Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Panahi, Ashkan"'
Millimeter wave (mmWave) multiple-input-multi-output (MIMO) is now a reality with great potential for further improvement. We study full-duplex transmissions as an effective way to improve mmWave MIMO systems. Compared to half-duplex systems, full-du
Externí odkaz:
http://arxiv.org/abs/2402.03886
Autor:
Shahriari-Mehr, Firooz, Panahi, Ashkan
We address a decentralized convex optimization problem, where every agent has its unique local objective function and constraint set. Agents compute at different speeds, and their communication may be delayed and directed. For this setup, we propose
Externí odkaz:
http://arxiv.org/abs/2401.03136
We provide exact asymptotic expressions for the performance of regression by an $L-$layer deep random feature (RF) model, where the input is mapped through multiple random embedding and non-linear activation functions. For this purpose, we establish
Externí odkaz:
http://arxiv.org/abs/2302.06210
Considering the spectral properties of images, we propose a new self-attention mechanism with highly reduced computational complexity, up to a linear rate. To better preserve edges while promoting similarity within objects, we propose individualized
Externí odkaz:
http://arxiv.org/abs/2211.15595
Autor:
Shahriari-Mehr, Firooz, Panahi, Ashkan
We consider a generic decentralized constrained optimization problem over static, directed communication networks, where each agent has exclusive access to only one convex, differentiable, local objective term and one convex constraint set. For this
Externí odkaz:
http://arxiv.org/abs/2210.03232
In this paper, we address the problem of unsupervised Domain Adaptation. The need for such an adaptation arises when the distribution of the target data differs from that which is used to develop the model and the ground truth information of the targ
Externí odkaz:
http://arxiv.org/abs/2210.00479
Publikováno v:
AAAI Conference on Artificial Intelligence. 37, 8 (Jun. 2023), 9882-9890
Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. When variables are missing in recurring patterns, fitting separate pattern submodels have been proposed as a solutio
Externí odkaz:
http://arxiv.org/abs/2206.11161
We compute precise asymptotic expressions for the learning curves of least squares random feature (RF) models with either a separable strongly convex regularization or the $\ell_1$ regularization. We propose a novel multi-level application of the con
Externí odkaz:
http://arxiv.org/abs/2204.02678
In this paper, we consider the convex, finite-sum minimization problem with explicit convex constraints over strongly connected directed graphs. The constraint is an intersection of several convex sets each being known to only one node. To solve this
Externí odkaz:
http://arxiv.org/abs/2106.11408
Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of ident
Externí odkaz:
http://arxiv.org/abs/2006.10657