoML has lead to state-of-the-art methods (e.g., AutoSKLearn) that can be readily used by non-experts to approach any supervised learning problem. Whereas these methods are quite effective, they are still limited in the sense that they work for tabular (matrix formatted) data only. This paper describes one step forward in trying to
Jorge G. Madrid +2 more
openaire +3 more sources
Learning Invariant Representation for Continual Learning
CoRR, 2021 Accepted at the AAAI Meta-Learning for Computer Vision Workshop (2021)
Sokar, Ghada A.Z.N. +2 more
openaire +4 more sources
Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source