IEEE

FranSys—A Fast Non-Autoregressive Recurrent Neural Network for Multi-Step Ahead Prediction

Abstract: Neural network-based nonlinear system identification is crucial for various multi-step ahead prediction tasks, including model predictive control and digital twins. These applications demand ...
GitHub

Train multi-step agents for real-world tasks using GRPO.

RULER (Relative Universal LLM-Elicited Rewards) eliminates the need for hand-crafted reward functions by using an LLM-as-judge to automatically score agent trajectories. Simply define your task in the ...
IEEE

The Effect of Input Length on Prediction Accuracy in Short-Term Multi-Step Electricity Load Forecasting: A CNN-LSTM Approach

Abstract: Accurate load forecasting is crucial for effective power system management and planning in the context of growing electricity demand triggered by the proliferation of technological devices ...