Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Modern machine learning suffers from catastrophic forgetting when learning new classes
incrementally. The performance dramatically degrades due to the missing data of old …

On-chip spectrometers with compact footprints are being extensively investigated owing to
their promising future in critical applications such as sensing, surveillance and spectral …

Humans and animals have the ability to continually acquire, fine-tune, and transfer
knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is …

Trojaning attack on neural networks Page 1 Please do not remove this page Trojaning
attack on neural networks Liu, Yingqi; Ma, Shiqing; Aafer, Yousra; et.al. https://scholarship.libraries.rutgers.edu/esploro/outputs/conferencePaper/Trojaning-attack-on-neural-networks/991031794682704646/filesAndLinks …

Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

A major open problem on the road to artificial intelligence is the development of
incrementally learning systems that learn about more and more concepts over time from a …

Deep neural networks are used in many state-of-the-art systems for machine perception.
Once a network is trained to do a specific task, eg, bird classification, it cannot easily be …

In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …