盟军
发表于 2025-3-28 14:49:52
,FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks,cal clients, postpruning without rewinding, and aggregation of LTNs using server momentum ensures that our approach significantly outperforms existing state-of-the-art solutions. Experiments on CIFAR-10 and Tiny ImageNet datasets show the efficacy of our approach in learning personalized models while significantly reducing communication costs.
信徒
发表于 2025-3-28 19:23:46
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Expiration
发表于 2025-3-29 00:51:21
,Explicit Model Size Control and Relaxation via Smooth Regularization for Mixed-Precision Quantizatiefore, usually leads to an accuracy drop. One of the possible ways to overcome this issue is to use different quantization bit-widths for different layers. The main challenge of the mixed-precision approach is to define the bit-widths for each layer, while staying under memory and latency requiremen
Emmenagogue
发表于 2025-3-29 03:03:53
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吝啬性
发表于 2025-3-29 10:30:53
You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Roundinires a large number of random numbers generated on the fly. This is not a trivial task on the hardware platforms such as FPGA and ASIC. The widely used solution is to introduce random number generators with extra hardware costs. In this paper, we innovatively propose to employ the stochastic propert
沐浴
发表于 2025-3-29 12:15:07
,Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks,cs. The integration of storage and computation paradigm on neuromorphic hardwares makes SNNs much different from Deep Neural Networks (DNNs). In this paper, we argue that SNNs may not benefit from the weight-sharing mechanism, which can effectively reduce parameters and improve inference efficiency
VALID
发表于 2025-3-29 18:21:50
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ensemble
发表于 2025-3-29 20:33:16
,Theoretical Understanding of the Information Flow on Continual Learning Performance,pite the numerous previous approaches to CL, most of them still suffer forgetting, expensive memory cost, or lack sufficient theoretical understanding. While different CL training regimes have been extensively studied empirically, insufficient attention has been paid to the underlying theory. In thi
湿润
发表于 2025-3-30 00:51:23
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亚麻制品
发表于 2025-3-30 05:29:35
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