Zobrazeno 1 - 10
of 352
pro vyhledávání: '"Wornell, Gregory W."'
Autor:
Medin, Safa C., Li, Gengyan, Du, Ruofei, Garbin, Stephan, Davidson, Philip, Wornell, Gregory W., Beeler, Thabo, Meka, Abhimitra
3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts$\unicode{x2014}$photorealism, efficiency, compatibility, and configurability. We present a novel representation that enables high-quality vo
Externí odkaz:
http://arxiv.org/abs/2404.13807
Publikováno v:
Proc. of ICASSP, 2024, pp. 9366-9370
Motivated by the proliferation of mobile devices, we consider a basic form of the ubiquitous problem of time-delay estimation (TDE), but with communication constraints between two non co-located sensors. In this setting, when joint processing of the
Externí odkaz:
http://arxiv.org/abs/2404.09244
Autor:
Shen, Maohao, Ryu, J. Jon, Ghosh, Soumya, Bu, Yuheng, Sattigeri, Prasanna, Das, Subhro, Wornell, Gregory W.
This paper questions the effectiveness of a modern predictive uncertainty quantification approach, called \emph{evidential deep learning} (EDL), in which a single neural network model is trained to learn a meta distribution over the predictive distri
Externí odkaz:
http://arxiv.org/abs/2402.06160
Autor:
Ryu, J. Jon, Wornell, Gregory W.
A confidence sequence (CS) is a sequence of confidence sets that contains a target parameter of an underlying stochastic process at any time step with high probability. This paper proposes a new approach to constructing CSs for means of bounded multi
Externí odkaz:
http://arxiv.org/abs/2402.03683
Autor:
Ryu, J. Jon, Xu, Xiangxiang, Erol, H. S. Melihcan, Bu, Yuheng, Zheng, Lizhong, Wornell, Gregory W.
Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scientific computing problems. For high-dimensional eigenvalue problems, tr
Externí odkaz:
http://arxiv.org/abs/2402.03655
We consider the classical problem of learning, with arbitrary accuracy, the natural parameters of a $k$-parameter truncated \textit{minimal} exponential family from i.i.d. samples in a computationally and statistically efficient manner. We focus on t
Externí odkaz:
http://arxiv.org/abs/2309.06413
Autor:
Jayashankar, Tejas, Lee, Gary C. F., Lancho, Alejandro, Weiss, Amir, Polyanskiy, Yury, Wornell, Gregory W.
We propose a new method for separating superimposed sources using diffusion-based generative models. Our method relies only on separately trained statistical priors of independent sources to establish a new objective function guided by maximum a post
Externí odkaz:
http://arxiv.org/abs/2306.14411
Double-descent refers to the unexpected drop in test loss of a learning algorithm beyond an interpolating threshold with over-parameterization, which is not predicted by information criteria in their classical forms due to the limitations in the stan
Externí odkaz:
http://arxiv.org/abs/2306.05583
Autor:
Choi, Jae Won, Chowdhary, Girish, Singer, Andrew C., Vishnu, Hari, Weiss, Amir, Wornell, Gregory W., Deane, Grant
Underwater communication signals typically suffer from distortion due to motion-induced Doppler. Especially in shallow water environments, recovering the signal is challenging due to the time-varying Doppler effects distorting each path differently.
Externí odkaz:
http://arxiv.org/abs/2305.19127
Key challenges in developing underwater acoustic localization methods are related to the combined effects of high reverberation in intricate environments. To address such challenges, recent studies have shown that with a properly designed architectur
Externí odkaz:
http://arxiv.org/abs/2305.17920