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Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations
Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik, Daniela Rus
Neural Information Processing Systems http://nips.cc/
cf8353a0efc3644454a5591a7cb55c17-Paper-Conference.pdf
Require:DatasetS = ((x1,y1),..., (xm,ym)) (X { 1}) Afunctionh in HNN can asacascade 2, left column).
Retrieval
Late interaction methods compute representations for the query and corpus graphs separately, and compare these representations using simple similarity functions at the last stage, leading to highly scalable systems. Early interaction methods combine information from both graphs right from the input stages, are usually considerablymoreaccurate,butslower.
Batched Thompson Sampling
O (log log(T)) expected batch complexity. This is achieved through a dynamic batching strategy, which uses the agents estimates to adaptively increase the batch duration.
565f995643da6329cec701f26f8579f5-Paper-Conference.pdf
FL framework, namely FedICON, to capture invariant knowledge among heterogeneous clients and consistently tune the model to adapt to test data.
