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Found 14 papers, 2 papers with code
Data-Centric Governance
Modern AI systems are data-centric: they act on data, produce data, and are built through data engineering.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
In this paper, we extend a recently-proposed framework for explainable RL that is based on analyses of "interestingness."
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2
We present a version of GR for LRL that satisfies two desiderata: (a) Introspective density modelling of the latent representations of policies learned using deep RL, and (b) Model-free end-to-end learning.
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space
We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characterize agent behavior.
Conformal Prediction Intervals for Markov Decision Process Trajectories
This paper extends previous work on conformal prediction for functional data and conformalized quantile regression to provide conformal prediction intervals over the future behavior of an autonomous system executing a fixed control policy on a Markov Decision Process (MDP).
Dynamically Throttleable Neural Networks (TNN)
Conditional computation for Deep Neural Networks (DNNs) reduce overall computational load and improve model accuracy by running a subset of the network.
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer
We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL.
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer
We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL.
Toward Runtime-Throttleable Neural Networks
As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers.
Generative Memory for Lifelong Reinforcement Learning
Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifetime experience.
Bootstrapping Deep Neural Networks from Approximate Image Processing Pipelines
Complex image processing and computer vision systems often consist of a processing pipeline of functional modules.
