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Found 4 papers, 3 papers with code
Mitigating harm in language models with conditional-likelihood filtration
Language models trained on large-scale unfiltered datasets curated from the open web acquire systemic biases, prejudices, and harmful views from their training data.
Learning Sparse Networks Using Targeted Dropout
Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights.
Targeted Dropout
Neural networks are extremely flexible models due to their large number of parameters, which is beneficial for learning, but also highly redundant.
Unsupervised Cipher Cracking Using Discrete GANs
This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext.
