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Found 7 papers, 1 papers with code
Towards Realistic Single-Task Continuous Learning Research for NER
There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications.
Paraphrase Generation for Semi-Supervised Learning in NLU
Semi-supervised learning is an efficient way to improve performance for natural language processing systems.
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity
In addition, methods optimizing diversity can reduce training data in many cases to 50% with little impact on performance.
Natural Language Understanding
Task-Oriented Dialogue Systems
Industry Scale Semi-Supervised Learning for Natural Language Understanding
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks.
Measuring and Mitigating Local Instability in Deep Neural Networks
We formulate the model stability problem by studying how the predictions of a model change, even when it is retrained on the same data, as a consequence of stochasticity in the training process.
Sketch and Text Guided Diffusion Model for Colored Point Cloud Generation
In this paper, we propose a sketch and text guided probabilistic diffusion model for colored point cloud generation that conditions the denoising process jointly with a hand drawn sketch of the object and its textual description.
External Knowledge Enhanced 3D Scene Generation from Sketch
Generating realistic 3D scenes is challenging due to the complexity of room layouts and object geometries. We propose a sketch based knowledge enhanced diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes.
