Results 81 to 90 of about 827,210 (276)
, 2021 Deep neural networks have had tremendous success in a wide range of applications where they achieve state of the art performance. Their success can be generally attributed to three main pillars: their natural back-propagation structure which allows time and resources efficient gradient computation; recent advances in optimization theory which have led ...
openaire +2 more sources
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 +4 more
wiley +1 more source
Two-Stage Approach to Image Classification by Deep Neural Networks
EPJ Web of Conferences, 2018 The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying.
Ososkov Gennady, Goncharov Pavel
doaj +1 more source
Spiking Neural Networks and Their Applications: A Review
Brain Sciences, 2022 The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs.
Kashu Yamazaki +3 more
doaj +1 more source
Potential therapeutic targeting of BKCa channels in glioblastoma treatment
Molecular Oncology, EarlyView.This review summarizes current insights into the role of BKCa and mitoBKCa channels in glioblastoma biology, their potential classification as oncochannels, and the emerging pharmacological strategies targeting these channels, emphasizing the translational challenges in developing BKCa‐directed therapies for glioblastoma treatment.
Kamila Maliszewska‐Olejniczak +4 more
wiley +1 more source
Algorithms, 2019 Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets.
Qingge Ji +3 more
doaj +1 more source
Learning to Balance Local Losses via Meta-Learning
IEEE Access, 2021 The standard training for deep neural networks relies on a global and fixed loss function. For more effective training, dynamic loss functions have been recently proposed.
Seungdong Yoa +3 more
doaj +1 more source
Interleaver design for deep neural networks [PDF]
2017 51st Asilomar Conference on Signals, Systems, and Computers, 2017 We propose a class of interleavers for a novel deep neural network (DNN) architecture that uses algorithmically pre-determined, structured sparsity to significantly lower memory and computational requirements, and speed up training. The interleavers guarantee clash-free memory accesses to eliminate idle operational cycles, optimize spread and ...
Sourya Dey +2 more
openaire +2 more sources
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
Algorithms, 2017 The literature [-5]contains several reports evaluating the abilities of deep neural networks in text transfer learning. To our knowledge, however, there have been few efforts to fully realize the potential of deep neural networks in cross-domain product ...
Xiaocong Wei +3 more
doaj +1 more source