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Found 8 papers, 5 papers with code
Factual and Personalized Recommendations using Language Models and Reinforcement Learning
Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences.
Demystifying Embedding Spaces using Large Language Models
Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format.
pyRDDLGym: From RDDL to Gym Environments
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description.
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy.
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation
The direct optimization of this empirical objective in an end-to-end manner is called the risk-averse straight-line plan, which commits to a sequence of actions in advance and can be sub-optimal in highly stochastic domains.
Online Continual Learning in Image Classification: An Empirical Survey
To better understand the relative advantages of various approaches and the settings where they work best, this survey aims to (1) compare state-of-the-art methods such as MIR, iCARL, and GDumb and determine which works best at different experimental settings; (2) determine if the best class incremental methods are also competitive in domain incremental setting; (3) evaluate the performance of 7 simple but effective trick such as "review" trick and nearest class mean (NCM) classifier to assess their relative impact.
Online Class-Incremental Continual Learning with Adversarial Shapley Value
As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption.
Batch-level Experience Replay with Review for Continual Learning
Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity.
