Generalization bounds via distillation
WebNon-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis ... Moment-based Uniform Deviation Bounds for -means and ... Advances in Neural … WebJun 26, 2024 · Norm based measures do not explicitly depend on the amount of parameters in the model and therefore have a better potential to represent its capacity [14]: norm-based measures can explain the generalization of Deep Neural Networks (DNNs), as the complexity of models trained on the random labels is always higher than the complexity …
Generalization bounds via distillation
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WebTitle: Generalization bounds via distillation; Authors: Daniel Hsu and Ziwei Ji and Matus Telgarsky and Lan Wang; Abstract summary: Given a high-complexity network with poor … WebThis paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network …
WebGeneralization bounds via distillation. This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization … WebMay 17, 2024 · In this paper, we address the model compression problem when no real data is available, e.g., when data is private. To this end, we propose Dream Distillation, a …
Webbounds and algorithm-dependent uniform stability bounds. 4. New generalization bounds for specific learning applications. In section5(see also Ap-pendixG), we illustrate the … WebGeneralization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic Atsushi Suzuki, Atsushi Nitanda, jing wang, Linchuan Xu, ... MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps Awais Muhammad, Fengwei Zhou, Chuanlong Xie, Jiawei Li, ...
WebGeneralization bounds via distillation Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang. In Ninth International Conference on Learning Representations, 2024. [ external link bibtex ] On the proliferation of support vectors in high dimensions Daniel Hsu, Vidya Muthukumar, Ji … tracfone reviewer blogspotWebJun 15, 2024 · These yield generalization bounds via a simple compression-based framework introduced here. ... Z. Ji, M. Telgarsky, and L. Wang. Generalization bounds … tracfone reviewer 2021WebMar 5, 2024 · Abstract:This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one … tracfone reviewer 2022WebMar 9, 2024 · This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network with nearly identical predictions but low complexity and vastly smaller generalization limits, as well as a variety of experiments demonstrating similar … tracfone retailers perry hall mdWebFor details and a discussion of margin histograms, see Section 2. - "Generalization bounds via distillation" Figure 2: Performance of stable rank bound (cf. Theorem 1.4). Figure 2a compares Theorem 1.4 to Lemma 3.1 and the VC bound (Bartlett et al., 2024b), and Figure 2b normalizes the margin histogram by Theorem 1.4, showing an unfortunate ... thermtrol tuyen dungWebApr 12, 2024 · This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a … tracfone reviewer 2020WebDomain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domain ensemble distillation (XDED), that learns domain-invariant features while encouraging the model to converge to flat minima, which recently turned out to be a … tracfone reviewer august 2022