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Meta-learning sparse implicit neural representations
Implicit neural representations are a promising new avenue of representing general signals
by learning a continuous function that, parameterized as a neural network, maps the domain …
by learning a continuous function that, parameterized as a neural network, maps the domain …
Pruning via iterative ranking of sensitivity statistics
With the introduction of SNIP [arxiv: 1810.02340 v2], it has been demonstrated that modern
neural networks can effectively be pruned before training. Yet, its sensitivity criterion has …
neural networks can effectively be pruned before training. Yet, its sensitivity criterion has …
Scheduling hyperparameters to improve generalization: From centralized sgd to asynchronous sgd
This article studies how to schedule hyperparameters to improve generalization of both
centralized single-machine stochastic gradient descent (SGD) and distributed asynchronous …
centralized single-machine stochastic gradient descent (SGD) and distributed asynchronous …
Critical parameters for scalable distributed learning with large batches and asynchronous updates
It has been experimentally observed that the efficiency of distributed training with stochastic
gradient (SGD) depends decisively on the batch size and—in asynchronous …
gradient (SGD) depends decisively on the batch size and—in asynchronous …
Performance of physical-informed neural network (PINN) for the key parameter inference in Langmuir turbulence parameterization scheme
F **u, Z Deng - Acta Oceanologica Sinica, 2024 - Springer
The Stokes production coefficient (E 6) constitutes a critical parameter within the Mellor-
Yamada type (MY-type) Langmuir turbulence (LT) parameterization schemes, significantly …
Yamada type (MY-type) Langmuir turbulence (LT) parameterization schemes, significantly …
Эмоциональный анализ данных социальных сетей с использованием кластерной вероятностной нейронной сети с параллелизмом данных
СД Старлин, НИ Ченталир - Научно-технический вестник …, 2024 - ntv.elpub.ru
Аннотация Социальные сети содержат огромное количество данных, которые
используются различными организациями для изучения эмоций, мыслей и мнений …
используются различными организациями для изучения эмоций, мыслей и мнений …
Efficient DNN training based on backpropagation parallelization
D **ao, C Yang, W Wu - Computing, 2022 - Springer
Pipeline parallelism is an efficient way to speed up the training of deep neural networks
(DNNs) by partitioning the model and pipelining the training process across a cluster of …
(DNNs) by partitioning the model and pipelining the training process across a cluster of …
Understanding the impact of data parallelism on neural network classification
S Starlin **i, DN Chenthalir Indra - Optical Memory and Neural Networks, 2022 - Springer
Social Networks have become a platform to express each moment of a person via texts in
widespread. With the help of a lot of words, ideas, thoughts and good memories are shared …
widespread. With the help of a lot of words, ideas, thoughts and good memories are shared …
A chaos theory approach to understand neural network optimization
Despite the complicated structure of modern deep neural network architectures, they are still
optimized with algorithms based on Stochastic Gradient Descent (SGD). However, the …
optimized with algorithms based on Stochastic Gradient Descent (SGD). However, the …
Federated Learning Optimization Algorithm Based on Dynamic Client Scale
L Wang, W Feng, R Luo - … Networking for Quality, Reliability, Security and …, 2023 - Springer
Federated learning methods typically learn models from the local iterative updates of a large
number of clients. The interest in the impact of client quantity on the training dynamics of …
number of clients. The interest in the impact of client quantity on the training dynamics of …