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Found 5 papers, 1 papers with code
ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals
The core of our approach is the novel detect-then-segment method for raw radar signals.
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape
In this paper, we study the sharpness of a deep learning (DL) loss landscape around local minima in order to reveal systematic mechanisms underlying the generalization abilities of DL models.
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs
It is however unclear how to extend these measures to DNNs and therefore the existing analyses are applicable to simple neural networks, which are not used in practice, e. g., linear or shallow ones or otherwise multi-layer perceptrons.
Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation
Melanoma is one of the ten most common cancers in the US.
VisualBackProp for learning using privileged information with CNNs
In many machine learning applications, from medical diagnostics to autonomous driving, the availability of prior knowledge can be used to improve the predictive performance of learning algorithms and incorporate `physical,' `domain knowledge,' or `common sense' concepts into training of machine learning systems as well as verify constraints/properties of the systems.
