tf.data: A Machine Learning Data Processing Framework 阅读笔记 tf.data (tf.data: Build TensorFlow input pipelines | TensorFlow Core) 是一个用于高效创建和执行机器学习任务输入数据 pipeline 的框架,目前已经集成在了流行的 TensorFlow 框架中。并且在2017年的时候,Google 就已经在实际的线上业务中使用了 tf...
we proposed a"data processing framework"to improve the quality of low field NMR echo data based on dictionary learning.Dictionary learning is a machine learning method based on redundancy and sparse representation theory.Available information in noisy NMR echo data can be adaptively extracted and ...
Pax is a framework to configure and run machine learning experiments on top of Jax. Quickstart Setting up a Cloud TPU VM We refer tothis pagefor more exhaustive documentation about starting a Cloud TPU project. The following command is sufficient to create a Cloud TPU VM with 8 cores from a...
In this work, we propose a novel machine learning framework, named BERE, for automatically extracting biomedical relations from large-scale literature repositories. BERE uses a hybrid encoding network to better represent each sentence from both semantic and syntactic aspects, and employs a feature ...
A Hybrid Framework for Query Processing and Data Analytics on Spark In this paper, we propose a hybrid framework for query processing and data analytics over large-scale data on Spark, to support multi-paradigm process (incl. SQL, OLAP, data mining, machine learning... H Chen,X Zhang,J Zh...
Machine learning We developed an integrated framework of machine learning to discriminate patients with SZ from HCs (Fig.2). Briefly, the framework involved three phases: the data preparation, model training, and independent model testing.
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech) - NVIDIA/NeMo
In addition, the progress of NCD research has been hampered by privacy of health and medical data. In this paper, a hierarchical idea has been proposed to study the effects of various factors on diseases, and a data-driven framework named d-DC with good extensibility is presented. d-DC ...
In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the experts framework. In our proposal, each of several ‘experts’ guesses a fixed value. The weighted average of these
While the promise of machine learning has been explored in a variety of scientific disciplines, its application in creation of a framework for computationally expensive transient models has not been fully explored. Here, we present an ensemble approach where one such computationally expensive tool, ...