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On Inherent Adversarial Robustness of Active Vision Systems
Current Deep Neural Networks are vulnerable to adversarial examples, which alter their predictions by adding carefully crafted noise.
Towards Two-Stream Foveation-based Active Vision Learning
Specifically, the proposed framework models the following mechanisms: 1) ventral (what) stream focusing on the input regions perceived by the fovea part of an eye (foveation), 2) dorsal (where) stream providing visual guidance, and 3) iterative processing of the two streams to calibrate visual focus and process the sequence of focused image patches.
Pruning for Improved ADC Efficiency in Crossbar-based Analog In-memory Accelerators
sparsity is then achieved by regularizing the variance of $L_{0}$ norms of neighboring columns within the same crossbar.
Towards Image Semantics and Syntax Sequence Learning
To mitigate this gap, we introduce the concept of "image grammar", consisting of "image semantics" and "image syntax", to denote the semantics of parts or patches of an image and the order in which these parts are arranged to create a meaningful object.
On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks
The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies.
On-chip Face Recognition System Design with Memristive Hierarchical Temporal Memory
Hierarchical Temporal Memory is a new machine learning algorithm intended to mimic the working principle of neocortex, part of the human brain, which is responsible for learning, classification, and making predictions.
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