They mimicked the scenario during meta-testing by sampling a sequence of incremental tasks from base classes. Furthermore, they proposed a bi-directional guided modulation based on meta-learning to automatically adapt to new knowledge. Drawing on metric learning within the context of meta-learning,...
Recurrent Neural Network (RNN) as being unidirectional and bi-directional, 5. Generative Adversarial Network (GAN). In the next section, a brief description of different deep learning models will be presented listing some of their application areas in PHM Society. 4.1.1 Auto-encoder and stacked ...
DiffSharp - An automatic differentiation (AD) library providing exact and efficient derivatives (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) for machine learning and optimization applications. Operations can be nested to any level, meanin...
The straight-up *OH adsorption configuration is less favorable than the tilted ones on transition metals because of the directional 1π-orbital interactions with metal d-states. In this study, we did not include other local minima of tilted *OH adsorption configurations. In the feature ...
2020-NIPS-Pruning neural networks without any data by iteratively conserving synaptic flow 2020-NIPS-Neuron-level Structured Pruning using Polarization Regularizer 2020-NIPS-SCOP: Scientific Control for Reliable Neural Network Pruning 2020-NIPS-Directional Pruning of Deep Neural Networks 2020-NIPS-Storage ...
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,...
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...
there are, the more mountains and valleys there are, which makes it very easy for the gradient descent method to fall into a small local valley and stop searching. This is the most common local optimal problem in solving multi-dimensional optimization problems by conventional gradient descent meth...
where the operators ∂/∂x and ∂/∂z refer to directional intensity variations in the x and z directions. Using these loss functions, the losses of the generator and the discriminator are defined as follows: $${{Loss}}_{G}={\lambda }_{1}L1+\,{\lambda }_{2}L2+\,{\lambda...
The most related work (Li and Guo 2014), which proposed a bi-directional representation model for multi-label classification, in which the mid-level representation layer is constructed from both input and output spaces. In essence, their network structure is different from ours. Their framework ...