Скачать или смотреть Why Does Scaling Self-Attention Create Instability?

Ever wondered why scaling self-attention, a fundamental component in many powerful AI models like Transformers, can lead to unexpected instability? This video dives deep into the core reasons behind these challenges, exploring how large attention scores and sequence lengths impact model training and performance.

In this video, we explore the intricate challenges associated with scaling self-attention mechanisms:
► Uncover how large attention scores can lead to gradient instability, causing training to diverge or slow down significantly.
► Understand the computational overhead associated with scaling self-attention and its impact on efficiency.
► Examine how increasing sequence lengths can exacerbate these issues, making models harder to train effectively.
► Discover potential architectural modifications and normalization techniques designed to mitigate these scaling problems.


#SelfAttention, #AIStability, #MachineLearning, #Transformers, #DeepLearning, #AIExplained