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Found 10 papers, 5 papers with code
An Empirical Investigation of Representation Learning for Imitation
We propose a modular framework for constructing representation learning algorithms, then use our framework to evaluate the utility of representation learning for imitation across several environment suites.
Retrospective on the 2021 BASALT Competition on Learning from Human Feedback
The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks.
Detecting Modularity in Deep Neural Networks
These results suggest that graph-based partitioning can reveal modularity and help us understand how deep neural networks function.
The MineRL BASALT Competition on Learning from Human Feedback
Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve.
Clusterability in Neural Networks
We also exhibit novel methods to promote clusterability in neural network training, and find that in multi-layer perceptrons they lead to more clusterable networks with little reduction in accuracy.
Importance and Coherence: Methods for Evaluating Modularity in Neural Networks
We apply these methods on partitionings generated by a spectral clustering algorithm which uses a graph representation of the network's neurons and weights.
Pruned Neural Networks are Surprisingly Modular
To discern structure in these weights, we introduce a measurable notion of modularity for multi-layer perceptrons (MLPs), and investigate the modular structure of MLPs trained on datasets of small images.
Adversarial Policies: Attacking Deep Reinforcement Learning
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss.
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content
Malicious web content is a serious problem on the Internet today.
