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Papers with Code 아카이브
Open-Ended Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.
기법
관련 논문에서 소개한 기법입니다. 이 태스크에서의 평가 여부는 평가 결과에서 확인하세요.
Local Augmentation
Local Augmentation for Graph Neural Networks, or LA-GNN, is a data augmentation technique that enhances node features by its local subgraph structures. Specifically, it learns the conditional distribution of the connected neighbors’ representations given the representation of the central node, which has an analogy with the Skip-gram of word2vec model that predicts the probability of the context given the central word. After augmenting the neighborhood, we concat the initial and the generated feature matrix as input for GNNs.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.