Search Results for author:
Found 7 papers, 2 papers with code
Multi-Level Contrastive Learning for Few-Shot Problems
Most current applications of contrastive learning benefit only a single representation from the last layer of an encoder. In this paper, we propose a multi-level contrasitive learning approach which applies contrastive losses at different layers of an encoder to learn multiple representations from the encoder.
Multi-Representation Ensemble in Few-Shot Learning
Deep neural networks (DNNs) compute representations in a layer by layer fashion, producing a final representation at the top layer of the pipeline, and classification or regression is made using the final representation.
AP18-OLR Challenge: Three Tasks and Their Baselines
The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles.
AP17-OLR Challenge: Data, Plan, and Baseline
We present the data profile and the evaluation plan of the second oriental language recognition (OLR) challenge AP17-OLR.
A deep learning approach for predicting the quality of online health expert question-answering services
Finally, a deep belief network (DBN)-based HQA answer quality prediction framework is proposed to predict the quality of answers by learning the high-level hidden semantic representation from the physicians' answers.
AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline
We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016.
OC16-CE80: A Chinese-English Mixlingual Database and A Speech Recognition Baseline
We present the OC16-CE80 Chinese-English mixlingual speech database which was released as a main resource for training, development and test for the Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on O-COCOSDA 2016.
