Results 71 to 80 of about 792,416 (284)
Is the Bellman residual a bad proxy? [PDF]
, 2017 This paper aims at theoretically and empirically comparing two standard optimization criteria for Reinforcement Learning: i) maximization of the mean value and ii) minimization of the Bellman residual.
Geist, Matthieu +2 more
core +1 more source
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
, 2016 In this work, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception.
Feng, Jiashi +6 more
core +1 more source
Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source
Deep Limits of Residual Neural Networks
, 2019 Neural networks have been very successful in many applications; we often, however, lack a theoretical understanding of what the neural networks are actually learning. This problem emerges when trying to generalise to new data sets.
Thorpe, Matthew, van Gennip, Yves
core +1 more source
Molecular Oncology, EarlyView.Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra +10 more
wiley +1 more source
Residual Learning Capability in Organic Amnesia
Cortex, 1982 This article provides a review of residual learning capability in patients suffering from the organic amnesic syndrome. It is shown that organic amnesics are able to learn a considerable number of laboratory tasks, many to a level comparable with normals.
openaire +2 more sources
CREST: Convolutional Residual Learning for Visual Tracking
, 2017 Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately
Gong, Lijun +5 more
core +1 more source
Dual targeting of RET and SRC synergizes in RET fusion‐positive cancer cells
Molecular Oncology, EarlyView.Despite the strong activity of selective RET tyrosine kinase inhibitors (TKIs), resistance of RET fusion‐positive (RET+) lung cancer and thyroid cancer frequently occurs and is mainly driven by RET‐independent bypass mechanisms. Son et al. show that SRC TKIs significantly inhibit PAK and AKT survival signaling and enhance the efficacy of RET TKIs in ...
Juhyeon Son +13 more
wiley +1 more source
Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks
Journal of Imaging, 2020 Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but ...
Michalis Giannopoulos +4 more
doaj +1 more source
Residual Attention Network for Image Classification
, 2017 In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is
Jiang, Mengqing +7 more
core +1 more source