Zobrazeno 1 - 10
of 405
pro vyhledávání: '"Chi Ning"'
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
The big energy data and carbon emission monitoring system is designed to collect carbon emission-related data for pollution gas management. This paper constructs a carbon emission monitoring system in the context of carbon neutrality and peaking. A m
Externí odkaz:
https://doaj.org/article/a37b5a2a9d24468cbaa4e00b38c7d290
Understanding how neural systems efficiently process information through distributed representations is a fundamental challenge at the interface of neuroscience and machine learning. Recent approaches analyze the statistical and geometrical attribute
Externí odkaz:
http://arxiv.org/abs/2405.06851
Autor:
Kuoch, Michael, Chou, Chi-Ning, Parthasarathy, Nikhil, Dapello, Joel, DiCarlo, James J., Sompolinsky, Haim, Chung, SueYeon
Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches. However, concrete methodologies for
Externí odkaz:
http://arxiv.org/abs/2312.14285
Autor:
Chou, Chi-Ning
Two transformative waves of computing have redefined the way we approach science. The first wave came with the birth of the digital computer, which enabled scientists to numerically simulate their models and analyze massive datasets. This technologic
Externí odkaz:
http://arxiv.org/abs/2310.20539
Autor:
Chou, Chi-Ning1 chiningchou@g.harvard.edu, Golovnev, Alexander2 alexgolovnev@gmail.com, Sudan, Madhu1 madhu@cs.harvard.edu, Velusamy, Santhoshini3 santhoshini@ttic.edu
Publikováno v:
Journal of the ACM. Apr2024, Vol. 71 Issue 2, p1-74. 74p.
Autor:
Chou, Chi-Ning, Golovnev, Alexander, Shahrasbi, Amirbehshad, Sudan, Madhu, Velusamy, Santhoshini
We analyze the sketching approximability of constraint satisfaction problems on Boolean domains, where the constraints are balanced linear threshold functions applied to literals. In~particular, we explore the approximability of monarchy-like functio
Externí odkaz:
http://arxiv.org/abs/2205.02345
Neural networks are known to use spurious correlations such as background information for classification. While prior work has looked at spurious correlations that are widespread in the training data, in this work, we investigate how sensitive neural
Externí odkaz:
http://arxiv.org/abs/2202.05189
Autor:
Lombo, Andres E., Lares, Jesus E., Castellani, Matteo, Chou, Chi-Ning, Lynch, Nancy, Berggren, Karl K.
Neuromorphic computing is poised to further the success of software-based neural networks by utilizing improved customized hardware. However, the translation of neuromorphic algorithms to hardware specifications is a problem that has been seldom expl
Externí odkaz:
http://arxiv.org/abs/2112.08928
Demonstrating quantum advantage requires experimental implementation of a computational task that is hard to achieve using state-of-the-art classical systems. One approach is to perform sampling from a probability distribution associated with a class
Externí odkaz:
http://arxiv.org/abs/2112.01657
We introduce a notion of \emph{generic local algorithm} which strictly generalizes existing frameworks of local algorithms such as \emph{factors of i.i.d.} by capturing local \emph{quantum} algorithms such as the Quantum Approximate Optimization Algo
Externí odkaz:
http://arxiv.org/abs/2108.06049