by instadatahelp | Sep 4, 2023 | AI Blogs
Continuous-Time q-Learning: A Study By Yanwei Jia and Xun Yu Zhou; 24(161):1−61, 2023. Abstract This study focuses on the continuous-time counterpart of Q-learning for reinforcement learning (RL) using the entropy-regularized, exploratory diffusion process formulation...
![YaRN: Enhancing Context Window Extension of Large Language Models for Improved Efficiency (arXiv:2309.00071v1 [cs.CL])](https://instadatanews.com/wp-content/uploads/2023/08/arxiv-logo-fb-1080x675.png)
by instadatahelp | Sep 4, 2023 | AI Blogs
The Rotary Position Embeddings (RoPE) have proven to be successful in encoding positional information in transformer-based language models. However, these models struggle to generalize beyond the maximum sequence length they were trained on. To address this...
by instadatahelp | Sep 4, 2023 | AI Blogs
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann, Arto Klami; 24(67):1−51, 2023. Abstract The choice of prior distributions in Bayesian models used in machine learning...
by instadatahelp | Sep 4, 2023 | AI Blogs
arXivLabs is a platform where collaborators can create and share new features for arXiv directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our core principles of openness, community, excellence, and user...
by instadatahelp | Sep 4, 2023 | AI Blogs
Flexible Model Aggregation for Quantile Regression Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani; 24(162):1−45, 2023. Abstract Quantile regression is a fundamental problem in statistical learning that aims to quantify uncertainty in...
by instadatahelp | Sep 4, 2023 | AI Blogs
The focus of this paper is on continual learning (CL), which involves learning new tasks without forgetting previous tasks. However, existing CL methods require a large amount of raw data, which is often unavailable due to copyright and privacy concerns. Instead,...
