Cognitive Computing Safety: The New Horizon for Reliability / The Design and Evolution of Deep Learning Workloads

10 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “To the best of our knowledge, there is no self-driving car system with an inherently safe design that utilizes, e.g., interpretable models [47].”
MnnFast: A Fast and Scalable System Architecture for Memory-Augmented Neural Networks (2019, background): “The excessive approximation can threaten our safety [89], and also degrade performance and energy if subsequent tasks are inefficient on inaccurate data [73, 86, 87].”