likelihood maximization
This is an estimation technique where model parameters are adjusted to maximize the likelihood of observed data under the model. It's a fundamental concept in statistics and machine learning for improving model fit.
- 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
- 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
- Direct Fisher Score Estimation for Likelihood Maximization
- IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation
- Risk-aware Direct Preference Optimization under Nested Risk Measure
- TreeGen: A Bayesian Generative Model for Hierarchies