Research map / Probabilistic and scientific modeling / Variational and generative sampling
Variational Implicit Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use VIP function-space inference with a Bayesian neural network prior instead of MCMC-ABC | Lotka–Volterra prediction · Lotka–Volterra test | Test NLL: 0.717 -> 0.485 | Helped |
| Use VIP function-space inference with a Bayesian neural network prior instead of SMC-ABC | Lotka–Volterra prediction · Lotka–Volterra test | Test NLL: 0.588 -> 0.485 | Helped |
| Use VIP function-space inference with a Bayesian neural network prior instead of VDO-BNN | Lotka–Volterra prediction · Lotka–Volterra test | Test NLL: 1.25 -> 0.485 | Helped |
| Use VIP function-space inference with a Bayesian neural network prior instead of SVGP | Lotka–Volterra prediction · Lotka–Volterra test | Test NLL: 1.266 -> 0.485 | Helped |
| Use VIP function-space inference with a Bayesian neural network prior instead of MCMC-ABC | Lotka–Volterra prediction · Lotka–Volterra test | Test RMSE: 0.307 -> 0.094 | Helped |
| Use VIP function-space inference with a Bayesian neural network prior instead of SMC-ABC | Lotka–Volterra prediction · Lotka–Volterra test | Test RMSE: 0.357 -> 0.094 | Helped |
| Use VIP function-space inference with a Bayesian neural network prior instead of VDO-BNN | Lotka–Volterra prediction · Lotka–Volterra test | Test RMSE: 0.80 -> 0.094 | Helped |
| Use VIP function-space inference with a Bayesian neural network prior instead of SVGP | Lotka–Volterra prediction · Lotka–Volterra test | Test RMSE: 0.950 -> 0.094 | Helped |
Closest papers by meaning
- Deep Variational Implicit Processes (ICLR 2023 · similarity 0.91)
- Function-space Inference with Sparse Implicit Processes (ICML 2022 · similarity 0.87)
- Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables (ICML 2020 · similarity 0.85)
- Kernel Implicit Variational Inference (ICLR 2018 · similarity 0.85)
- Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models (NeurIPS 2026 · similarity 0.85)
- Sparse Variational Student-t Processes (AAAI 2024 · similarity 0.84)
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