We investigate a sequential optimization procedure to minimize the empiricalrisk functional fbθ(x) = 12kGbθ(x) yk2 for certain families of deep networks Gθ(x).

We find that the inconsistency between training and inference of BN is the leading cause that results in the failure of BN in NLP. We define Training Inference Discrepancy (TID) to quantitatively measure this inconsistencyand reveal that TID can indicate BN'sperformance, supported by extensiveexperiments,includingimageclassification,neuralmachinetranslation, language modeling, sequence labeling, andtextclassification tasks.

Previous ablation studies showedthatadding WD to normalized nets indeed leads to better generalization [126, 72, 125].

GMED-editedexamplesremain similar to their unedited forms, but can yield increased loss in the upcoming model updates, thereby making thefuture replays more effectiveinovercoming catastrophic forgetting.

The promise of mobile health (mHealth) is the ability to use wearable sensors to monitor participant physiology athigh frequencies during daily life to enable temporally-precise health interventions.

Mitigating these biases relies on exposing and removingnuisance components.

Toourknowledge, ADASPIDER isthefirstparameterfree non-convex variance-reduction method in the sense that it does not require the knowledge of problem-dependent parameters, such as smoothness constant L,targetaccuracyϵoranybound ongradient norms.

We show it depends on the precise way in which the limit is taken, and in particular on how the quantityofdata,thehiddenlayerwidth,&thelearningratescalesasd .

Forsuch models, one particularly elegant approach is that ofpassive-aggressive learning[3]. In this framework, a model isonly updated when itfails to classify an example correctly with high confidence.

Several approaches to predictive uncertainty quantification have been introduced in recent years, considering uncertainty inaBayesian sense [2,7,18]aswell asfrom afrequentist'spoint ofview [15,5,34].