Li Fei-Fei

TL;DR: This paper proposes Implicit Reinforcement without Interaction at Scale (IRIS), a novel framework for learning from large-scale demonstration datasets that factorizes the control problem into a goal-conditioned low-level controller that imitates short demonstration sequences and a high-level goal selection mechanism that sets goals for the low- level and selectively combines parts of suboptimal solutions leading to more successful task completions.

TL;DR: This work proposes an LSTM model which can learn general human movement and predict their future trajectories, and introduces a new characterization that describes the “social sensitivity” at which two targets interact.

TL;DR: The experiments show that the proposed latent space planning is able to learn plannable semantic representations without explicit supervision, which enables sequential reasoning on real-world videos and leads to stronger generalization compared to existing planning approaches and neural network policies.

TL;DR: It is shown that it is possible to amortize delays and achieve global convergence with probability 1, even under polynomially growing delays, reaffirming the successful application of DASGD to large-scale optimization problems.

TL;DR: The authors proposed an iterative model that localizes the two entities in the referring relationship, conditioned on one another, and formulated the cyclic condition between the entities in a relationship by modelling predicates that connect the entities as shifts in attention from one entity to another.