Zobrazeno 1 - 10
of 486
pro vyhledávání: '"Príncipe Jose C"'
This paper proposes \emph{Episodic and Lifelong Exploration via Maximum ENTropy} (ELEMENT), a novel, multiscale, intrinsically motivated reinforcement learning (RL) framework that is able to explore environments without using any extrinsic reward and
Externí odkaz:
http://arxiv.org/abs/2412.03800
Autor:
Li, Kan, Príncipe, José C.
Motivated by the surge of interest in Koopman operator theory, we propose a machine-learning alternative based on a functional Bayesian perspective for operator-theoretic modeling of unknown, data-driven, nonlinear dynamical systems. This formulation
Externí odkaz:
http://arxiv.org/abs/2411.00198
Autor:
Ma, Shihan, Hu, Bo, Jia, Tianyu, Clarke, Alexander Kenneth, Zicher, Blanka, Caillet, Arnault H., Farina, Dario, Principe, Jose C.
The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capt
Externí odkaz:
http://arxiv.org/abs/2410.14697
The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum sufficient representation $\mathbf{t}$ by striking a trade-off between a
Externí odkaz:
http://arxiv.org/abs/2404.17951
Conventional kernel adaptive filtering (KAF) uses a prescribed, positive definite, nonlinear function to define the Reproducing Kernel Hilbert Space (RKHS), where the optimal solution for mean square error estimation is approximated using search tech
Externí odkaz:
http://arxiv.org/abs/2402.03497
Autor:
Sledge, Isaac J., Byrne, Dominic M., King, Jonathan L., Ostertag, Steven H., Woods, Denton L., Prater, James L., Kennedy, Jermaine L., Marston, Timothy M., Principe, Jose C.
We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels, to uncover a set of semi-sparse
Externí odkaz:
http://arxiv.org/abs/2401.11313
Kernel Adaptive Filtering (KAF) are mathematically principled methods which search for a function in a Reproducing Kernel Hilbert Space. While they work well for tasks such as time series prediction and system identification they are plagued by a lin
Externí odkaz:
http://arxiv.org/abs/2312.12318
This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in multiview systems. By framing view similarities as dependencies and en
Externí odkaz:
http://arxiv.org/abs/2305.20074
The Cauchy-Schwarz (CS) divergence was developed by Pr\'{i}ncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence
Externí odkaz:
http://arxiv.org/abs/2301.08970
This paper presents a close form solution in Reproducing Kernel Hilbert Space (RKHS) for the famed Wiener filter, which we called the functional Wiener filter(FWF). Instead of using the Wiener-Hopf factorization theory, here we define a new lagged RK
Externí odkaz:
http://arxiv.org/abs/2301.00291