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Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov +5
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do t…
A Statistical Approach to Assessing Neural Network Robustness
Stefan Webb, Tom Rainforth, Yee Whye Teh +1
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate th…
On Exploration, Exploitation and Learning in Adaptive Importance Sampling
Xiaoyu Lu, Tom Rainforth, Yuan Zhou +2
We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation. Bor…
Do Deep Generative Models Know What They Don't Know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh +2
A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work h…
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Juho Lee, Yoonho Lee, Jungtaek Kim +3
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such pr…
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu +1
Empirical evidence suggests that heavy-tailed degree distributions occurring in many real networks are well-approximated by power laws with exponents that may take values eithe…