Zobrazeno 1 - 10
of 161
pro vyhledávání: '"He, Yihan"'
We investigate the ground-state probabilistic logic based on a binary energy landscape (GSPL-BEL) model, implementing the many-body interactions within Ising model cells. The GSPL-BEL model offers a simplified binary energy landscape, enabling the co
Externí odkaz:
http://arxiv.org/abs/2311.00410
We study the problem of testing and recovering the hidden $k$-clique Ferromagnetic correlation in the planted Random Field Curie-Weiss model (a.k.a. the pRFCW model). The pRFCW model is a random effect Ising model that exhibits richer phase diagrams
Externí odkaz:
http://arxiv.org/abs/2310.00667
We study the problem of testing and recovering $k$-clique Ferromagnetic mean shift in the planted Sherrington-Kirkpatrick model (i.e., a type of spin glass model) with $n$ spins. The planted SK model -- a stylized mixture of an uncountable number of
Externí odkaz:
http://arxiv.org/abs/2309.14192
Publikováno v:
IEEE Electron Device Letters
A probabilistic-bit (p-bit) is the fundamental building block in the circuit network of a stochastic computing, and it could produce a continuous random bit-stream with tunable probability. Utilizing the stochasticity in few-domain ferroelectric mate
Externí odkaz:
http://arxiv.org/abs/2302.02305
Autor:
Xu, Chejian, Ding, Wenhao, Lyu, Weijie, Liu, Zuxin, Wang, Shuai, He, Yihan, Hu, Hanjiang, Zhao, Ding, Li, Bo
As shown by recent studies, machine intelligence-enabled systems are vulnerable to test cases resulting from either adversarial manipulation or natural distribution shifts. This has raised great concerns about deploying machine learning algorithms fo
Externí odkaz:
http://arxiv.org/abs/2206.09682
Publikováno v:
In Food Chemistry 1 September 2024 451
Publikováno v:
In Food Chemistry 15 January 2025 463 Part 2
Autor:
He, Yihan, Bruna, Joan
In the problem of structured prediction with graph representation learning (GRL for short), the hypothesis returned by the algorithm maps the set of features in the \emph{receptive field} of the targeted vertex to its label. To understand the learnab
Externí odkaz:
http://arxiv.org/abs/2111.12865
Autor:
He, Yihan
This work addressed the problem of learning a network with communication between vertices. The communication between vertices is presented in the form of perturbation on the measure. We studied the scenario where samples are drawn from a uniform ergo
Externí odkaz:
http://arxiv.org/abs/2111.10708
Autor:
He, Yihan
We consider the problem of recovering the rank of a set of $n$ items based on noisy pairwise comparisons. We assume the SST class as the family of generative models. Our analysis gave sharp information theoretic upper and lower bound for the exact re
Externí odkaz:
http://arxiv.org/abs/2111.10021