Automatic variational inference with cascading flows
Authors: Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the new variational programs in a series of structured inference problems. We find that cascading flows have much higher performance than both normalizing flows and ASVI in a large set of structured inference problems. |
| Researcher Affiliation | Academia | 1 Donders Centre for Cognition, Radboud University, Netherlands 2 One Planet Research Center, imec-the Netherlands, Wageningen, Netherlands. |
| Pseudocode | Yes | The code for the spacial case without amortization and backward auxiliary coupling is shown in Figure 5. |
| Open Source Code | No | We provide an open-source implementation of this algorithm in Tensor Flow Probability (Dillon et al., 2017). The code for the spacial case without amortization and backward auxiliary coupling is shown in Figure 5. |
| Open Datasets | No | Ground-truth multivariate timeseries x = ( x1, . . . , x T ) were sampled from the generative model together with simulated first-half observations y1:T/2 = (y1, . . . , y T/2) and second-half observations y T/2:T = (y T/2+1, . . . , y T ). |
| Dataset Splits | No | All the variational models were trained conditioned only on the first half observations. Performance was assessed using two metrics. The first metric is the average marginal log-probability of the ground-truth given the variational posterior... Our second metric is log p(y T/2:T | y1:T/2): the predictive log-probability of the ground-truth observations in the second half of the timeseries given the observations in the first half... |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using “Tensor Flow Probability” but does not specify a version number or other software dependencies with version details. |
| Experiment Setup | Yes | In all experiments, the CF architectures were comprised of three highway flow blocks with softplus activation functions in each block except for the last which had linear activations. ... Weights and biases were initialized from centered normal distributions with scale 0.01. The λ variable was defined independently for each network as the logistic sigmoid of a learnable parameter l, which was initialized as 4 in order to keep the variational program close to the input program. ... In each repetition, all the variational programs were re-trained for 8000 iterations (enough to ensure convergence in all methods)... |