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Trojan Signatures in DNN Weights
Deep neural networks have been shown to be vulnerable to backdoor, or trojan, attacks where an adversary has embedded a trigger in the network at training time such that the model correctly classifies all standard inputs, but generates a targeted, incorrect classification on any input which contains the trigger.
Adaptive Sampling for Minimax Fair Classification
Machine learning models trained on uncurated datasets can often end up adversely affecting inputs belonging to underrepresented groups.
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