Education
Regularizing Towards Permutation Invariance in Recurrent Models
In many machine learning problems the output should not depend on the order of the input. Such "permutation invariant" functions have been studied extensively recently. Here we argue that temporal architectures such as RNNs are highly relevant for such problems, despite the inherent dependence of RNNs on order. We show that RNNs can be regularized towards permutation invariance, and that this can result in compact models, as compared to non-recurrent architectures. We implement this idea via a novel form of stochastic regularization. Existing solutions mostly suggest restricting the learning problem to hypothesis classes which are permutation invariant by design [Zaheer et al., 2017, Lee et al., 2019, Murphy et al., 2018]. Our approach of enforcing permutation invariance via regularization gives rise to models which are semi permutation invariant (e.g.
Supplementary Materials: Asynchronous Decentralized Online Learning
Now we can formally analyze the feasibility of AD-OGP .
Asynchronous Decentralized Online Learning Jiyan Jiang Tsinghua University
Most existing algorithms in decentralized online learning are conducted in the synchronous setting.
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning Zhenhuan Yang 1 Y unwen Lei 2 Puyu Wang 3 Tianbao Yang
Pairwise learning refers to learning tasks where the loss function depends on a pair of instances.
Explicable Reward Design for Reinforcement Learning Agents
A reward function plays the central role during the learning/training process of a reinforcement learning (RL) agent. Given a "task" the agent is expected to perform (i.e., the desired learning outcome), there are typically many different reward specifications under which an optimal policy
Explicable Reward Design for Reinforcement Learning Agents
A reward function plays the central role during the learning/training process of a reinforcement learning (RL) agent. Given a "task" the agent is expected to perform (i.e., the desired learning outcome), there are typically many different reward specifications under which an optimal policy
Supplementary Material for Towards Efficient 3D Object Detection with Knowledge Distillation Jihan Y ang
In this section, we provide some additional experimental results as a supplement to our main paper.
Towards Efficient 3D Object Detection with Knowledge Distillation Jihan Y ang
Then, we build a benchmark to assess existing KD methods developed in the 2D domain for 3D object detection upon six well-constructed teacher-student pairs.
The MAGICAL Benchmark for Robust Imitation
The robot could learn from these demonstrations to complete the tasks autonomously. For IL algorithms to be useful, however, they must be able to learn how to perform tasks from few demonstrations. A domestic robot wouldn't be very helpful if it required thirty demonstrations before it figured out that you are deliberately washing your purple cravat
