Results 11 to 20 of about 19,993,772 (359)
Learning to Compare: Relation Network for Few-Shot Learning [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each.
Flood Sung +5 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
Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning [PDF]
Sensors, 2021 Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning.
Jiang Hua +3 more
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Meta Learning via Learned Loss [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 Project website with code and video at https://sites.google.com/view ...
Bechtle, Sarah +6 more
openaire +3 more sources
Deep Learning with Differential Privacy [PDF]
Conference on Computer and Communications Security, 2016 Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information.
Martín Abadi +6 more
semanticscholar +1 more source
node2vec: Scalable Feature Learning for Networks [PDF]
Knowledge Discovery and Data Mining, 2016 Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by ...
Aditya Grover, J. Leskovec
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
A Comprehensive Survey on Transfer Learning [PDF]
Proceedings of the IEEE, 2019 Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains.
Fuzhen Zhuang +7 more
semanticscholar +1 more source
Object Detection With Deep Learning: A Review [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures.
Zhong-Qiu Zhao +3 more
semanticscholar +1 more source
Cognitive Science, 2023 AbstractHumans can learn complex functional relationships between variables from small amounts of data. In doing so, they draw on prior expectations about the form of these relationships. In three experiments, we show that people learn to adjust these expectations through experience, learning about the likely forms of the functions they will encounter.
Michael Y, Li +4 more
openaire +2 more sources