668 citations · 988 across the 11 of their papers we have counts for
7 papers · 1 filter
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy +10
Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investiga…
Attentive Neural Processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz +5
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each…
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning
Aishwarya Agrawal, Mateusz Malinowski, Felix Hill +3
Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must le…
Neural Processes
Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum +4
A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP),…
Conditional Neural Processes
Marta Garnelo, Dan Rosenbaum, Chris J. Maddison +6
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Proce…
Kickstarting Deep Reinforcement Learning
Simon Schmitt, Jonathan J. Hudson, Augustin Zidek +8
We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and po…