Deep Tabular Learning

online deep learning

Introduced by Sahoo et al. in Online Deep Learning: Learning Deep Neural Networks on the Fly

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings.

Source: Online Deep Learning: Learning Deep Neural Networks on the Fly

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Dictionary Learning 2 25.00%
Learning Theory 1 12.50%
Continual Learning 1 12.50%
Image Classification 1 12.50%
Keyword Spotting 1 12.50%
Recommendation Systems 1 12.50%
Image Deconvolution 1 12.50%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories