Variational inference (Jordan et al., 1998)
has been essential in learning deep directed latent variable models on high-dimensional data, enabling extraction of complex, non-linear relationships, such as object identities(Higgins et al., 2016) and dynamics (Xue et al., 2016; Karl et al., 2017) directly from observations. Variational inference reformulates inference as optimization (Neal & Hinton, 1998; Hoffman et al., 2013). However, the current trend has moved toward employing inference models (Dayan et al., 1995; Gregor et al., 2014; Kingma & Welling, 2014; Rezende et al., 2014), mappings from data to approximate posterior estimates that are amortized across examples. Intuitively, the inference model encodes observations into latent representations, and the generative model decodes these representations into reconstructions. Yet, this approach has notable limitations. For instance, in models with empirical priors, such as hierarchical latent variable models, “bottom-up” data-encoding inference models cannot account for “top-down” priors (Section 4.1). This has prompted the use of top-down inference techniques (Sønderby et al., 2016), which currently lack a rigorous theoretical justification. More generally, the inability of inference models to reach fully optimized approximate posterior estimates results in decreased modeling performance, referred to as an amortization gap (Krishnan et al., 2018; Cremer et al., 2017).
To combat this problem, our work offers a departure from previous approaches by re-examining inference from an optimization perspective. We utilize approximate posterior gradients to perform inference optimization. Yet, we improve computational efficiency over conventional optimizers by encoding these gradients with an inference model that learns how to iteratively update approximate posterior estimates. The resulting iterative inference models resemble learning to learn (Andrychowicz et al., 2016) applied to variational inference optimization. However, we refine and extend this method along several novel directions. Namely, (1) we show that learned optimization models can be applied to inference optimization of latent variables; (2) we show that non-recurrent optimization models work well in practice, breaking assumptions about the necessity of non-local curvature for outperforming conventional optimizers (Andrychowicz et al., 2016; Putzky & Welling, 2017); and (3) we provide a new form of optimization model that encodes errors rather than gradients to approximate higher order derivatives, empirically resulting in faster convergence.
Our main contributions are summarized as follows:
we introduce a family of iterative inference models, which generalize standard inference models,
we provide the first theoretical justification for top-down inference techniques,
we empirically evaluate iterative inference models, demonstrating that they outperform standard inference models on several data sets of images and text.
2.1 Latent Variable Models & Variational Inference
Latent variable models are generative probabilistic models that use local (per data example) latent variables, , to model observations, , using global (across data examples) parameters,
. A model is defined by the joint distribution, composed of the conditional likelihood and the prior. Learning the model parameters and inferring the posterior, , are intractable for all but the simplest models, as they require evaluating the marginal likelihood, , which involves integrating the model over . For this reason, we often turn to approximate inference methods.
Variational inference reformulates this intractable integration as an optimization problem by introducing an approximate posterior111We use to denote that the approximate posterior is conditioned on a data example (i.e. local), however this does not necessarily imply a direct functional mapping., , typically chosen from some tractable family of distributions, and minimizing the KL-divergence from the posterior, . This quantity cannot be minimized directly, as it contains the posterior. Instead, KL-divergence can be decomposed into
where is the evidence lower bound (ELBO), which is defined as:
The first term in eq. 3 expresses how well the output reconstructs the data example. The second term quantifies the dissimilarity between the approximate posterior and the prior. Because is not a function of , we can minimize in eq. 1 by maximizing w.r.t. , thereby performing approximate inference. Likewise, because is non-negative, is a lower bound on . Therefore, once we have inferred an optimal , learning corresponds to maximizing w.r.t. .
2.2 Variational Expectation Maximization (EM) via Gradient Ascent
The optimization procedures for variational inference and learning are respectively the expectation and maximization steps of the variational EM algorithm (Dempster et al., 1977; Neal & Hinton, 1998), which alternate until convergence. This is typically performed in the batched setting of stochastic variational inference (Hoffman et al., 2013). When takes a parametric form, the expectation step for data example involves finding a set of distribution parameters, , that are optimal w.r.t. . With a factorized Gaussian density over continuous latent variables, i.e. and , conventional optimization techniques repeatedly estimate the stochastic gradients to optimize w.r.t. , e.g.:
where is the step size. This procedure, which is repeated for each example, is computationally expensive and requires setting step-size hyper-parameters.
2.3 Amortized Inference Models
Due to the aforementioned issues, gradient updates of approximate posterior parameters are rarely performed in practice. Rather, inference models are often used to map observations to approximate posterior estimates. Optimization of each data example’s approximate posterior parameters, , is replaced with the optimization of a shared, i.e. amortized (Gershman & Goodman, 2014), set of parameters, , contained within an inference model, , of the form:
, which employs the reparameterization trick to propagate stochastic gradients from the generative model to the inference model, both of which are parameterized by neural networks. We refer to inference models of this form asstandard inference models. As discussed in Section 3, the aim of this paper is to move beyond the direct encoder paradigm of standard inference models to develop improved techniques for performing inference.
