Results 61 to 70 of about 1,489,144 (280)
Multi-task Deep Reinforcement Learning with PopArt
, 2018 The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent ...
Czarnecki, Wojciech +5 more
core +1 more source
Heterogeneous Multi-Task Learning With Expert Diversity
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022 Predicting multiple heterogeneous biological and medical targets is a challenge for traditional deep learning models. In contrast to single-task learning, in which a separate model is trained for each target, multi-task learning (MTL) optimizes a single model to predict multiple related targets simultaneously.
Raquel Aoki +2 more
openaire +3 more sources
Overview of molecular signatures of senescence and associated resources: pros and cons
FEBS Open Bio, EarlyView.Cells can enter a stress response state termed cellular senescence that is involved in various diseases and aging. Detecting these cells is challenging due to the lack of universal biomarkers. This review presents the current state of senescence identification, from biomarkers to molecular signatures, compares tools and approaches, and highlights ...
Orestis A. Ntintas +6 more
wiley +1 more source
Adversarial Multi-task Learning for Text Classification
, 2017 Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features.
Huang, Xuanjing +2 more
core +1 more source
Multi-task learning for pKa prediction [PDF]
Journal of Computer-Aided Molecular Design, 2012 Journal of Computer-Aided Molecular Design, 26 (7)
Skolidis, Grigorios +3 more
openaire +4 more sources
Curriculum-Based Asymmetric Multi-Task Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Accepted by TPAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence)
Hanchi Huang +3 more
openaire +3 more sources
Enzymatic degradation of biopolymers in amorphous and molten states: mechanisms and applications
FEBS Open Bio, EarlyView.This review explains how polymer morphology and thermal state shape enzymatic degradation pathways, comparing amorphous and molten biopolymer structures. By integrating structure–reactivity principles with insights from thermodynamics and enzyme engineering, it highlights mechanisms that enable efficient polymer breakdown.
Anđela Pustak, Aleksandra Maršavelski
wiley +1 more source
Open‐set recognition of compound jamming signal based on multi‐task multi‐label learning
IET Radar, Sonar & NavigationIn the increasingly intricate electromagnetic environment, the radar receiver may simultaneously encounter multiple intentional or unintentional jamming signals, which results in temporal and spectral overlap of received signals and forms a composite ...
Yihan Xiao +3 more
doaj +1 more source
Learning weakly supervised multimodal phoneme embeddings
, 2017 Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate the role of combining different speech modalities, i.e.
Chaabouni, Rahma +3 more
core +3 more sources
FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source