Robotics,autonomousdriving,augmentedreality,andmanyembodiedcomputer vision applications must quickly react to user-defined events unfolding in real time.

We show that agents with HCAM substantially outperform agents with other memory architectures attasks requiring long-term recall, retention, orreasoning over memory.

Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.

Neural Information Processing Systems http://nips.cc/

As inthe classical problem, weights are fixed by an adversary and elements appear in random order. In contrast to previous variants of predictions, our algorithm only has access toamuch weakerpiece ofinformation: anadditive gapc.