Does model drift occur during training with dropout and inference without it?
Machine Learning / AI
What is user data?
What methods can be used to improve a model?
How does the minimum number of objects in a tree node affect overfitting?
What is secure aggregation?
Are batch statistics or saved parameters used in batch normalization during inference?
What semantics should be reflected in batching for identification?
How does IVF-PQ differ from IVF-Flat?
What is TensorRT-LLM and where does it have advantages?
What are quantized embeddings and how are they used in production search?
How to find a user in the database based on old observations when characteristics change?
What is HyDE (Hypothetical Document Embeddings)?
Why is a look-alike task needed?
How does banner regeneration increase conversions?
What methods are used for selecting negative examples?
What are reference-free and reference-based metrics?