157 citations · 172 across the 6 of their papers we have counts for
6 papers
Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks
Hongfei Du, Emre Barut, Fang Jin
Despite the popularity of Convolutional Neural Networks (CNN), the problem of uncertainty quantification (UQ) of CNN has been largely overlooked. Lack of efficient UQ tools severel…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought
Jooyoung Lee, Fan Yang, Thanh Tran +4
We introduce a novel framework, LM-Guided CoT, that leverages a lightweight (i.e., <1B) language model (LM) for guiding a black-box large (i.e., >10B) LM in reasoning tasks. Specif…
Building Goal-Oriented Dialogue Systems with Situated Visual Context
Sanchit Agarwal, Jan Jezabek, Arijit Biswas +3
Most popular goal-oriented dialogue agents are capable of understanding the conversational context. However, with the surge of virtual assistants with screen, the next generation o…
Conditional Sure Independence Screening
Emre Barut, Jianqing Fan, Anneleen Verhasselt
Independence screening is a powerful method for variable selection for `Big Data' when the number of variables is massive. Commonly used independence screening methods are based on…
Adaptive robust variable selection
Jianqing Fan, Yingying Fan, Emre Barut
Heavy-tailed high-dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. A natural procedure to address th…