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Found 6 papers, 0 papers with code
Privacy-Preserving Debiasing using Data Augmentation and Machine Unlearning
Data augmentation is widely used to mitigate data bias in the training dataset.
SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processing
SDIP efficiently utilizes this trait in a reinforcement learning manner, where the current iteration's output is utilized by a steering algorithm to update the network input for the next iteration, guiding the algorithm toward improved results.
Hardware Acceleration of Explainable Artificial Intelligence
Extensive experimental evaluation demonstrates that proposed approach deployed on TPU can provide drastic improvement in interpretation time (39x on average) as well as energy efficiency (69x on average) compared to existing acceleration techniques.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution
In this paper, we propose a novel backdoor attack based on effective learning and targeted utilization of reverse distribution.
Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units
(1) To the best of our knowledge, our proposed work is the first attempt in enabling hardware acceleration of explainable ML using TPUs.
Fast Approximate Spectral Normalization for Robust Deep Neural Networks
One promising strategy to counter adversarial attacks is to utilize spectral normalization, which ensures that the trained model has low sensitivity towards the disturbance of input samples.
