In industrial image anomaly detection, self-supervised feature reconstruction methods show promise but still grapple with challenges such as generating realistic and diverse anomaly samples while mitigating feature redundancy and pre-training bias. Synthetic anomalies...

AI applications are revolutionizing various industries, including healthcare and finance, leading to a boom in the sector. However, it is still very difficult to guarantee the security and dependability of these complex systems. Envision a medical diagnostic tool...

In artificial intelligence, scaling laws serve as useful guides for developing Large Language Models (LLMs). Like skilled directors, these laws coordinate models’ growth, revealing development patterns that go beyond mere computation. With each step forward, these...

A sparse Mixture of Experts (SMoEs) has gained traction for scaling models, especially useful in memory-constrained setups. They’re pivotal in Switch Transformer and Universal Transformers, offering efficient training and inference. However, implementing SMoEs...

With the recent progress made in the field of Artificial Intelligence (AI) and mainly Generative AI, the ability of Large Language Models (LLMs) to generate text in response to inputs or prompts has been demonstrated. These models are capable of generating text just...

Managing and validating structured data efficiently poses a significant challenge in today’s digital age. Traditional methods of function calling or JSON schema validation often fall short, especially when dealing with large datasets or complex data structures. When...