Zobrazeno 1 - 10
of 610
pro vyhledávání: '"Tran Hoang P"'
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
The differences in H2O2 production between conventional (CONV) and ultra-high dose rate (UHDR) irradiations in water radiolysis are still not fully understood. The lower levels of this radiolytic species, as a critical end product of water radiolysis
Externí odkaz:
http://arxiv.org/abs/2409.11993
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
Autor:
Wang, Xingyao, Li, Boxuan, Song, Yufan, Xu, Frank F., Tang, Xiangru, Zhuge, Mingchen, Pan, Jiayi, Song, Yueqi, Li, Bowen, Singh, Jaskirat, Tran, Hoang H., Li, Fuqiang, Ma, Ren, Zheng, Mingzhang, Qian, Bill, Shao, Yanjun, Muennighoff, Niklas, Zhang, Yizhe, Hui, Binyuan, Lin, Junyang, Brennan, Robert, Peng, Hao, Ji, Heng, Neubig, Graham
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways. At the same time, thanks to improvements in large language models (LLMs), there ha
Externí odkaz:
http://arxiv.org/abs/2407.16741
There is a significant gap between our theoretical understanding of optimization algorithms used in deep learning and their practical performance. Theoretical development usually focuses on proving convergence guarantees under a variety of different
Externí odkaz:
http://arxiv.org/abs/2407.01825
We introduce a new zeroth-order algorithm for private stochastic optimization on nonconvex and nonsmooth objectives. Given a dataset of size $M$, our algorithm ensures $(\alpha,\alpha\rho^2/2)$-R\'enyi differential privacy and finds a $(\delta,\epsil
Externí odkaz:
http://arxiv.org/abs/2406.19579
Autor:
Tran, Hoang Anh, Toh, Kim-Chuan
We introduce a S.O.S hierarchy of lower bounds for a polynomial optimization problem whose constraint is expressed as a matrix polynomial semidefinite condition. Our approach involves utilizing a penalty function framework to directly address the mat
Externí odkaz:
http://arxiv.org/abs/2406.12013
This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase
Externí odkaz:
http://arxiv.org/abs/2403.00554
This paper proposes a transition system abstraction framework for neural network dynamical system models to enhance the model interpretability, with applications to complex dynamical systems such as human behavior learning and verification. To begin
Externí odkaz:
http://arxiv.org/abs/2402.11739
We propose and analyze an efficient algorithm for solving the joint sparse recovery problem using a new regularization-based method, named orthogonally weighted $\ell_{2,1}$ ($\mathit{ow}\ell_{2,1}$), which is specifically designed to take into accou
Externí odkaz:
http://arxiv.org/abs/2311.12282