Filter Pruning via Filters Similarity in Consecutive Layers

Filter pruning is widely adopted to compress and accelerate the Convolutional Neural Networks (CNNs), but most previous works ignore the relationship between filters and channels in different layers. Processing each layer independently fails to utilize the collaborative relationship across layers. In this paper, we intuitively propose a novel pruning method by explicitly leveraging the Filters Similarity in Consecutive Layers (FSCL). FSCL compresses models by pruning filters whose corresponding features are more worthless in the model. The extensive experiments demonstrate the effectiveness of FSCL, and it yields remarkable improvement over state-of-the-art on accuracy, FLOPs and parameter reduction on several benchmark models and datasets.Comment: Accepted by ICASSP 2023 (oral

A gauss function based approach for unbalanced ontology matching

Ontology matching, aiming to obtain semantic correspon-dences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the appli-cations cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community. In this paper, we propose a novel Gauss Function based ontology matching approach to deal with this unbalanced ontology matching issue. Given a relative lightweight on-tology which represents the local domain knowledge, we ex-tract a“similar ” sub-ontology from the corresponding heavy-weight ontology and then carry out the matching procedure between this lightweight ontology and the newly generated sub-ontology. The sub-ontology generation is based on the influences between concepts in the heavyweight ontology. We propose a Gauss Function based method to properly cal-culate the influence values between concepts. In addition, we perform an extensive experiment to verify the effective-ness and efficiency of our proposed approach by using OAEI 2007 tasks. Experimental results clearly demonstrate that our solution outperforms the existing methods in terms of precision, recall and elapsed time

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling. Our benchmark is released at \url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}