Many applications in natural language processing rely on adapting one large-scale, pre-trained language model to multiple downstream applications.

Can MLLMs effectively serve as judges in the multimodal domain, and how closely do their evaluations align with human preferences?

LDA's main function is not dimension reduction but Topic Modeling, and the definition of Topic Modeling, borrowing from the expression in the paper, is as follows

The transformer model represents another significant leap in the NLP field after the Seq2Seq model and the attention mechanism.

The attention mechanism was proposed to improve the Seq2Seq model. To understand the attention mechanism well, you must understand the Seq2Seq model.

The learning and generation principles of Diffusion Models, which model multi-layer transformations and inverse transformations between data distribution and noise distribution, share many similarities with Flow Models.

So, on what principle do generative models, the core of generative AI, learn and operate? What does 'generation' mean? In the fields of statistics or machine learning, generation is defined as sampling from a probability distribution in data space, which was not observed or used during the learning process.

Today we'll look at <Ray tracing> the dominant technique used in the graphics industry.

Paper Review of Image Style Transfer Using Convolutional Neural Networks.

Basics of MLOps using basic vision project.

Paper review of Physics-based Human Motion Estimation and Synthesis from Videos

Basics of MLOps using basic vision project.

Gradient descent method is also called as steepest descent method.

ADAM is the use of Gradient Descent with Momentum and RMSProp at the same time.

Linear regression is a linear approach for modeling the relationship between a scalar response and one or more explanatory variables.

KL Divergence (Kullback-Leibler) also known as Relative Entropy is all about the difference between two distributions.

Bayesian probability is an interpretation of the concept of probability.

Framework for estimating generative models.