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Found 6 papers, 0 papers with code
Efficient Model Stealing Defense with Noise Transition Matrix
With the escalating complexity and investment cost of training deep neural networks safeguarding them from unauthorized usage and intellectual property theft has become imperative.
An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training
To address these issues, we propose a flexible model placement framework that offers two general and agile model placement strategies.
Modeling Event Propagation via Graph Biased Temporal Point Process
Temporal point process is widely used for sequential data modeling.
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
Temporal point process is an expressive tool for modeling event sequences over time.
Decoupled Learning for Factorial Marked Temporal Point Processes
In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i. e. a marker.
Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes
By treating a sequence of transition events as a point process, we develop a novel framework for modeling patient flow through various CUs and jointly predicting patients' destination CUs and duration days.
