Powering the Future: IBM's AI Solver Revolutionizes Grid Management #MISO #NeuralSolver #GridTech #RenewableEnergy #AIinEnergy #SmartGrids
Powering the Future: IBM’s AI Solver Revolutionizes Grid Management - Cozzy Energy Solutions
IBM Research and its partners have released GENCO, a new neural solver that combines three core grid analysis tasks, along with the GridFM Development Framework on Linux Foundation Energy’s OpenGridFM project. The electric grid faces growing demands as transportation, buildings, and industry increasingly rely on electricity. Simultaneously, the grid is evolving in both power supply
cozzyenergysolutions.com
August 12, 2026 at 6:57 AM
Powering the Future: IBM's AI Solver Revolutionizes Grid Management #MISO #NeuralSolver #GridTech #RenewableEnergy #AIinEnergy #SmartGrids
Bernardo (@esteveste.bsky.social) will present NeuralSolver, our new approach to learn decision-making algorithms that scale to large inputs at test time.
Thread: bsky.app/profile/este...
Poster: neurips.cc/virtual/2024...
OpenReview: openreview.net/forum?id=IxR...
Thread: bsky.app/profile/este...
Poster: neurips.cc/virtual/2024...
OpenReview: openreview.net/forum?id=IxR...
🚀 Excited to present NeuralSolver at NeurIPS 2024!
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
December 11, 2024 at 5:30 PM
Bernardo (@esteveste.bsky.social) will present NeuralSolver, our new approach to learn decision-making algorithms that scale to large inputs at test time.
Thread: bsky.app/profile/este...
Poster: neurips.cc/virtual/2024...
OpenReview: openreview.net/forum?id=IxR...
Thread: bsky.app/profile/este...
Poster: neurips.cc/virtual/2024...
OpenReview: openreview.net/forum?id=IxR...
Check out our paper: openreview.net/forum?id=IxR...
Project website: bernardoesteves.com/NeuralSolver
Project website: bernardoesteves.com/NeuralSolver
NeuralSolver: Learning Algorithms For Consistent and Efficient...
We contribute NeuralSolver, a novel recurrent solver that can efficiently and consistently extrapolate, i.e., learn algorithms from smaller problems (in terms of observation size) and execute those...
openreview.net
November 26, 2024 at 2:56 PM
Check out our paper: openreview.net/forum?id=IxR...
Project website: bernardoesteves.com/NeuralSolver
Project website: bernardoesteves.com/NeuralSolver
This method can also be applied to sequential decision problems, where small mistakes lead to catastrophic results. NeuralSolver can solve the doorkey environment of size 128x128 with a performance very close to the oracle, just by performing behavior cloning on smaller sizes.
November 26, 2024 at 2:56 PM
This method can also be applied to sequential decision problems, where small mistakes lead to catastrophic results. NeuralSolver can solve the doorkey environment of size 128x128 with a performance very close to the oracle, just by performing behavior cloning on smaller sizes.
With this approach, NeuralSolver achieves almost perfect algorithmic extrapolation by executing the same learned algorithm on much harder problems. While highly surpassing previous works.
November 26, 2024 at 2:56 PM
With this approach, NeuralSolver achieves almost perfect algorithmic extrapolation by executing the same learned algorithm on much harder problems. While highly surpassing previous works.
We can notice this by looking at how the latent values converge to the final result and the number of recurrent iterations. NeuralSolver learns an algorithm that starts by solving the dead ends of the maze, until finding the correct path that goes from the player to the goal.
November 26, 2024 at 2:37 PM
We can notice this by looking at how the latent values converge to the final result and the number of recurrent iterations. NeuralSolver learns an algorithm that starts by solving the dead ends of the maze, until finding the correct path that goes from the player to the goal.
We developed NeuralSolver, a method capable of learning algorithms that solve much more complex tasks than the ones used for training. That works on tasks that have the same or different output sizes!
November 26, 2024 at 2:37 PM
We developed NeuralSolver, a method capable of learning algorithms that solve much more complex tasks than the ones used for training. That works on tasks that have the same or different output sizes!
Check out our paper: openreview.net/forum?id=IxR...
Project website: bernardoesteves.com/NeuralSolver
Project website: bernardoesteves.com/NeuralSolver
NeuralSolver: Learning Algorithms For Consistent and Efficient...
We contribute NeuralSolver, a novel recurrent solver that can efficiently and consistently extrapolate, i.e., learn algorithms from smaller problems (in terms of observation size) and execute those...
openreview.net
November 26, 2024 at 2:30 PM
Check out our paper: openreview.net/forum?id=IxR...
Project website: bernardoesteves.com/NeuralSolver
Project website: bernardoesteves.com/NeuralSolver
This method can also be applied to sequential decision problems, where a small mistake in the sequence can lead to catastrophic results. NeuralSolver can solve the doorkey environment of size 128x128 with a performance very close to the oracle, just by performing behavior cloning on smaller sizes.
November 26, 2024 at 2:22 PM
This method can also be applied to sequential decision problems, where a small mistake in the sequence can lead to catastrophic results. NeuralSolver can solve the doorkey environment of size 128x128 with a performance very close to the oracle, just by performing behavior cloning on smaller sizes.
With this approach, NeuralSolver achieves almost perfect algorithmic extrapolation by executing the same learned algorithm on much harder problems. While highly surpassing previous works.
November 26, 2024 at 2:22 PM
With this approach, NeuralSolver achieves almost perfect algorithmic extrapolation by executing the same learned algorithm on much harder problems. While highly surpassing previous works.
We can notice this by looking at how the recurrent latent values converge to the final result, along a certain number of recurrent iterations. NeuralSolver learns an algorithm that starts by solving the dead ends of the maze, until finding the correct path that goes from the player to the goal.
November 26, 2024 at 2:22 PM
We can notice this by looking at how the recurrent latent values converge to the final result, along a certain number of recurrent iterations. NeuralSolver learns an algorithm that starts by solving the dead ends of the maze, until finding the correct path that goes from the player to the goal.
🚀 Excited to present NeuralSolver at NeurIPS 2024!
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
November 26, 2024 at 2:22 PM
🚀 Excited to present NeuralSolver at NeurIPS 2024!
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
🚀 Excited to present NeuralSolver at NeurIPS 2024!
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
November 27, 2024 at 1:42 AM
🚀 Excited to present NeuralSolver at NeurIPS 2024!
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵:
Humans can easily learn algorithms to solve much more complex tasks, from simple tasks. Machine learning methods usually fail to do this.
Can we build models that have similar algorithmic extrapolation abilities?
Thread 🧵: