Then, responses of unlabeled instances are predicted with the inferred representations using a prediction network.

For example, the autonomous driving systems are usually equipped with multiple sensors to collect information from different perspectives [2]. In medical diagnosis [3], multimodal data usually come from different types of examinations, typically including a variety of clinical data.

Furthermore, sinceft is L-Lipschitz, w.r.t.

We provide a technique for online convex optimization that obtains regret G w Tlog( w G T)+ w 2 +G2 on G-Lipschitz losses for any comparison pointw without knowing eitherG or w .

Lifelong learners must recognize concept vocabularies that evolveover time.

We achieve that by maintaining a replay buffer for each environment the agent interacts with.

Since t and s are Gaussian n isthe replacedwithq.

Thebottom-up-and-top-down ConvRNN architecture is related to the standard U-Net structure (e.g.

Bound to each node is a vector of latent attributes that intuitively represent object properties such as surface shape and texture.