Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Cobb, Adam D."'
This paper introduces a second-order hyperplane search, a novel optimization step that generalizes a second-order line search from a line to a $k$-dimensional hyperplane. This, combined with the forward-mode stochastic gradient method, yields a secon
Externí odkaz:
http://arxiv.org/abs/2408.10419
We introduce a new amortized likelihood ratio estimator for likelihood-free simulation-based inference (SBI). Our estimator is simple to train and estimates the likelihood ratio using a single forward pass of the neural estimator. Our approach direct
Externí odkaz:
http://arxiv.org/abs/2311.10571
Autor:
Cobb, Adam D., Roy, Anirban, Elenius, Daniel, Heim, F. Michael, Swenson, Brian, Whittington, Sydney, Walker, James D., Bapty, Theodore, Hite, Joseph, Ramani, Karthik, McComb, Christopher, Jha, Susmit
We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs require
Externí odkaz:
http://arxiv.org/abs/2306.05562
Computer-aided design (CAD) is a promising new area for the application of artificial intelligence (AI) and machine learning (ML). The current practice of design of cyber-physical systems uses the digital twin methodology, wherein the actual physical
Externí odkaz:
http://arxiv.org/abs/2211.08138
Bayesian methods hold significant promise for improving the uncertainty quantification ability and robustness of deep neural network models. Recent research has seen the investigation of a number of approximate Bayesian inference methods for deep neu
Externí odkaz:
http://arxiv.org/abs/2202.03770
Autor:
Kiskin, Ivan, Sinka, Marianne, Cobb, Adam D., Rafique, Waqas, Wang, Lawrence, Zilli, Davide, Gutteridge, Benjamin, Dam, Rinita, Marinos, Theodoros, Li, Yunpeng, Msaky, Dickson, Kaindoa, Emmanuel, Killeen, Gerard, Herreros-Moya, Eva, Willis, Kathy J., Roberts, Stephen J.
This paper presents the first large-scale multi-species dataset of acoustic recordings of mosquitoes tracked continuously in free flight. We present 20 hours of audio recordings that we have expertly labelled and tagged precisely in time. Significant
Externí odkaz:
http://arxiv.org/abs/2110.07607
In this paper, we propose a surrogate-assisted evolutionary algorithm (EA) for hyperparameter optimization of machine learning (ML) models. The proposed STEADE model initially estimates the objective function landscape using RadialBasis Function inte
Externí odkaz:
http://arxiv.org/abs/2012.06453
Autor:
Cobb, Adam D., Jalaian, Brian
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) approach that exhibits favourable exploration properties in high-dimensional models such as neural networks. Unfortunately, HMC has limited use in large-data regimes and little work h
Externí odkaz:
http://arxiv.org/abs/2010.06772
While deep learning methods continue to improve in predictive accuracy on a wide range of application domains, significant issues remain with other aspects of their performance including their ability to quantify uncertainty and their robustness. Rec
Externí odkaz:
http://arxiv.org/abs/2007.04466
Autor:
Himes, Michael D., Harrington, Joseph, Cobb, Adam D., Baydin, Atilim Gunes, Soboczenski, Frank, O'Beirne, Molly D., Zorzan, Simone, Wright, David C., Scheffer, Zacchaeus, Domagal-Goldman, Shawn D., Arney, Giada N.
Publikováno v:
Planet. Sci. J. 3 (2022) 91
Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parame
Externí odkaz:
http://arxiv.org/abs/2003.02430