Bayesian probability-revision formulae comprise a normative model for concept learning tasks. In an initial study suggested by this consideration, 48 Ss estimated the number of tenable hypotheses remaining on each of 13 trials. On a separate task the same Ss gave one hypothesis on each trial, ...
No optimizer is the "best" across all types of machine learning problems and model architectures. Even just comparing the performance of optimizers is a difficult task. 🤖 We recommend sticking with well-established, popular optimizers, especially when starting a new project. Ideally, choose the...
7) Self-Discovered Reasoning Structures - proposes a new framework, Self-Discover, that enables LLMs to select from multiple reasoning techniques (e.g., critical thinking and thinking step-by-step) to compose task-specific reasoning strategies; outperforms CoT (applied to GPT-4 and PaLM 2) on...
As a result, there has been a recent attention towards building efficient expressive speech synthesis models as another step forward in achieving human-like speech. Therefore, many studies have been devoted to expressive speech synthesis (ETTS) as a hot research area, particularly over the last 5...
In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps....
- stage:'DeployDev'displayName:'Deploy to dev environment'dependsOn:Buildjobs:- deployment:Deploypool:vmImage:'ubuntu-20.04'environment:devvariables:- group:'Release Pipeline'strategy:runOnce:deploy:steps:- download:currentartifact:drop- task:AzureWebApp@1displayName:'Azure App Service Deploy: websit...
In the previous task, you learned how to create a template that contains a single resource and deploy it. Now you're ready to deploy more resources, including a dependency. In this task, you'll add an App Service plan and app to the Bicep template....
Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machi...
efficient and robust automated machine learning robustness in machine learning - jerry li [Paper] robust ml [Paper] robust machine learning models and their applications [Paper] robust machine learning - data analytics and ... - tum [Paper] what is the definition of the robustness of a ...
ML Applications Model Compression and Acceleration Multi-Task and Multi-View Learning NLP inspired Visual Models Online Learning Optimization Semi-Supervised and Unsupervised Learning Transfer Learning Trustworthy Machine Learning To reduce class imbalance, we separate some of the hot sub-topics from the or...