DeepSeek has expanded its R1 whitepaper by 60 pages to disclose training secrets, clearing the path for a rumored V4 coding ...
Abstract: Expensive multi-objective binary optimization problems frequently emerge in real-world applications, where evaluating a single solution incurs significant computational or physical costs.
Researchers from Cornell and Google introduce a unified Regression Language Model (RLM) that predicts numeric outcomes directly from code strings—covering GPU kernel latency, program memory usage, and ...
Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses the kernel matrix inverse (Cholesky ...
Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
This important study combines the use of Fisher Kernels with Hidden Markov models aiming to improve brain-behaviour prediction. The evidence supporting the authors' conclusions is compelling, ...
Abstract: This paper introduces a novel kernel regression framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM). Motivated by ...
Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland National Center for ...
Back in the early 1990s, I worked in a large telecoms research lab, as part of the Advanced Local Loop group. Our problem domain was the “last mile”—getting services to peoples’ homes. One of my ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback