Results 51 to 60 of about 1,489,144 (280)
Attentive Multi-task Deep Reinforcement Learning [PDF]
, 2020 Accepted as conference paper at ECML PKDD ...
Timo Bräm +3 more
openaire +2 more sources
Improving Evidential Deep Learning via Multi-Task Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 The Evidential regression network (ENet) estimates a continuous target and its predictive uncertainty without costly Bayesian model averaging. However, it is possible that the target is inaccurately predicted due to the gradient shrinkage problem of the original loss function of the ENet, the negative log marginal likelihood (NLL) loss.
Oh, Dongpin, Shin, Bonggun
openaire +2 more sources
Next‐generation proteomics improves lung cancer risk prediction
Molecular Oncology, EarlyView.This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj +4 more
wiley +1 more source
Uniform Loss Versus Specialized Optimization: A Comparative Analysis in Multi-Task Learning
IEEE AccessSpecialized Multi-Task Optimizers (SMTOs) balance task learning in Multi-Task Learning by addressing issues like conflicting gradients and differing gradient norms, which hinder equal-weighted task training. However, recent critiques suggest that equally
Gabriel S. Gama, Valdir Grassi
doaj +1 more source
Seeing Pedestrian in the Dark via Multi-Task Feature Fusing-Sharing Learning for Imaging Sensors
Sensors, 2020 Pedestrian detection is an essential problem of computer vision, which has achieved tremendous success under controllable conditions using visible light imaging sensors in recent years.
Yuanzhi Wang +3 more
doaj +1 more source
A Fused Elastic Net Logistic Regression Model for Multi-Task Binary Classification [PDF]
, 2013 Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations. We present the fused logistic regression, a sparse multi-task learning approach for binary classification. Specifically,
Claassen, Manfred, Mitov, Venelin
core
Networked Federated Multi-Task Learning [PDF]
, 2021 we characterize the network structure of data such that federated mulit-task learning is possible. <br>
Alexander Jung +3 more
openaire +1 more source
Molecular Oncology, EarlyView.Etoposide induces DNA damage, activating p53‐dependent apoptosis via caspase‐3/7, which cleaves PARP1. Dammarenediol II enhances this apoptotic pathway by suppressing O‐GlcNAc transferase activity, further decreasing O‐GlcNAcylation. The reduction in O‐GlcNAc levels boosts p53‐driven apoptosis and influences the Akt/GSK3β/mTOR signaling pathway ...
Jaehoon Lee +8 more
wiley +1 more source
Multitask Classification Algorithm of ECG Signals Based on Radient Magnitude Direction Adjustment [PDF]
Jisuanji kexueCardiovascular diseases are posing more and more serious threats to human health and safety.ECG signals can be used to diagnose and classify related diseases.Most existing ECG classification algorithms adopt single-task learning model,which can not make ...
ZHANG Xue, TIAN Lan, ZENG Ming, LIU Junhui, ZONG Shaoguo
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Scientific Reports, 2022 Pell and Gregory, and Winter’s classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction.
Shintaro Sukegawa +10 more
doaj +1 more source