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Found 3 papers, 2 papers with code
Amortized Network Intervention to Steer the Excitatory Point Processes
Excitatory point processes (i. e., event flows) occurring over dynamic graphs (i. e., evolving topologies) provide a fine-grained model to capture how discrete events may spread over time and space.
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision.
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Word Sense Disambiguation
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Denoising Multi-Source Weak Supervision for Neural Text Classification
We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources.
