Education
Making Scalable Meta Learning Practical
Meta learning aims to learn the inductive biases ( e .
Optimal Stochastic and Online Learning with Individual Iterates
Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou
Neural Information Processing Systems http://nips.cc/
Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction Manuel Brenner
Dynamical systems (DS) theory is fundamental for many areas of science and engineering. It can provide deep insights into the behavior of systems evolving in time, as typically described by differential or recursive equations.
Small batch deep reinforcement learning
Contrastingly, others have observed that larger batch sizes tend to converge to "sharper" optimization
Small batch deep reinforcement learning
Since the policy used to collect transitions is changing throughout learning, the replay memory contains data coming from a mixture of policies (that differ from the agent's current policy), and