Search · four archives
Search · four archives
19 papers · ranked by Valyu relevance
Amir Masoud Jafarpisheh, Ali Khalesi, Petros Elia
—The introduction of the new multi-user linearlyseparable distributed computing framework, has recently revealed how a parallel treatment of users can yield large parallelization gains with relatively low computation and communication costs. These gains stem from a new approach that converts the computing problem into…
Haoning Chen, Minquan Cheng, Zhenhao Huang, Youlong Wu
Computation Cost Authors: ['Haoning Chen' 'Minquan Cheng' 'Zhenhao Huang' 'Youlong Wu'] Abstract—The distributed linearly separable computation problem finds extensive applications across domains such as distributed gradient coding, distributed linear transform, real-time rendering, etc. In this paper, we investigate…
Kai Wan, Hua Sun, Mingyue Ji, Giuseppe Caire
Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. For this purpose, the data center assigns a…
Romain D. Cazé, Marcel Stimberg
In theory, neurons modelled as single layer perceptrons can implement all linearly separable computations. In practice, however, these computations may require arbitrarily precise synaptic weights. This is a strong constraint since both biological neurons and their artificial counterparts have to cope with limited…
Kai Wan, Hua Sun, Mingyue Ji, Giuseppe Caire
This paper studies the distributed linearly separable computation problem, which is a generalization of many existing distributed computing problems such as distributed gradient descent and distributed linear transform. In this problem, a master asks N distributed workers to compute a linearly separable function of K…
Kai Wan, Sun Hua, Mingyue Ji, Giuseppe Caire
—This paper formulates a distributed computation problem, where a master asks N distributed workers to compute a linearly separable function. The task function can be expressed as Kc linear combinations of K messages, where each message is a function of one dataset. Our objective is to find the optimal tradeoff between…
Marco Gherardi, David Cuesta-Frau
Linear separability, a core concept in supervised machine learning, refers to whether the labels of a data set can be captured by the simplest possible machine: a linear classifier. In order to quantify linear separability beyond this single bit of information, one needs models of data structure parameterized by…
Romain D. Cazé
Multiple studies show how dendrites might extend some neurons’ computational capacity. These studies leave a large fraction of the nervous system unexplored. Here we demonstrate how a modest dendritic tree can allow cerebellar granule cells to implement linearly non-separable computations. Granule cells’ dendrites do…
Katarzyna Jaworska, Nicola J. van Rijsbergen, Robin A.A. Ince, Philippe G. Schyns
A key challenge in systems neuroscience remains to understand where, when and now particularly how brain networks compute over sensory inputs to achieve behavior. We used XOR, OR and AND functions as behavioral tasks, because each requires a different computation over the same inputs to produce correct outputs. In each…
Romain Daniel Cazé, Mark Humphries, Boris Gutkin, Olaf Sporns
Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other…
Romain D. Cazé
Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the…
Gabriele Di Antonio, Sofia Raglio, Maurizio Mattia
A general mathematical description of the way the brain encodes ordinal knowledge of sequences is still lacking. Coherently with the well-established idea of mixed selectivity in high-dimensional state spaces, we conjectured the existence of a linear solution for serial learning tasks. In this theoretical framework…
Romain D. Cazé, Alexandra Tran-Van-Minh, Boris S. Gutkin, David A. DiGregorio
Theory predicts that nonlinear summation of synaptic potentials within dendrites allows neurons to perform linearly non-separable computations (LNSCs). Using Boolean analysis approaches, we predicted that both supralinear and sublinear synaptic summation could allow single neurons to implement a type of LNSC, the…
Heng Ma, Longsheng Jiang, Tao Liu, Jia Liu
Classification constitutes a core cognitive challenge for both biological and artificial intelligence systems, with many tasks potentially reducible to classification problems. Here we investigated how the brain categorizes stimuli that are not linearly separable in the physical world by analyzing the geometry of…
Maximilian S. Ernst, Aaron Peikert, Andreas M. Brandmaier, Yves Rosseel
'Yves Rosseel'] We show that separable nonlinear least squares (SNLLS) estimation is applicable to all linear structural equation models (SEMs) that can be specified in RAM notation. SNLLS is an estimation technique that has successfully been applied to a wide range of models, for example neural networks and dynamic…
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We present a fast, asymptotically linear-scaling implementation of the perturbative quadruples energy correction in coupled-cluster theory using local natural orbitals. Our work follows the domain-based local pair natural orbital (DLPNO) approach previously applied to lower levels of excitations in coupled-cluster…
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Deciphering the correct mechanism governing certain phenomenon in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic flow (in the…
Alexander S. Kulikov, Ivan Mikhailin, Andrey Mokhov, Vladimir V. Podolskii
'Vladimir V. Podolskii'] As we observe in this paper, this problem contains as a special case the well-known range queries problem and has a rich variety of applications in such areas as graph algorithms, functional programming, circuit complexity, and others. It is easy to compute Ax using O(u) semigroup operations.…
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Modeling multimetallic systems efficiently enables faster prediction of desirable chemical properties and design of new materials. This work describes an initial implementation for performing multireference wave function method localized active space self-consistent field (LASSCF) calculations through the use of…