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Learning Layer-wise Equivariances Automatically using Gradients Tycho F.A. van der Ouderaa
First, it requires efficient and flexible parameterisations of layer-wise equivari-ances.
Deep Structured Prediction for Facial Landmark Detection
Neural Information Processing Systems http://nips.cc/
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity
This is, e.g., the typical setting of many ERM
Maximum-Entropy Fine Grained Classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik
Neural Information Processing Systems http://nips.cc/
Near-OptimalRegretforAdversarialMDPwith DelayedBanditFeedback
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately. However, in practice feedback is often observedindelay.
Near-OptimalRegretforAdversarialMDPwith DelayedBanditFeedback
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately. However, in practice feedback is often observedindelay.
Harnessing small projectors and multiple views for efficient vision pretraining
We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute.