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Mind the GAP: Glimpse-based Active Perception improves generalization and sample efficiency of visual reasoning
Active vision theories postulate that the learning of visual relations is grounded in actions that we take to fixate objects and their parts by moving our eyes.
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
In this work, we pair L2L with in-memory computing NMHW based on phase-change memory devices to build efficient AI models that can rapidly adapt to new tasks.
Intrinsic Biologically Plausible Adversarial Robustness
Artificial Neural Networks (ANNs) trained with Backpropagation (BP) excel in different daily tasks but have a dangerous vulnerability: inputs with small targeted perturbations, also known as adversarial samples, can drastically disrupt their performance.
Online Spatio-Temporal Learning with Target Projection
Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks.
