Message-Passing Neural Networks Learn Little’s Law
نویسندگان
چکیده
منابع مشابه
Mapping Neural Networks onto Message-Passing Multicomputers
This paper investigates the architectural requirements for simulating neural networks using massively parallel multiprocessors. First, we model the connectiv-ity patterns in large neural networks. A distributed processor/memory organization is developed for eeciently simulating asynchronous, value-passing connection-ist models. Based on the network connectivity and mapping policy, we estimate t...
متن کاملMapping Neural Networks onto Message - Passing
This paper investigates the architectural requirements for simulating neural networks using massively parallel multiprocessors. First, we model the connectivity patterns in large neural networks. A distributed processor/memory organization is developed for efficiently simulating asynchronous, value-passing connectionist models. On the basis of the network connectivity and mapping policy, we est...
متن کاملMeasuring Neural Synchrony by Message Passing
A novel approach to measure the interdependence of two time series is proposed, referred to as “stochastic event synchrony” (SES); it quantifies the alignment of two point processes by means of the following parameters: time delay, variance of the timing jitter, fraction of “spurious” events, and average similarity of events. SES may be applied to generic one-dimensional and multi-dimensional p...
متن کاملNeural Reconstruction with Approximate Message Passing (NeuRAMP)
Many functional descriptions of spiking neurons assume a cascade structure where inputs are passed through an initial linear filtering stage that produces a lowdimensional signal that drives subsequent nonlinear stages. This paper presents a novel and systematic parameter estimation procedure for such models and applies the method to two neural estimation problems: (i) compressed-sensing based ...
متن کاملNeural Message Passing for Quantum Chemistry
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Communications Letters
سال: 2019
ISSN: 1089-7798,1558-2558,2373-7891
DOI: 10.1109/lcomm.2018.2886259