Zobrazeno 1 - 10
of 1 777
pro vyhledávání: '"Behboodi, A."'
On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing
Autor:
Behboodi, Arash, Cesa, Gabriele
Weight sharing, equivariance, and local filters, as in convolutional neural networks, are believed to contribute to the sample efficiency of neural networks. However, it is not clear how each one of these design choices contribute to the generalizati
Externí odkaz:
http://arxiv.org/abs/2411.14288
Autor:
Khisti, Ashish, Ebrahimi, M. Reza, Dbouk, Hassan, Behboodi, Arash, Memisevic, Roland, Louizos, Christos
We consider multi-draft speculative sampling, where the proposal sequences are sampled independently from different draft models. At each step, a token-level draft selection scheme takes a list of valid tokens as input and produces an output token wh
Externí odkaz:
http://arxiv.org/abs/2410.18234
Autor:
Silvestri, Gianluigi, Massoli, Fabio Valerio, Orekondy, Tribhuvanesh, Abdi, Afshin, Behboodi, Arash
A promising way to mitigate the expensive process of obtaining a high-dimensional signal is to acquire a limited number of low-dimensional measurements and solve an under-determined inverse problem by utilizing the structural prior about the signal.
Externí odkaz:
http://arxiv.org/abs/2407.07794
Compressed sensing combines the power of convex optimization techniques with a sparsity-inducing prior on the signal space to solve an underdetermined system of equations. For many problems, the sparsifying dictionary is not directly given, nor its e
Externí odkaz:
http://arxiv.org/abs/2407.06646
Modelling the propagation of electromagnetic wireless signals is critical for designing modern communication systems. Wireless ray tracing simulators model signal propagation based on the 3D geometry and other scene parameters, but their accuracy is
Externí odkaz:
http://arxiv.org/abs/2406.14995
In recent years, solving optimization problems involving black-box simulators has become a point of focus for the machine learning community due to their ubiquity in science and engineering. The simulators describe a forward process $f_{\mathrm{sim}}
Externí odkaz:
http://arxiv.org/abs/2406.04261
Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability. Intuitively, the size of the prediction set encodes a general n
Externí odkaz:
http://arxiv.org/abs/2405.02140
Autor:
Arnold, Maximilian, Major, Bence, Massoli, Fabio Valerio, Soriaga, Joseph B., Behboodi, Arash
In the context of communication networks, digital twin technology provides a means to replicate the radio frequency (RF) propagation environment as well as the system behaviour, allowing for a way to optimize the performance of a deployed system base
Externí odkaz:
http://arxiv.org/abs/2401.17781
Autor:
Cesa, Gabriele, Behboodi, Arash
In this work, we introduce a novel approach based on algebraic topology to enhance graph convolution and attention modules by incorporating local topological properties of the data. To do so, we consider the framework of sheaf neural networks, which
Externí odkaz:
http://arxiv.org/abs/2311.10156
Lattice reduction is a combinatorial optimization problem aimed at finding the most orthogonal basis in a given lattice. In this work, we address lattice reduction via deep learning methods. We design a deep neural model outputting factorized unimodu
Externí odkaz:
http://arxiv.org/abs/2311.08170