Zobrazeno 1 - 10
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pro vyhledávání: '"Tran, An"'
Designing neural network architectures that can handle data symmetry is crucial. This is especially important for geometric graphs whose properties are equivariance under Euclidean transformations. Current equivariant graph neural networks (EGNNs), p
Externí odkaz:
http://arxiv.org/abs/2410.04692
Autor:
Vo, Thieu N., Tran, Viet-Hoang, Huu, Tho Tran, The, An Nguyen, Tran, Thanh, Nguyen-Nhat, Minh-Khoi, Pham, Duy-Tung, Nguyen, Tan Minh
Neural Functional Networks (NFNs) have gained increasing interest due to their wide range of applications, including extracting information from implicit representations of data, editing network weights, and evaluating policies. A key design principl
Externí odkaz:
http://arxiv.org/abs/2410.04213
Autor:
Tran, Viet-Hoang, Vo, Thieu N., The, An Nguyen, Huu, Tho Tran, Nguyen-Nhat, Minh-Khoi, Tran, Thanh, Pham, Duy-Tung, Nguyen, Tan Minh
This paper systematically explores neural functional networks (NFN) for transformer architectures. NFN are specialized neural networks that treat the weights, gradients, or sparsity patterns of a deep neural network (DNN) as input data and have prove
Externí odkaz:
http://arxiv.org/abs/2410.04209
Neural functional networks (NFNs) have recently gained significant attention due to their diverse applications, ranging from predicting network generalization and network editing to classifying implicit neural representation. Previous NFN designs oft
Externí odkaz:
http://arxiv.org/abs/2409.11697
Recognizing human activities in videos is challenging due to the spatio-temporal complexity and context-dependence of human interactions. Prior studies often rely on single input modalities, such as RGB or skeletal data, limiting their ability to exp
Externí odkaz:
http://arxiv.org/abs/2409.02385
We study the exclusive semileptonic decays $\Upsilon(1S)\to B_{(c)}\ell\bar{\nu}_\ell$, where $\ell = e,\mu,\tau$. The relevant hadronic form factors are calculated using the Covariant Confined Quark Model developed previously by our group. We predic
Externí odkaz:
http://arxiv.org/abs/2408.13776
Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performance on previous ones. In this paper, we introduce a novel approach, Lif
Externí odkaz:
http://arxiv.org/abs/2410.08905
Autor:
Sarwar, Zain, Tran, Van, Bhagoji, Arjun Nitin, Feamster, Nick, Zhao, Ben Y., Chakraborty, Supriyo
Machine learning (ML) models often require large amounts of data to perform well. When the available data is limited, model trainers may need to acquire more data from external sources. Often, useful data is held by private entities who are hesitant
Externí odkaz:
http://arxiv.org/abs/2410.08432
Autor:
Brubaker, Ben, Dasher, A. Suki, Hu, Michael, Jain, Nupur, Li, Yifan, Lin, Yi, Mihaila, Maria, Tran, Van, Ünel, I. Deniz
We use algebraic methods in statistical mechanics to represent a multi-parameter class of polynomials in severable variables as partition functions of a new family of solvable lattice models. The class of polynomials, defined by A.N. Kirillov, is der
Externí odkaz:
http://arxiv.org/abs/2410.07960
Autor:
Bender, Christian, Thuan, Nguyen Tran
In our recent work [3] we introduced the grid-sampling SDE as a proxy for modeling exploration in continuous-time reinforcement learning. In this note, we provide further motivation for the use of this SDE and discuss its wellposedness in the presenc
Externí odkaz:
http://arxiv.org/abs/2410.07778