Ising model
PulseAugur coverage of Ising model — every cluster mentioning Ising model across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
-
Schelling segregation model shows no critical scaling, study finds
This paper investigates the Schelling segregation model, finding no evidence of critical scaling. The research explored various neighborhood definitions and grid sizes, consistently showing that scaling diagnostics fail…
-
New attention mechanism VIA enhances AI for scientific tasks
Researchers have introduced Variational-Ising-Attention (VIA), a novel attention mechanism designed for scientific tasks. Unlike standard softmax attention, VIA incorporates an interacting Ising model to capture structu…
-
New method for causal inference in sequential settings with interference
Researchers have developed a new method for causal inference in sequential settings with interference and latent confounding. The approach utilizes an Ising model to capture dependencies between unit outcomes over time,…
-
Autoencoders Learn Ising Model Dynamics: Two Regimes Identified
Researchers have investigated the learning dynamics of autoencoders when trained on data from the Ising model, a system used to study magnetism. They identified two distinct dynamical regimes related to model hyperparam…
-
New algorithm learns constant-depth circuits under locally sampleable graphical models
Researchers have developed a new algorithm for learning constant-depth circuits under graphical models that can be locally sampled. This work extends previous findings by Chandrasekaran, Gaitonde, Moitra, and Vasilyan (…
-
New research explores neural network and transfer learning for Ising models · 3 sources tracked
Two new research papers explore the application of neural networks and transfer learning to high-dimensional Ising models. The first paper investigates out-of-distribution performance of various neural architectures, fi…
-
New algorithm enables scalable training for thermodynamic AI models
Researchers have developed a new backpropagation-based algorithm to train deep convolutional networks for thermodynamic inference on Ising machine hardware. This method enables scalable training for low-power AI inferen…
-
New tool synthesizes probabilistic processors using Ising model for optimization
Researchers have developed a new tool designed to synthesize and simulate probabilistic processors that leverage the Ising model for solving complex combinatorial optimization problems. This tool automatically generates…
-
Thermodynamic Hardware Slashes Energy Use for Drug Development Optimization
Researchers have developed a method to perform codon optimization for drug development using thermodynamic hardware, which leverages thermal fluctuations for computation. This approach, applied to the SARS-CoV-2 spike p…
-
Basis rotation impacts Neural Quantum State performance
A new arXiv paper explores how basis rotations affect Neural Quantum States (NQS) performance. Researchers used an Ising model to demonstrate that these rotations can alter the optimization landscape, potentially leadin…
-
AI framework identifies physical symmetries using attention mechanisms
Researchers have developed a novel optimization framework that leverages a Set-Transformer architecture with self-attention mechanisms to identify symmetries in physical models. This machine learning-based approach enco…
-
AI research explores physics and algebra to boost neural network efficiency
Two new research papers explore incorporating physical priors and algebraic insights into neural networks to improve their efficiency and performance. The first paper introduces Variational Autoregressive Networks that …
-
AI models learn to invert renormalization group for physics simulations
Researchers have developed minimal neural networks capable of inverting the renormalization group coarse-graining process in the two-dimensional Ising model. These networks can probabilistically reconstruct scale-invari…
-
Sampling two-dimensional spin systems with transformers
Researchers have developed a novel transformer-based approach for sampling two-dimensional spin systems, addressing the common inefficiency associated with transformers in this domain. Their method generates groups of s…