Daniel S. Brown

TL;DR: In contrastive learning as mentioned in this paper , the goal is to identify and isolate the causal features that people actually care about and use when they represent states and behaviors, and to learn representations that are more generalizable than self-supervised and task-input alternatives.

TL;DR: Monte Carlo augmented Actor-Critic (MCAC), a parameter free modification to standard actor-critic algorithms which initializes the replay buffer with demonstrations and computes a modi fied Q -value by taking the maximum of the standard temporal distance target and a Monte Carlo estimate of the reward-to-go.

TL;DR: Evidence that learning rewards from pairwise trajectory preferences is highly sensitive and non-robust to spurious features and increasing model capacity, but not as sensitive to the type of training data is presented.

TL;DR: In this paper, an abatement system may include an exhaust conduit to flow an effluent stream therethrough, a plurality of packed beds disposed in the exhaust conduit, one or more spray jets to provide effluent treating agent between adjacent packed beds, and a dripper disposed above an uppermost packed bed to provide the treating agent in large droplets to wet and rinse particulate from an upper surface of the uppermost packing bed substantially without forming fine droplets.