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DEEP METRIC LEARNING USING TRIPLET NETWORK
The Triplet network model learns useful representations by distance comparisons. -
Representer Theorems for Metric and Preference Learning
The dataset is used to demonstrate the representer theorem for simultaneous metric and preference learning from paired comparisons and metric learning from triplet comparisons. -
COMET: A neural framework for MT evaluation
The COMET dataset contains human-annotated scores for machine translation candidates. -
WMT2020 Metrics Shared Task
The WMT2020 Metrics Shared Task dataset contains human-annotated scores for machine translation candidates. -
RoBLEURT Submission for the WMT2021 Metrics Task
RoBLEURT is a robustly optimizing the training of BLEURT, a trainable metric model for evaluating the semantic consistency between machine translation candidates and golden... -
Discriminative Deep Forest (DisDF)
A Discriminative Deep Forest (DisDF) as a metric learning algorithm is proposed in the paper. -
Automated Vulnerability Detection Framework for Smart Contracts
The proposed framework utilizes metric learning based DNN to make vulnerability detection for smart contracts. -
Memory-Augmented Relation Network for Few-Shot Learning
The proposed method Memory-Augmented Relation Network (MRN) for few-shot learning.
