Evidence of novelty and specialization behavior in participatory science reporting
https://doi.org/10.1111/oik.10938
#ParticipatoryScience #TaxonomicSpecialization #SimulationBasedInference #NoveltyBehavior
https://doi.org/10.1111/oik.10938
#ParticipatoryScience #TaxonomicSpecialization #SimulationBasedInference #NoveltyBehavior
March 13, 2025 at 5:06 PM
Evidence of novelty and specialization behavior in participatory science reporting
https://doi.org/10.1111/oik.10938
#ParticipatoryScience #TaxonomicSpecialization #SimulationBasedInference #NoveltyBehavior
https://doi.org/10.1111/oik.10938
#ParticipatoryScience #TaxonomicSpecialization #SimulationBasedInference #NoveltyBehavior
On Oct 5 2025 researchers presented a fast SBI framework using a two‑step amortized scheme with Chebyshev sampling, applied to trawl processes for energy demand. https://getnews.me/new-simulation-based-inference-boosts-complex-time-series-accuracy/ #simulationbasedinference #trawlprocesses
October 8, 2025 at 8:25 AM
On Oct 5 2025 researchers presented a fast SBI framework using a two‑step amortized scheme with Chebyshev sampling, applied to trawl processes for energy demand. https://getnews.me/new-simulation-based-inference-boosts-complex-time-series-accuracy/ #simulationbasedinference #trawlprocesses
No likelihood? No problem
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
Simulation-trained conditional normalizing flows for likelihood approximation: a case study in stress regulation kinetics in yeast
Physics-inspired inference often hinges on the ability to construct a likelihood, or the probability of observing a sequence of data given a model. These likelihoods can be directly maximized for para...
arxiv.org
June 19, 2025 at 10:25 PM
No likelihood? No problem
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
The class of stochastic models we can simulate is A LOT larger than the ones we can write likelihoods.
What if we could learn the likelihood directly from simulation? See the 🧵👇
arxiv.org/abs/2506.09374
#SimulationBasedInference #Neuralnetworks #AI
We tackle this with #SimulationBasedInference!
For physicists: we write down the extended log-likelihood and parametrize everything that isn't known analytically with NNs.
For ML-folks: we maximize domain knowledge and factorize the problem so that we only need small regressors and a classifier.
For physicists: we write down the extended log-likelihood and parametrize everything that isn't known analytically with NNs.
For ML-folks: we maximize domain knowledge and factorize the problem so that we only need small regressors and a classifier.
May 12, 2025 at 11:35 AM
We tackle this with #SimulationBasedInference!
For physicists: we write down the extended log-likelihood and parametrize everything that isn't known analytically with NNs.
For ML-folks: we maximize domain knowledge and factorize the problem so that we only need small regressors and a classifier.
For physicists: we write down the extended log-likelihood and parametrize everything that isn't known analytically with NNs.
For ML-folks: we maximize domain knowledge and factorize the problem so that we only need small regressors and a classifier.