Energy
SPRING: Studying the Paper and Reasoning to Play Games Yue Wu
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or Minecraft. We propose a novel approach, SPRING, to read Crafter's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM).
Learning and Collusion in Multi-unit Auctions
In a carbon auction, licenses for CO2 emissions are allocated among multiple interested players. Inspired by this setting, we consider repeated multi-unit auctions with uniform pricing, which are widely used in practice. Our contribution is to analyze these auctions in both the offline and online settings, by designing efficient bidding algorithms with low regret and giving regret lower bounds. We also analyze the quality of the equilibria in two main variants of the auction, finding that one variant is susceptible to collusion among the bidders while the other is not.
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research.
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Many of those tasks have been addressed on datasets created with single climate models.
BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time Series
Nevertheless, the use of these techniques at a large scale can be challenging and expensive. Satellite data have been widely used as a valuable tool in estimating biomass on a global scale.
Supplementary Materials for " Cross-Scale MAE: AT ale of Multiscale 1 Exploitation in Remote Sensing "
We observe interesting patterns from the results presented in Table 2.
Cross-Scale MAE: AT ale of Multi-Scale Exploitation in Remote Sensing Maofeng T ang
We present Cross-Scale MAE, a self-supervised model built upon the Masked Auto-Encoder (MAE).
Appendix Table of Contents
Our datasets and code are available via the following links: Github: https://github.com/NREL/BuildingsBench As described in Sec. 3 and Sec. 4, Buildings-900K and the BuildingsBench benchmark datasets are B.1 Motivation Q: For what purpose was the dataset created? It specifically addresses a lack of appropriately sized and diverse datasets for pretraining STLF models. We emphasize that the EULP was not originally developed for studying STLF. Rather, it was developed as a general resource to "...help electric utilities, grid operators, manufacturers, Q: Who created the dataset (e.g., which team, research group) and on behalf of which entity Q: Who funded the creation of the dataset?
Buildings Bench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting
Short-term load forecasting (STLF), which typically ranges from hour-ahead to a few days ahead, plays a multitude of critical roles.
