Databricks built a RAG agent it says can handle every kind of enterprise search

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.
IEEE

Regularization Effect of Random Node Fault/Noise on Gradient Descent Learning Algorithm

Abstract: For decades, adding fault/noise during training by gradient descent has been a technique for getting a neural network (NN) tolerant to persistent fault/noise or getting an NN with better ...
IEEE

Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation

Abstract: Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples. Compared to ...
GitHub

The Impact of Positional Encoding on Length Generalization in Transformers

Length generalization, the ability to generalize from small training context sizes to larger ones, is a critical challenge in the development of Transformer-based language models. Positional encoding ...