These works attempt to leverage the memorization effect of DNNs [25] to gradually distinguish the noisy image-text pairs for robust learning in a co-teaching manner.

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

Real-world robotics tasks commonly manifest ascontrol problems overcontinuous action spaces. When learning to act in such settings, control policies are typically represented as continuous probability distributions that cover all feasible control inputs - often Gaussians. The underlying assumption is that this enables more refined decisions compared to crude policy choices such as discretized controllers, which limit the search space but induce abrupt changes. While switching controls canbeundesirable inpractice astheymaychallenge stability andaccelerate system weardown, they are theoretically feasible and even arise as optimal strategies in some settings.

Conditional image synthesis aims tocreate an image according tothe given control signals.

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.

Inprinciple, onecandesign Lipschitz constrained architectures using the composition property of Lipschitz functions, but Anil et al.[2] recently identified a key obstacle to this approach: gradient norm attenuation.

In these diverse real-world applications, there is often a degree of uncertainty in the input which has led to the study of stochastic matching models.