Zobrazeno 1 - 10
of 14 736
pro vyhledávání: '"Nathan, M"'
Autor:
Qayyum, Alif Bin Abdul, Mertins, Susan D., Paulson, Amanda K., Urban, Nathan M., Yoon, Byung-Jun
The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured molecular space. The junction tree variational autoencoder (JTVAE) has b
Externí odkaz:
http://arxiv.org/abs/2411.03460
The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming. To enhance the analysis and storage of wind data, we introduce a deep learning fr
Externí odkaz:
http://arxiv.org/abs/2409.17367
Autor:
Rahmati, Amir Hossein, Fan, Mingzhou, Zhou, Ruida, Urban, Nathan M., Yoon, Byung-Jun, Qian, Xiaoning
Instead of randomly acquiring training data points, Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected based on the prediction uncertainty, thereby aiming at minimizing the lab
Externí odkaz:
http://arxiv.org/abs/2408.13690
Autor:
Hsu, Heng-Chia, Dasgupta, Kaushik, Neihart, Nathan M., Shekhar, Sudip, Walling, Jeffrey S., Allstot, David J.
A comprehensive study of methods of maximizing Q for slow-wave coplanar waveguides is described. In addition to the widths of the signal conductor and coplanar ground lines and the distance between them, the length, spacing and stacking of the metal
Externí odkaz:
http://arxiv.org/abs/2408.14482
A topological quantum number, the Witten index, characterizes supersymmetric models by probing for zero energy modes and the possibility of supersymmetry breaking. We propose an averaging method to infer the Witten index in quantum analogue simulator
Externí odkaz:
http://arxiv.org/abs/2405.21073
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune t
Externí odkaz:
http://arxiv.org/abs/2405.20573
We construct a hybrid quantum algorithm to estimate gaps in many-body energy spectra and prove that it is inherently fault-tolerant to global multi-qubit depolarizing noise. Using trial-state optimization without active error correction, we show that
Externí odkaz:
http://arxiv.org/abs/2405.10306
Deep generative models have been accelerating the inverse design process in material and drug design. Unlike their counterpart property predictors in typical molecular design frameworks, generative molecular design models have seen fewer efforts on u
Externí odkaz:
http://arxiv.org/abs/2405.00202
Classical systems placed in contact with a thermal bath will inevitably equilibrate to a thermal state at the bath temperature. The same is not generally true for open quantum systems, which place additional conditions on the structure of the bath an
Externí odkaz:
http://arxiv.org/abs/2403.00197
Autor:
Dunfield, Nathan M., Tiozzo, Giulio
Motivated by an observation of Dehornoy, we study the roots of Alexander polynomials of knots and links that are closures of positive 3-strand braids. We give experimental data on random such braids and find that the roots exhibit marked patterns, wh
Externí odkaz:
http://arxiv.org/abs/2402.06771