Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Dutta, Abhratanu"'
Autor:
Sterbentz, Marko, Barrie, Cameron, Shahi, Shubham, Dutta, Abhratanu, Hooshmand, Donna, Pack, Harper, Hammond, Kristian J.
Large language models (LLMs) are capable of producing documents, and retrieval augmented generation (RAG) has shown itself to be a powerful method for improving accuracy without sacrificing fluency. However, not all information can be retrieved from
Externí odkaz:
http://arxiv.org/abs/2406.12069
Autor:
Sterbentz, Marko, Barrie, Cameron, Hooshmand, Donna, Shahi, Shubham, Dutta, Abhratanu, Pack, Harper, Zhao, Andong Li, Paley, Andrew, Einarsson, Alexander, Hammond, Kristian
The principal goal of data science is to derive meaningful information from data. To do this, data scientists develop a space of analytic possibilities and from it reach their information goals by using their knowledge of the domain, the available da
Externí odkaz:
http://arxiv.org/abs/2311.12848
We study the design of computationally efficient algorithms with provable guarantees, that are robust to adversarial (test time) perturbations. While there has been an proliferation of recent work on this topic due to its connections to test time rob
Externí odkaz:
http://arxiv.org/abs/1911.04681
The Euclidean k-means problem is arguably the most widely-studied clustering problem in machine learning. While the k-means objective is NP-hard in the worst-case, practitioners have enjoyed remarkable success in applying heuristics like Lloyd's algo
Externí odkaz:
http://arxiv.org/abs/1712.01241