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Found 5 papers, 2 papers with code
Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments
Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training.
Exploring the Structure of Human Adjective Representations
Human semantic representations are both difficult to capture and hard to fully interpret.
Learning deep representations by mutual information estimation and maximization
In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder.
On the Challenges of Detecting Rude Conversational Behaviour
In this study, we aim to identify moments of rudeness between two individuals.
Variance Regularizing Adversarial Learning
We hypothesize that this approach ensures a non-zero gradient to the generator, even in the limit of a perfect classifier.
