Random Projections Insights
Exploring the intricacies of influence functions reveals the importance of random projections in managing gradient vectors. Surprisingly, these projections exhibit a peaking behavior, suggesting that they contribute positively to the analysis before information loss sets in. This unexpected finding opens up new avenues for understanding the dynamics of model training and performance.In this clip
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Machine Learning Street Talk (MLST)
Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)
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