Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.

Understanding Neural networks ingeneral, and howtotrain Recurrent Neural Networks (RNNs) in particular,remains acentral question in modern machine learning.

We also establish new convergence complexities to achieve an approximate KKT solution when the objective can be smooth/nonsmooth, deterministic/stochastic and convex/nonconvex with complexity that is on a par with gradient descent for unconstrained optimization problems in respective cases. To the best of our knowledge, this is the first study of the first-order methods with complexity guarantee for nonconvex sparse-constrained problems.

To do so, we extend to neural activity the maximum occupancy principle (MOP) developed for behavior, and refer to this new neural principle asNeuroMOP.NeuroMOP posits thatthegoal ofthenervoussystem istomaximize future action-state entropy, a reward-free, intrinsic motivation that entails creating allpossible activity patterns while avoiding terminal ordangerous ones.

Modeling multi-agent systems requires understanding howagents interact. Such systems are often difficult to model because they can involve a variety of types ofinteractions that layer together todriverich social behavioral dynamics.