Search Results
Automatic Anomaly Detection in the Cloud Via Statistical Learning
Although there exists a large body of prior research in anomaly detection, existing techniques are not applicable in the context of social network data, owing to the inherent seasonal and trend components in the time series data.
Shoulder Implant X-Ray Manufacturer Classification: Exploring with Vision Transformer
In this paper, we will demonstrate different methods for classifying the manufacturer of a shoulder implant.
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models.
Face.evoLVe: A High-Performance Face Recognition Library
In this paper, we develop face. evoLVe -- a comprehensive library that collects and implements a wide range of popular deep learning-based methods for face recognition.
Build a Deep Neural Network model using CPUs Builds a feed-forward multilayer artificial neural network on an H2OFrame
With hundreds of meetups over the past three years, H2O has become a word-of-mouth phenomenon, growing amongst the data community by a hundred-fold, and is now used by 30, 000+ users and is deployed using R, Python, Hadoop, and Spark in 2000+ corporations.
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
Here we present $\Phi$-SO, a Physical Symbolic Optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints.
GraphScope Flex: LEGO-like Graph Computing Stack
Furthermore, GraphScope Flex accomplishes up to a 2, 400X performance gain in real-world applications, demonstrating its proficiency across a wide range of graph computing scenarios with increased effectiveness.
Distributed, Parallel, and Cluster Computing Databases
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion.
HiFrames: High Performance Data Frames in a Scripting Language
Data frames in scripting languages are essential abstractions for processing structured data.
Distributed, Parallel, and Cluster Computing
Neural Variational Inference for Text Processing
We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.
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Question Answering
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