Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs).
Large language models (LLMs) often generate inaccurate or fabricated information and generally fail to indicate their confidence, which limits their broader applications.
To this end, we explore the sparse representation and review the task design for end-to-end autonomous driving, proposing a new paradigm named SparseDrive.
Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making prototype verification critical to quality assessment.
Accuracy and speed are critical in image editing tasks.
Our top-performing model, built on Llama3-8B-Instruct, achieves a remarkable 44. 7 length-controlled win rate on AlpacaEval 2 -- surpassing Claude 3 Opus on the leaderboard, and a 33. 8 win rate on Arena-Hard -- making it the strongest 8B open-source model.
While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge.
Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting.
The development of text-to-video (T2V), i. e., generating videos with a given text prompt, has been significantly advanced in recent years.
In addition, we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts.