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Found 5 papers, 1 papers with code
Learning To Learn by Jointly Optimizing Neural Architecture and Weights
Existing NAS-based meta-learning methods apply a two-stage strategy, i. e., first searching architectures and then re-training meta-weights on the searched architecture.
Differentiable Graph Optimization for Neural Architecture Search
To this end, we learn a differentiable graph neural network as a surrogate model to rank candidate architectures, which enable us to obtain gradient w. r. t the input architectures.
Connection-Adaptive Meta-Learning
In this paper, we aim to obtain better meta-learners by co-optimizing the architecture and meta-weights simultaneously.
A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis
In this paper, to make multi-task learning feasible for incremental learning, we proposed Category Name Embedding network (CNE-net).
Charge-Based Prison Term Prediction with Deep Gating Network
Judgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction.
