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Found 2 papers, 1 papers with code
Categorical Representation Learning and RG flow operators for algorithmic classifiers
In particular we apply the RG categorifier to particular genomic sequences of flu viruses and show how our technology is capable of extracting the information from given genomic sequences, find their hidden symmetries and dominant features, classify them and use the trained data to make stochastic prediction of new plausible generated sequences associated with new set of viruses which could avoid the human immune system.
T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification
T-Miner employs a sequence-to-sequence (seq-2-seq) generative model that probes the suspicious classifier and learns to produce text sequences that are likely to contain the Trojan trigger.
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