Invideo recognition, weneedtosample multiple frames torepresent eachvideo which makesthe computational cost scale proportionally to the number of sampled frames. In most cases, a small proportion of all the frames is sampled for each input, which only contains limited information of the original video.

We showthesebycounterexamples.4 Suppose α = 1 and in particular we assumeα = 2.

Therefore, forasource task withacomplexmodel orfewtraining samples, even though itis similar to the target task, the knowledge transferable from this source task can still be verylimited.

Our algorithm is based on the semidefinite program (SDP) relaxation of the non-convexl1-regularized PCA problem.

Loss functions play an important role in training deep-network-based object detectors.

Bayesian predictive intervals are conditioned on the specific observed sequenceZ1:n and make statements on the next value[Yn+1 | Xn+1]. Subjective Bayesian statements on predictions are non-refutable, and are in this sense unscientific, but are optimal according to decision theoretic foundations. However,tomakesuch strong statements, the Bayesian must usually make the strict assumption of the model being well-specified. Asmentionedearlier,computingtheAOI interval is an efficient matrix-vector multiplication, whereas the LOO interval requires expensive broadcastingtoconstructthe ngrid T nISweightarray. We use the same Bayesian model as in (10), again consideringc=1,0.02.

We introduce a CISS framework that alleviates the forgetting problem and facilitates learning novel classes effectively. We have found that a logit can be decomposed into two terms. They quantify how likely an input belongs toaparticular class ornot, providing aclue forareasoning process ofa model. The KD technique, in this context, preserves the sum of two terms (i.e., a class logit), suggesting that each could be changed and thus the KD does not imitate thereasoning process.

Thewidely-used convolutional neural networkandtransformer treatthe image as a grid or sequence structure, which is not flexible to capture irregular andcomplexobjects.

Previous works have validated that text generation APIs can be stolen through imitation attacks, causing IP violations. In order to protect the IP of text generationAPIs,recentworkhasintroduced awatermarking algorithm andutilized the null-hypothesis test as a post-hoc ownership verification on the imitation models.

In this paper, we set out to provide insights and answers to theseandadditionalquestions.