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Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

**SC-GPT** is a multi-layer [Transformer](http://paperswithcode.com/method/transformer) neural language model, trained in three steps: (i) Pre-trained on plain text, similar to [GPT-2](http://paperswithcode.com/method/gpt-2); (ii) Continuously pretrained on large amounts of dialog-act labeled utterances corpora to acquire the ability of controllable generation; (iii) Fine-tuned for a target domain using very limited amounts of domain labels. Unlike [GPT-2](http://paperswithcode.com/method/gpt-2), SC-GPT generates semantically controlled responses that are conditioned on the given semantic form, similar to SC-[LSTM](https://paperswithcode.com/method/lstm) but requiring much less domain labels to generalize to new domains. It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains.

Source	Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com