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searching for Stochastic gradient descent 11 found (125 total)

alternate case: stochastic gradient descent

Martín Abadi (444 words) [view diff] exact match in snippet view article find links to article

has contributed to the development of differentially private stochastic gradient descent.[1] He is a 2008 Fellow of the Association for Computing Machinery

Hinge loss (995 words) [view diff] exact match in snippet view article find links to article

in Preference Handling. Zhang, Tong (2004). Solving large scale linear prediction problems using stochastic gradient descent algorithms (PDF). ICML.

Huber loss (1,037 words) [view diff] exact match in snippet view article find links to article

(2004). Solving large scale linear prediction problems using stochastic gradient descent algorithms. ICML. Friedman, J. H. (2001). "Greedy Function Approximation:

Michèle Sebag (357 words) [view diff] exact match in snippet view article find links to article

Bottou, and Patrick Gallinari. "SGD-QN: Careful quasi-Newton stochastic gradient descent." Journal of Machine Learning Research 10.Jul (2009): 1737–1754

Computer-generated holography (2,595 words) [view diff] exact match in snippet view article find links to article

optimisation algorithms such as direct search, simulated annealing or stochastic gradient descent using, for example, TensorFlow. The third (technical) issue is

Stability (learning theory) (2,656 words) [view diff] exact match in snippet view article

Yoram Singer, Train faster, generalize better: Stability of stochastic gradient descent, ICML 2016. Elisseeff, A. A study about algorithmic stability

Diffusion model (10,605 words) [view diff] exact match in snippet view article find links to article

q(x_{1:T}|x_{0})]} and now the goal is to minimize the loss by stochastic gradient descent. The expression may be simplified to L ( θ ) = ∑ t = 1 T E x

Non-negative matrix factorization (7,781 words) [view diff] exact match in snippet view article find links to article

Sismanis (2011). Large-scale matrix factorization with distributed stochastic gradient descent. Proc. ACM SIGKDD Int'l Conf. on Knowledge discovery and data

Neural radiance field (2,614 words) [view diff] exact match in snippet view article find links to article

color, and opacity. The gaussians are directly optimized through stochastic gradient descent to match the input image. This saves computation by removing

Edward Y. Chang (2,492 words) [view diff] case mismatch in snippet view article find links to article

2010.88. PMID 20421667. S2CID 6703419. "SpeeDO: Parallelizing Stochastic Gradient Descent for Deep Convolutional Neural Network" (PDF). Chang, Edward Y

University of Illinois Center for Supercomputing Research and Development (6,992 words) [view diff] exact match in snippet view article find links to article

properties of neural networks which are typically trained using stochastic gradient descent and its variants. They observed that neurons saturate when network