BayesFlow
@bayesflow.org
Amortized Bayesian Workflows in Python.
🎲 Post author sampled from a multinomial distribution, choices
⋅ @marvin-schmitt.com
⋅ @paulbuerkner.com
⋅ @stefanradev.bsky.social
🔗 GitHub github.com/bayesflow-org/bayesflow
💬 Forum discuss.bayesflow.org
🎲 Post author sampled from a multinomial distribution, choices
⋅ @marvin-schmitt.com
⋅ @paulbuerkner.com
⋅ @stefanradev.bsky.social
🔗 GitHub github.com/bayesflow-org/bayesflow
💬 Forum discuss.bayesflow.org
The software implementation elegantly uses BayesFlow‘s modular data pipeline:
- Observables are embedded by a summary network.
- Context information (eg, prior and likelihood type) bypasses the summary net and enters the normalizing flow as direct conditions.
📀 Code: github.com/bayesflow-or...
- Observables are embedded by a summary network.
- Context information (eg, prior and likelihood type) bypasses the summary net and enters the normalizing flow as direct conditions.
📀 Code: github.com/bayesflow-or...
GitHub - bayesflow-org/SA-ABI: Contains the code accompanying the paper "Sensitivity-Aware Amortized Bayesian Inference".
Contains the code accompanying the paper "Sensitivity-Aware Amortized Bayesian Inference". - bayesflow-org/SA-ABI
github.com
November 25, 2024 at 12:46 PM
The software implementation elegantly uses BayesFlow‘s modular data pipeline:
- Observables are embedded by a summary network.
- Context information (eg, prior and likelihood type) bypasses the summary net and enters the normalizing flow as direct conditions.
📀 Code: github.com/bayesflow-or...
- Observables are embedded by a summary network.
- Context information (eg, prior and likelihood type) bypasses the summary net and enters the normalizing flow as direct conditions.
📀 Code: github.com/bayesflow-or...
The paper was led by @elseml.bsky.social, with multiple high-impact applications:
🦠 Disease outbreak modeling
🌎 Global warming thresholds
🧠 Human decision-making
✨ Sensitivity-aware amortized inference increases the amortization scope by a lot. Another step towards a Bayesian foundation model!
🦠 Disease outbreak modeling
🌎 Global warming thresholds
🧠 Human decision-making
✨ Sensitivity-aware amortized inference increases the amortization scope by a lot. Another step towards a Bayesian foundation model!
November 25, 2024 at 10:52 AM
The paper was led by @elseml.bsky.social, with multiple high-impact applications:
🦠 Disease outbreak modeling
🌎 Global warming thresholds
🧠 Human decision-making
✨ Sensitivity-aware amortized inference increases the amortization scope by a lot. Another step towards a Bayesian foundation model!
🦠 Disease outbreak modeling
🌎 Global warming thresholds
🧠 Human decision-making
✨ Sensitivity-aware amortized inference increases the amortization scope by a lot. Another step towards a Bayesian foundation model!
Hi, thanks for reaching out!
In the context of amortized inference, it’s been shown that many of the algorithms we use are susceptible to adversarial attacks, and this can be mitigated by regularizing wrt Fisher information.
📝 Paper by @mackelab.bsky.social:
arxiv.org/abs/2305.14984
In the context of amortized inference, it’s been shown that many of the algorithms we use are susceptible to adversarial attacks, and this can be mitigated by regularizing wrt Fisher information.
📝 Paper by @mackelab.bsky.social:
arxiv.org/abs/2305.14984
Adversarial robustness of amortized Bayesian inference
Bayesian inference usually requires running potentially costly inference procedures separately for every new observation. In contrast, the idea of amortized Bayesian inference is to initially invest c...
arxiv.org
November 24, 2024 at 8:25 PM
Hi, thanks for reaching out!
In the context of amortized inference, it’s been shown that many of the algorithms we use are susceptible to adversarial attacks, and this can be mitigated by regularizing wrt Fisher information.
📝 Paper by @mackelab.bsky.social:
arxiv.org/abs/2305.14984
In the context of amortized inference, it’s been shown that many of the algorithms we use are susceptible to adversarial attacks, and this can be mitigated by regularizing wrt Fisher information.
📝 Paper by @mackelab.bsky.social:
arxiv.org/abs/2305.14984