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Found 4 papers, 1 papers with code
Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory
In this paper, we are the first to characterize the trade-off of performance and index size in existing SSD-based graph and cluster indexes: to improve throughput by {5. 7\,$\times$} and {1. 7\,$\times$}, these indexes have to pay a {5. 8\,$\times$} storage amplification and {7. 7\,$\times$} with respect to the dataset size, respectively.
Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation
However, in some domain-specific scenarios, building such a large paired corpus for code generation is difficult because there is no directly available pairing data, and a lot of effort is required to manually write the code descriptions to construct a high-quality training dataset.
Towards Full-line Code Completion with Neural Language Models
In this paper, we take a further step and discuss the probability of directly completing a whole line of code instead of a single token.
Self-adaptive Single and Multi-illuminant Estimation Framework based on Deep Learning
In this paper, we proposed a self-adaptive single and multi-illuminant estimation framework, which includes the following novelties: (1) Learning local self-adaptive kernels from the entire image for illuminant estimation with encoder-decoder CNN structure; (2) Providing confidence measurement for the prediction; (3) Clustering-based iterative fitting for computing single and multi-illumination vectors.
