After calibrating in the first session, the slow efficient tuning parameters can capture more informativefeatures, improving generalization to incoming classes. Moreover, to further incorporate novel concepts, we strikeabalance between stability and plasticity byfixing slowefficient tuning parameters and continuously updating the fast ones. Specifically, a cross-classification loss with feature alignment is proposed to circumvent catastrophic forgetting.

DNN pruning is an approach for deep model compression, which aimsateliminating someparameters withtolerable performance degradation.

Conversely, adaptive first order methods are very popular in Machine Learning, with AdaGrad, [12],beingthemostprominent methodamongthisclass. AdaGrad isanonlinelearning algorithm which adapts its learning rate using the feedback (gradients) received through the optimization process, and is known to successfully handle noisy feedback.

By this way,YoutubeDNN can be compatible with non-sequential recommendation task.SCANN and IPNSW are built on the learned representation of YoutubeDNN.

However, within this specific application domain, existing VAE methods are restricted by using only one layer of latent variables andstrictly Gaussian posterior approximations.

Thiscanbe formulated into anon-linear equality constrained optimization problem. Unlike GANs, solving such problem iscomputationally challenging, wethen proposed a simple yet effective procedure to decouple the alternating updates for the two networks for stability. By teaching the perturbation generator to hijacking the training trajectory of the victim classifier, the generator can thus learn to move against thevictim classifier stepbystep.

However, the structure of their hidden layer representations is only theoretically well-understood incertain infinite-width limits, inwhichtheserepresentations cannot flexibly adapt tolearn data-dependent features [3-11,24]. Inthe Bayesian setting, these representations are described by fixed, deterministic kernels [3-11].

However, overparameterization also leads to excessive computational and memory requirements.

The result is aO(D T) regret bound that holds uniformly for all comparatorsu X.

Different from the original MoCo [19] and its cross-modal versions [15, 33, 35] that utilize momentum update for only momentum encoders to maintain a large consistent queue, our BMU strategy imposes momentum update on both momentum encoders and (video/text) encoders.