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Found 10 papers, 2 papers with code
Operational Wind Speed Forecasts for Chile's Electric Power Sector Using a Hybrid ML Model
As Chile's electric power sector advances toward a future powered by renewable energy, accurate forecasting of renewable generation is essential for managing grid operations.
Controlling Commercial Cooling Systems Using Reinforcement Learning
This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems.
Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning
Reinforcement learning (RL) techniques have been developed to optimize industrial cooling systems, offering substantial energy savings compared to traditional heuristic policies.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Semi-analytical Industrial Cooling System Model for Reinforcement Learning
We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation.
Biologically Inspired Oscillating Activation Functions Can Bridge the Performance Gap between Biological and Artificial Neurons
The recent discovery of special human neocortical pyramidal neurons that can individually learn the XOR function highlights the significant performance gap between biological and artificial neurons.
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks
This ability to learn complex high-dimensional functions hierarchically can be attributed to the use of nonlinear activation functions.
Game Plan: What AI can do for Football, and What Football can do for AI
The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis.
3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement
We propose GAN-based image enhancement models for frequency enhancement of 2D and 3D seismic images.
AutoML for Contextual Bandits
We see that our model is able to perform much better than random exploration, being more regret efficient and able to converge with a limited number of samples, while remaining very general and easy to use due to the meta-learning approach.
