Zobrazeno 1 - 10
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pro vyhledávání: '"Wu, Chenxi"'
Autor:
Toscano, Juan Diego, Oommen, Vivek, Varghese, Alan John, Zou, Zongren, Daryakenari, Nazanin Ahmadi, Wu, Chenxi, Karniadakis, George Em
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the pa
Externí odkaz:
http://arxiv.org/abs/2410.13228
Autor:
He, Yan Mary, Wu, Chenxi
We study endperiodic maps of an infinite graph with finitely many ends. We prove that any such map is homotopic to an endperiodic relative train track map. Moreover, we show that the (largest) Perron-Frobenius eigenvalue of the transition matrix is a
Externí odkaz:
http://arxiv.org/abs/2408.13401
Polynomial neural networks have been implemented in a range of applications and present an advantageous framework for theoretical machine learning. A polynomial neural network of fixed architecture and activation degree gives an algebraic map from th
Externí odkaz:
http://arxiv.org/abs/2408.04569
Every pseudo-Anosov flow $\phi$ in a closed $3$-manifold $M$ gives rise to an action of $\pi_1(M)$ on a circle $S^{1}_{\infty}(\phi)$ from infinity \cite{Fen12}, with a pair of invariant \emph{almost} laminations. From certain actions on $S^{1}$ with
Externí odkaz:
http://arxiv.org/abs/2407.07634
The new polymath Large Language Models (LLMs) can speed-up greatly scientific reviews, possibly using more unbiased quantitative metrics, facilitating cross-disciplinary connections, and identifying emerging trends and research gaps by analyzing larg
Externí odkaz:
http://arxiv.org/abs/2312.03769
Autor:
Richter, Ole, Wu, Chenxi, Whatley, Adrian M., Köstinger, German, Nielsen, Carsten, Qiao, Ning, Indiveri, Giacomo
Publikováno v:
Neuromorph. Comput. Eng. 4 (2024)
With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract r
Externí odkaz:
http://arxiv.org/abs/2310.00564
Autor:
Zhang, Qian, Wu, Chenxi, Kahana, Adar, Kim, Youngeun, Li, Yuhang, Karniadakis, George Em, Panda, Priyadarshini
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared to traditional Artificial Neural
Externí odkaz:
http://arxiv.org/abs/2308.16372
We prove a lower bound on the number of spanning two-forests in a graph, in terms of the number of vertices, edges, and spanning trees. This implies an upper bound on the average cut size of a random two-forest. The main tool is an identity relating
Externí odkaz:
http://arxiv.org/abs/2308.03859
Publikováno v:
IOP Neuromorphic Computing and Engineering 2024 4 (1)
Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads, taking advantage of sparse asynchronous computation within Spiking Neural Networks (SNNs). However, deploying robust applications to these device
Externí odkaz:
http://arxiv.org/abs/2303.12167
It is well-known that the parameterized family of functions representable by fully-connected feedforward neural networks with ReLU activation function is precisely the class of piecewise linear functions with finitely many pieces. It is less well-kno
Externí odkaz:
http://arxiv.org/abs/2209.04036