Results 1 to 10 of about 5,601,682 (347)
Ecological Monographs, 2018 In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better ...
Kamil Barton +2 more
exaly +2 more sources
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time [PDF]
International Conference on Machine Learning, 2022 The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the
Mitchell Wortsman +10 more
semanticscholar +1 more source
Road foundation thawing at change in Ice content along the active layer depth [PDF]
E3S Web of Conferences, 2023 The seasonal temperature and humidity regime of the road foundation in the active layer of soil largely determines the reliability and safety of the automobile road use.
Galkin A.F. +3 more
doaj +1 more source
Stochastic Controlled Averaging for Federated Learning with Communication Compression [PDF]
International Conference on Learning Representations, 2023 Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potential of alleviating its communication overhead.
Xinmeng Huang, Ping Li, Xiaoyun Li
semanticscholar +1 more source
Merging Models with Fisher-Weighted Averaging [PDF]
Neural Information Processing Systems, 2021 Averaging the parameters of models that have the same architecture and initialization can provide a means of combining their respective capabilities. In this paper, we take the perspective that this"merging"operation can be seen as choosing parameters ...
Michael Matena, Colin Raffel
semanticscholar +1 more source
Decentralized Federated Averaging [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Federated averaging (FedAvg) is a communication-efficient algorithm for distributed training with an enormous number of clients. In FedAvg, clients keep their data locally for privacy protection; a central parameter server is used to communicate between ...
Tao Sun, Dong-sheng Li, Bao Wang
semanticscholar +1 more source
Nonlinear Analysis, 2023 We consider an optimal control problem for a differential inclusion of the Carathéodory type affine with respect to the control with a coercive cost functional on a semiaxis and with fast oscillating time-dependent coefficients.
Sergey Dashkovskiy +3 more
doaj +1 more source
Diverse Weight Averaging for Out-of-Distribution Generalization [PDF]
Neural Information Processing Systems, 2022 Standard neural networks struggle to generalize under distribution shifts in computer vision. Fortunately, combining multiple networks can consistently improve out-of-distribution generalization. In particular, weight averaging (WA) strategies were shown
Alexandre Ramé +5 more
semanticscholar +1 more source
Federated Learning with Fair Averaging [PDF]
International Joint Conference on Artificial Intelligence, 2021 Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the magnitudes. To address this issue, we propose the federated fair averaging
Z. Wang +5 more
semanticscholar +1 more source
Substantiation of asymptotical solution of weakly nonlinear hyperbolic system
Lietuvos Matematikos Rinkinys, 2023 A hyperbolic system of first order partial differential equations with small parameter and periodical initial conditions is obtained. The uniformly valid in a long time interval asymptotical solution can be constructed using the averaging along ...
Aleksandras Krylovas
doaj +1 more source