Обучение от начала до конца

method ID: endtoendlearning

Related Papers

Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractEnd-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce …...

40052

Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractA number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the …...

32588

Complex Question Answering on knowledge graphs using machine translation and multi-task learning

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractQuestion answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG. In a real-world industrial setting, this …...

42531

Continual Learning in Task-Oriented Dialogue Systems

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractContinual learning in task-oriented dialogue systems allows the system to add new domains and functionalities overtime after deployment, without incurring the high cost of retraining the whole system each time. …...

31371

Efficient Transformers with Dynamic Token Pooling

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractTransformers achieve unrivalled performance in modelling language, but remain inefficient in terms of memory and time complexity. A possible remedy is to reduce the sequence length in the intermediate layers …...

38516