gradient methodsdescent methodsfunctional minimizationIn mostdirectional algorithms for optimization of a mathematical function, thestep along each direction is ... W.,C.,TurnerP.,... - 《Journal of Optimization Theory & Applications》 被引量: 4发表: 1975年 RATE-CONSTRAINED WAVELET MULTIRESOLUTION-...
In this case, SGD and GD are known to converge to the unique minimum-norm solution; however, with the moderate and annealing learning rate, we show that they exhibit different directional bias: SGD converges along the large eigenvalue directions of the data matrix, while GD goes after the ...
不过,如果任务字典里面包括不相干的任务,MTL的联合学习会带来negative transfer。为此不少方法是想寻找一个MTL的平衡点,比如Uncertainty Weighting、Gradient normalization、Dynamic Weight Averaging (DWA) 、Dynamic task prioritization、multiple gradient descent algorithm (MGDA) 和adversarial training等。另外一些最近的工...
add to our state-of-the-art deep learning toolkit by delving further into essential theory, specifically: weight initialization uniform normal Xavier Glorot stochastic gradient descent learning rate batch size second-order gradient learning momentum Adam unstable gradients vanishing exploding avoiding...
undirectional graphic model markov network gibbs&boltzman machine conditional random field following above, naive bayesian(if independent) ---> linear regression ---> logistic regression ---> bayesian network(HMM/linear CRF) ---> RNN --->seq2seq---> attention ---> transformer ---> BERT/GP...
201711 ICLR-18 GENERALIZING ACROSS DOMAINS VIA CROSS-GRADIENT TRAINING 不同于以往的工作,本文运用贝叶斯网络建模label和domain的依赖关系,抓住training、inference 两个过程,有效引入domain perturbation来实现domain adaptation。 ICLR-18 generalizing across domains via cross-gradient training 20181106 PRCV-...
1. Restricted Boltzmann Machine RBM and its variants,2Auto-encoder (AE) and stacked AE (SAE),3.Convolutional Neural Network(CNN),4.Recurrent Neural Network(RNN) as being unidirectional and bi-directional,5. Generative Adversarial Network (GAN). ...
Bi-directional electrodes have emerged as a solution for this problem, as they enable concurrent, real-time sensing and stimulation. Early research in the development of such electrodes involved determining optimal designs for filtering stimulation-mediated electrical noise119. While this work continues,...
andparametersby gradient descent get updated in each iteration. But hyperparameters can also be part of the learning process. Like inSAC(Soft-Actor Critic method Version-3), the entropy factor α is learnt from the data by specifying an objective function to tune it. This is calledend-to-...
However, I was confused about gradient everytime when I start to think about the details in graident descent. Why the gradient is the steepest direction of the function space? Why not other directional vectors? Now, I figure it out. As we all know that every space in the university can ...