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An adaptive Mirror-Prox method for variational inequalities with singular operators
Kimon Antonakopoulos, Veronica Belmega, Panayotis Mertikopoulos
One of the main reasons for their extensive applicability is that they comprise a flexible optimization framework which can simultaneously account for loss function minimization, saddle-point, game-theoretic, and fixed point problems.
Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams
The key ingredient to MixTURE's success is automatically learning
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros
Contemporary sensorimotor learning approaches typically start with anexisting complexagent (e.g., arobotic arm), which theylearn tocontrol. Limbs pass messages to their neighbors in this graph in order to coordinate behavior.
SyncVIS: Synchronized Video Instance Segmentation
Also, we employ a synchronized embedding optimization strategy'Sync.
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Patrick Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh
Neural Information Processing Systems http://nips.cc/
Fine-grained Optimization of Deep Neural Networks
Neural Information Processing Systems http://nips.cc/
Metric Learning for Adversarial Robustness
Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray
Deep networks achieve impressive accuracy and wide adoption in computer vision [17], speech recognition [14], and natural language processing [21].