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Found 11 papers, 1 papers with code
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning
However, due to safety, ethical, and practicality constraints, this type of trial-and-error experimentation is often infeasible in many real-world domains such as healthcare and robotics.
Multi-step Planning for Automated Hyperparameter Optimization with OptFormer
As machine learning permeates more industries and models become more expensive and time consuming to train, the need for efficient automated hyperparameter optimization (HPO) has never been more pressing.
Retrieval-Augmented Reinforcement Learning
In this paper we explore an alternative paradigm in which we train a network to map a dataset of past experiences to optimal behavior.
On the role of planning in model-based deep reinforcement learning
These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.
Model-based Reinforcement Learning
reinforcement-learning
+1
Modular Meta-Learning with Shrinkage
Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components.
Submodular Field Grammars: Representation, Inference, and Application to Image Parsing
Natural scenes contain many layers of part-subpart structure, and distributions over them are thus naturally represented by stochastic image grammars, with one production per decomposition of a part.
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem
Based on this, we develop a recursive mini-batch algorithm for learning deep hard-threshold networks that includes the popular but poorly justified straight-through estimator as a special case.
The Sum-Product Theorem: A Foundation for Learning Tractable Models
We illustrate the power and generality of this approach by applying it to a new type of structured prediction problem: learning a nonconvex function that can be globally optimized in polynomial time.
Recursive Decomposition for Nonconvex Optimization
Similarly to DPLL-style SAT solvers and recursive conditioning in probabilistic inference, our algorithm, RDIS, recursively sets variables so as to simplify and decompose the objective function into approximately independent sub-functions, until the remaining functions are simple enough to be optimized by standard techniques like gradient descent.
How Prior Probability Influences Decision Making: A Unifying Probabilistic Model
To explain this data, a second model has been proposed which assumes a time-varying influence of the prior.
An ideal observer model for identifying the reference frame of objects
The object people perceive in an image can depend on its orientation relative to the scene it is in (its reference frame).
