The Duck's Brain: Training and Inference of Neural Networks in Modern Database Engines
Although database systems perform well in data access and manipulation, their relational model hinders data scientists from formulating machine learning algorithms in SQL. Nevertheless, we argue that modern database systems perform well for machine learning algorithms expressed in relational algebra. To overcome the barrier of the relational model, this paper shows how to transform data into a relational representation for training neural networks in SQL: We first describe building blocks for data transformation, model training and inference in SQL-92 and their counterparts using an extended array data type. Then, we compare the implementation for model training and inference using array data types to the one using a relational representation in SQL-92 only. The evaluation in terms of runtime and memory consumption proves the suitability of modern database systems for matrix algebra, although specialised array data types perform better than matrices in relational representation.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset
The modernization of smart farming is a way to improve agricultural production efficiency, and improve the agricultural production environment. Although many large models have achieved high accuracy in the task of object…
Knowledge DistillationObject RecognitionSegmentationDUCK: Distance-based Unlearning via Centroid Kinematics
Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring privacy in modern artificial intelligence models. This technique primarily aims to eradicate any residual influence of a specific …
Inference AttackMachine UnlearningMembership Inference AttackMetric LearningPSyDUCK: Training-Free Steganography for Latent Diffusion
Recent advances in generative AI have opened promising avenues for steganography, which can securely protect sensitive information for individuals operating in hostile environments, such as journalists, activists, and wh…
DenoisingLyra: A Benchmark for Turducken-Style Code Generation
Recently, neural techniques have been used to generate source code automatically. While promising for declarative languages, these approaches achieve much poorer performance on datasets for imperative languages. Since a …
Code GenerationDeveloping a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping
Our solution is implemented in and for the frame of Duckietown. The goal of Duckietown is to provide a relatively simple platform to explore, tackle and solve many problems linked to autonomous driving. "Duckietown" is s…
Autonomous DrivingManagement