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View article: DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning Open
Training corpuses for vision language models (VLMs) typically lack sufficient amounts of decision-centric data. This renders off-the-shelf VLMs sub-optimal for decision-making tasks such as in-the-wild device control through graphical user…
View article: Discovering Influencers in Opinion Formation Over Social Graphs
Discovering Influencers in Opinion Formation Over Social Graphs Open
The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on pr…
View article: Discovering Influencers in Opinion Formation over Social Graphs
Discovering Influencers in Opinion Formation over Social Graphs Open
The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on pr…
View article: Unsupervised Simplification of Legal Texts
Unsupervised Simplification of Legal Texts Open
The processing of legal texts has been developing as an emerging field in natural language processing (NLP). Legal texts contain unique jargon and complex linguistic attributes in vocabulary, semantics, syntax, and morphology. Therefore, t…