GIRNet: Interleaved Multi-Task Recurrent State Sequence Models
نویسندگان
چکیده
منابع مشابه
Multi-task Sequence to Sequence Learning
Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three settings to multi-task sequence to sequence learning: (a) the one-to-many setting – where the encoder is shared between several tasks such as machine transla...
متن کاملProbabilistic Recurrent State-Space Models
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g., LSTMs) proved extremely successful in modeling complex timeseries data. Fully probabilistic SSMs, however, unfortunately often prove hard to train, even for smaller problems. To overcome this limitation, we propose a scalabl...
متن کاملMulti-task Domain Adaptation for Sequence Tagging
Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for mul...
متن کاملMulti-task Multi-domain Representation Learning for Sequence Tagging
Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for mul...
متن کاملA Multi - Task Theory of the State
During transition, maintaining employment and providing a social safety net to the unemployed are important to social stability, which in turn is crucial for the productivity of the whole economy. Because independent institutions for social safety are lacking and firms with strong profit incentives have little incentives to promote social stability due to its public good nature, state-owned ent...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33016497