Results 11 to 20 of about 16,800,197 (383)
Learning sites for health system governance in Kenya and South Africa: reflecting on our experience
Health Research Policy and Systems, 2020 Background Health system governance is widely recognised as critical to well-performing health systems in low- and middle-income countries. However, in 2008, the Alliance for Health Policy and Systems Research identified governance as a neglected health ...
The RESYST/DIAHLS learning site team
doaj +1 more source
A Survey on Bias and Fairness in Machine Learning [PDF]
ACM Computing Surveys, 2019 With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems.
Ninareh Mehrabi +4 more
semanticscholar +1 more source
Federated Learning: Challenges, Methods, and Future Directions [PDF]
IEEE Signal Processing Magazine, 2019 Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized.
Tian Li +3 more
semanticscholar +1 more source
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising [PDF]
IEEE Transactions on Image Processing, 2016 The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
K. Zhang +4 more
semanticscholar +1 more source
Learning to learn differently [PDF]
Journal of Workplace Learning, 2018 Purpose This paper aims to investigate whether the formal and informal learning patterns of community health-care nurses changed in the wake of a reform that altered their work by introducing new patient groups, and to explore whether conditions in the new workplaces facilitated or impeded shifts in learning patterns.
Olsen, Trude Høgvold +2 more
openaire +4 more sources
Learning without Forgetting [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available.
Zhizhong Li, Derek Hoiem
semanticscholar +1 more source
Exploring Simple Siamese Representation Learning [PDF]
Computer Vision and Pattern Recognition, 2020 Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing ...
Xinlei Chen, Kaiming He
semanticscholar +1 more source
Context Encoders: Feature Learning by Inpainting [PDF]
Computer Vision and Pattern Recognition, 2016 We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders - a convolutional neural network trained to generate the contents of an arbitrary image ...
Deepak Pathak +4 more
semanticscholar +1 more source
, 2021 We consider reservoirs in the form of liquid state machines, i.e., recurrently connected networks of spiking neurons with randomly chosen weights. So far only the weights of a linear readout were adapted for a specific task. We wondered whether the performance of liquid state machines can be improved if the recurrent weights are chosen with a purpose ...
Subramoney, Anand +2 more
openaire +4 more sources
Xception: Deep Learning with Depthwise Separable Convolutions [PDF]
Computer Vision and Pattern Recognition, 2016 We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution)
François Chollet
semanticscholar +1 more source