Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Chew, Joyce"'
Autor:
Venkat, Aarthi, Chew, Joyce, Rodriguez, Ferran Cardoso, Tape, Christopher J., Perlmutter, Michael, Krishnaswamy, Smita
Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically ha
Externí odkaz:
http://arxiv.org/abs/2309.07813
We introduce a class of manifold neural networks (MNNs) that we call Manifold Filter-Combine Networks (MFCNs), that aims to further our understanding of MNNs, analogous to how the aggregate-combine framework helps with the understanding of graph neur
Externí odkaz:
http://arxiv.org/abs/2307.04056
When generalizing schemes for real-valued data approximation or decomposition to data living in Riemannian manifolds, tangent space-based schemes are very attractive for the simple reason that these spaces are linear. An open challenge is to do this
Externí odkaz:
http://arxiv.org/abs/2306.00507
Societal biases in the usage of words, including harmful stereotypes, are frequently learned by common word embedding methods. These biases manifest not only between a word and an explicit marker of its stereotype, but also between words that share r
Externí odkaz:
http://arxiv.org/abs/2305.14574
High-dimensional data arises in numerous applications, and the rapidly developing field of geometric deep learning seeks to develop neural network architectures to analyze such data in non-Euclidean domains, such as graphs and manifolds. Recent work
Externí odkaz:
http://arxiv.org/abs/2212.12606
Autor:
Cheng, Keyi, Inzer, Stefan, Leung, Adrian, Shen, Xiaoxian, Perlmutter, Michael, Lindstrom, Michael, Chew, Joyce, Presner, Todd, Needell, Deanna
We propose a multi-scale hybridized topic modeling method to find hidden topics from transcribed interviews more accurately and efficiently than traditional topic modeling methods. Our multi-scale hybridized topic modeling method (MSHTM) approaches d
Externí odkaz:
http://arxiv.org/abs/2211.13496
Autor:
Chew, Joyce, Hirn, Matthew, Krishnaswamy, Smita, Needell, Deanna, Perlmutter, Michael, Steach, Holly, Viswanath, Siddharth, Wu, Hau-Tieng
The scattering transform is a multilayered, wavelet-based transform initially introduced as a model of convolutional neural networks (CNNs) that has played a foundational role in our understanding of these networks' stability and invariance propertie
Externí odkaz:
http://arxiv.org/abs/2208.08561
Autor:
Chew, Joyce, Steach, Holly R., Viswanath, Siddharth, Wu, Hau-Tieng, Hirn, Matthew, Needell, Deanna, Krishnaswamy, Smita, Perlmutter, Michael
The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-like operators to general manifolds. The initial work on this model foc
Externí odkaz:
http://arxiv.org/abs/2206.10078
Autor:
Li, Pengyu, Tseng, Christine, Zheng, Yaxuan, Chew, Joyce A., Huang, Longxiu, Jarman, Benjamin, Needell, Deanna
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform classi
Externí odkaz:
http://arxiv.org/abs/2201.13324
Autor:
Chew, Joyce, Hirn, Matthew, Krishnaswamy, Smita, Needell, Deanna, Perlmutter, Michael, Steach, Holly, Viswanath, Siddharth, Wu, Hau-Tieng
Publikováno v:
In Applied and Computational Harmonic Analysis May 2024 70