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MedMention Mohan and Li (2019) is a publicly available corpus for medical concept normalization. Researchers suggest it is possible that older men have a different concept of masculinity than the younger men in the survey. We use all possible permutations of the Medmention, MedNorm, and 3DNotes datasets to test how well different methods prevent CF. Though we do not include a recurrent neural network (RNN) model in this paper, these methods can be easily extended to such architectures.111 RNNs are constructed of linear layers with non-linear activations. Still, we compare our methods to experience replay to assess the relative performance we can achieve when we maintain only statistics and model weights for prior tasks. With Silagra you can set out on a new journey to take your coital life on next level. In this paper, we assume that each instance consists of a trimmed down set of candidates, and a mention in its context. We use 20 words on the left and right as mention context. They compared pain relief from use of sildenafil vaginally with that of a placebo. In practice, for purposes of computational efficiency, we use only the diagonal of the FIM Kirkpatrick et al. Kronecker Factorization. A downside to using a diagonal FIM is that this assumes independence between all model parameters.

Specifically, we apply Kronecker Factorization in two large-scale neural networks for Entity Linking: (1) a convolutional and (2) a transformer-based architecture Vaswani et al. We perform all experiments using a CNN-based model with 26M parameters and a Transformer-based model with 46M parameters that is initialized using the first three layers of BERT Devlin et al. We perform each tier of training for models using an NVIDIA V100 GPU with 16GB of memory. Ritter et al. (2018b) relax this independence assumption while still maintaining low memory requirements by using a convenient Kronecker Factorization of the FIM for parameters within a layer. Kronecker Factorization is therefore essential to reduce asymptotic memory requirements. 2019) is a method for preventing CF in continuous learning by “replaying” examples from a memory buffer. Learning rate control was proposed in ULMFit Howard and Ruder (2018) as “discriminative fine-tuning” motivated by the intuition that different layers contain distinct information and should be fine-tuned at different rates, accordingly. Here we compare recently proposed baselines, describe an extension of EWC, and demonstrate how to scale this to modern, large NLP models.

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