VP - AI @ JPMorgan || IISc PhD Gold Medalist/Best PhD Thesis Award II PhD (IISc, Bangalore) || M.Tech (IIT) ll IEEE Reviewer II Multimodal LLMs & Diffusion Models ll CV-NLP || 20+ Publications ||Qualcomm/Jio/OLA
Brain Teaser #11: What mathematical arguments can explain how **Layer Normalization** enhances training stability in large language models (LLMs) and Transformer architectures, particularly in mitigating issues like vanishing and exploding gradients? Answer: Training destabilization in neural networks, particularly due to gradient explosion or vanishing, typically occurs because of the high condition numbers of weight matrices during backpropagation. These large condition numbers lead to unstable gradient scaling, either amplifying gradients excessively or diminishing them, hindering effective learning. How Layer Normalization overcomes and brings stability in training is briefly discussed here: https://lnkd.in/gmWm42tQ #MachineLearning #DeepLearning #Transformers #LargeLanguageModels #LayerNormalization #GradientStability #AIResearch #DataScience #NeuralNetworks #AIInsights
Arjit Bhardwaj
3mo
• LayerNorm normalizes the activations within a layer which stabilizes the network's dynamics by reducing the sensitivity to extreme values. • To avoid vanishing gradients, it keeps the activations within a manageable range (unit variance) throughout the network whereas for large gradients it just normalizes the layer output. • It ensures stable distribution of activations regardless of batch size or sequence length. • It allows each weight update to contribute evenly to learning.
Praveen Kumar Pokala, PhD Layer normalization standardizes the inputs within a layer, ensuring that activations maintain a consistent range across different layers and mini-batches. By doing this, it prevents the internal covariate shift and keeps gradients in a stable range, indirectly addressing the vanishing and exploding gradient problem. Interesting point to know, applying layer normalization in all layers is common because it maximizes gradient stability and maintains consistency across the model. However, there are alternative ways to selectively apply normalization, monitoring gradient variance across layers can help identify specific layers where normalization will have the most impact. The selective normalization might be explored in specific applications where computational constraints are a priority.
VP - AI @ JPMorgan || IISc PhD Gold Medalist/Best PhD Thesis Award II PhD (IISc, Bangalore) || M.Tech (IIT) ll IEEE Reviewer II Multimodal LLMs & Diffusion Models ll CV-NLP || 20+ Publications ||Qualcomm/Jio/OLA
3moAnswer: Training destabilization in neural networks, particularly due to gradient explosion or vanishing, typically occurs because of the high condition numbers of weight matrices during backpropagation. These large condition numbers lead to unstable gradient scaling, either amplifying gradients excessively or diminishing them, hindering effective learning. How Layer Normalization overcomes and brings stability in training is briefly discussed here: https://www.youtube.com/watch?v=Pe9tvgXPLoE