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(PDF) Probabilistic Modeling of Dissolved Gas Concentration for Predicting Operating Status of Oil-Immersed Transformers

PDF | Accurate prediction of dissolved gas concentration is vital for status assessment and fault diagnosis in oil-immersed transformers.
2 weeks ago

Transformer? Diffusion? Transfusion! | by Mengliu Zhao | Sep, 2024

Towards Data Science

Recently, Meta and Waymo released their latest paper — Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model,...
2 months ago

KCL Leverages Topos Theory to Decode Transformer Architectures

Synced | AI Technology & Industry Review

The transformer architecture has emerged as the predominant framework for deep learning, playing a pivotal role in the remarkable...
7 months ago

Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis

This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in...
9 months ago

The Representative Capacity of Transformer Language Models LMs with n-gram Language Models LMs: Capturing the Parallelizable Nature of n-gram LMs

Researchers from ETH Zurich studied the representative capacity of transformer LMs with n-gram LMs. They successfully demonstrated that it is easy to capture...
6 months ago

Hands-On with Moirai: A Foundation Forecasting Model by Salesforce

Towards Data Science

Researchers from Salesforce propose a foundation model capable of probabilistic zero-shot forecasting, while also supporting exogenous features.
2 months ago

Amazon AI Researchers Introduce Chronos: A New Machine Learning Framework for Pretrained Probabilistic Time Series Models

Amazon AI Researchers Introduce Chronos: A New Machine Learning Framework for Pretrained Probabilistic Time Series Models.
7 months ago

Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents

ScienceDirect.com

Biochar adsorbents synthesized from food and agricultural wastes are commonly applied to eliminate heavy metal (HM) ions from wastewater.
9 months ago

Human-like systematic generalization through a meta-learning neural network

Here we successfully address Fodor and Pylyshyn's challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for...
12 months ago

Generative AI, Explained

Generative AI refers to artificial intelligence systems that are designed to create new and original content based on the data they are trained on.
20 months ago