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Auto Learning Attention
BentengMa, JingZhang, YongXia....
Published date-12/01/2020
ImageClassification, KeypointDetection, ObjectDetection
Attention modules have been demonstrated effective in strengthening the representation ability of a neural network via reweighting spatial or channel features or stacking both operations sequentially. However, designing the structures …
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
JiaxingWang, HaoliBai, JiaxiangWu....
Published date-12/01/2020
NeuralArchitectureSearch
Recent advances in neural architecture search inspire many channel number search algorithms~(CNS) for convolutional neural networks. To improve searching efficiency, parameter sharing is widely applied, which reuses parameters among different …
Weak Form Generalized Hamiltonian Learning
KevinCourse, TreforEvans, PrasanthNair....
Published date-12/01/2020
TimeSeries
We present a method for learning generalized Hamiltonian decompositions of ordinary differential equations given a set of noisy time series measurements. Our method simultaneously learns a continuous time model and …
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield
JohnChoi, KrishanKumar, MohammadKhazali....
Published date-12/01/2020
Neural-Matrix style, high-density electrode arrays for brain-machine interfaces (BMIs) and neuroscientific research require the use of multiplexing: Each recording channel can be routed to one of several electrode sites on …
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces
AkashSaha, BalamuruganPalaniappan....
Published date-12/01/2020
Operator-valued kernels have shown promise in supervised learning problems with functional inputs and functional outputs. The crucial (and possibly restrictive) assumption of positive definiteness of operator-valued kernels has been instrumental …
Look-ahead Meta Learning for Continual Learning
GunshiGupta, KarmeshYadav, LiamPaull....
Published date-12/01/2020
ContinualLearning, Meta-Learning
The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks. While meta-learning shows great potential for …