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Diagram of a Federated Learning protocol with smartphones training a global AI model. Federated learning (also known as collaborative learning) is a machine learning technique focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. [1]
Teaching Assistant Evaluation Dataset Teaching assistant reviews. Features of each instance such as class, class size, and instructor are given. 151 Text Classification 1997 [18] [19] W. Loh et al. Vietnamese Students’ Feedback Corpus (UIT-VSFC) Students’ Feedback. Comments 16,000 Text Classification 1997 [20] Nguyen et al.
The NIST Enterprise Architecture Model is a five-layered model for enterprise architecture, designed for organizing, planning, and building an integrated set of information and information technology architectures. The five layers are defined separately but are interrelated and interwoven. [2] The model defined the interrelation as follows: [3]
[21] [22] ARC also develops techniques to identify and test whether an AI model is potentially dangerous. [4] In April 2023, Christiano told The Economist that ARC was considering developing an industry standard for AI safety. [23] As of April 2024, Christiano was listed as the head of AI safety for the US AI Safety Institute at NIST. [24]
Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration DNN model interconnect Common platform TensorFlow, Keras, Caffe, Torch: Algorithm training No No / Separate files in most formats No No No Yes ONNX: Algorithm training Yes No / Separate files in most formats No No No Yes
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Choice of model: This depends on the data representation and the application. Model parameters include the number, type, and connectedness of network layers, as well as the size of each and the connection type (full, pooling, etc. ). Overly complex models learn slowly. Learning algorithm: Numerous trade-offs exist between learning algorithms.
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...