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Found 7 papers, 2 papers with code
Feature emergence via margin maximization: case studies in algebraic tasks
Understanding the internal representations learned by neural networks is a cornerstone challenge in the science of machine learning.
Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning
The success of SGD in deep learning has been ascribed by prior works to the implicit bias induced by high learning rate or small batch size ("SGD noise").
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization.
Loss of Plasticity in Continual Deep Reinforcement Learning
The ability to learn continually is essential in a complex and changing world.
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms.
Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management
Social media such as Twitter provide valuable information to crisis managers and affected people during natural disasters.
A Study of Policy Gradient on a Class of Exactly Solvable Models
Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return.