3 Iterative Amortized Inference
In Section 3.3, we introduce our contribution, iterative inference models. However, we first motivate our approach in Section 3.1 by discussing the limitations of standard inference models. We then draw inspiration from other techniques for learning to optimize (Section 3.2).
3.1 Standard Inference Models & Amortization Gaps
As described in Section 2.1, variational inference reformulates inference as the maximization of w.r.t. , constituting the expectation step of the variational EM algorithm. In general, this is a difficult non-convex optimization problem, typically requiring a lengthy iterative estimation procedure. Yet, standard inference models attempt to perform this optimization through a direct, discriminative mapping from data observations to approximate posterior parameters. Of course, generative models can adapt to accommodate sub-optimal approximate posteriors. Nevertheless, the possible limitations of a direct inference mapping applied to this difficult optimization procedure may result in a decrease in overall modeling performance.
We demonstrate this concept in Figure 1 by visualizing the optimization surface of
defined by a 2-D latent Gaussian model and a particular binarized MNIST(LeCun et al., 1998) data example. To visualize the approximate posterior, we use a point estimate, , where is the estimate and is the Dirac delta function. See Appendix C.1 for details. Shown on the plot are the optimal (maximum a posteriori or MAP) estimate, the estimate from a standard inference model, and an optimization trajectory of gradient ascent. The inference model is unable to achieve the optimum, but manages to output a reasonable estimate in one pass. Gradient ascent requires many iterations and is sensitive to step-size, but through the iterative estimation procedure, ultimately arrives at a better final estimate. The inability of inference models to reach optimal approximate posterior estimates, as typically compared with gradient-based methods, creates an amortization gap (Krishnan et al., 2018; Cremer et al., 2017), which impairs modeling performance. Additional latent dimensions and more complex data could further exacerbate this problem.
3.2 Learning to Iteratively Optimize
While offering significant benefits in computational efficiency, standard inference models can suffer from sizable amortization gaps (Krishnan et al., 2018). Parameterizing inference models as direct, static mappings from to may be overly restrictive, widening this gap. To improve upon this direct encoding paradigm, we pose the following question: can we retain the computational efficiency of inference models while incorporating more powerful iterative estimation capabilities? Our proposed solution is a new class of inference models, capable of learning how to update approximate posterior estimates by encoding gradients or errors. Due to the iterative nature of these models, we refer to them as iterative inference models. Through an analysis with latent Gaussian models, we show that iterative inference models generalize standard inference models (Section 4.3) and offer theoretical justification for top-down inference in hierarchical models (Section 4.1).
Our approach relates to learning to learn (Andrychowicz et al., 2016), where an optimizer model learns to optimize the parameters of an optimizee model. The optimizer receives the optimizee’s parameter gradients and outputs updates to these parameters to improve the optimizee’s loss. The optimizer itself can be learned due to the differentiable computation graph. Such models can adaptively adjust step sizes, potentially outperforming conventional optimizers. For inference optimization, previous works have combined standard inference models with gradient updates (Hjelm et al., 2016; Krishnan et al., 2018; Kim et al., 2018), however, these works do not learn to iteratively optimize. (Putzky & Welling, 2017) use recurrent inference models for MAP estimation of denoised images in linear models. We propose a unified method for learning to perform variational inference optimization, generally applicable to probabilistic latent variable models. Our work extends techniques for learning to optimize along several novel directions, discussed in Section 4.
3.3 Iterative Inference Models
We denote an iterative inference model as with parameters . With as the ELBO for data example at inference iteration , the model uses the approximate posterior gradients, denoted , to output updated estimates of :
where is the estimate of at inference iteration . Eq. 6 is in a general form and contains, as special cases, the linear update in eq. 4, as well as the residual, non-linear update used in (Andrychowicz et al., 2016). Figure 2 displays a computation graph of the inference procedure, and Algorithm 1 in Appendix B describes the procedure in detail. As with standard inference models, the parameters of an iterative inference model can be updated using estimates of , obtained through the reparameterization trick (Kingma & Welling, 2014; Rezende et al., 2014) or through score function methods (Gregor et al., 2014; Ranganath et al., 2014). Model parameter updating is performed using stochastic gradient techniques with and .
4 Iterative Inference in Latent Gaussian Models
We now describe an instantiation of iterative inference models for (single-level) latent Gaussian models, which have a Gaussian prior density over latent variables:
. Although the prior is typically a standard Normal density, we use this prior form for generality. Latent Gaussian models are often used in VAEs and are a common choice for continuous-valued latent variables. While the approximate posterior can be any probability density, it is typically also chosen as Gaussian:. With this choice, corresponds to for example . Dropping the superscript to simplify notation, we can express eq. 6 for this model as:
where and are the iterative inference models for updating and respectively. In practice, these models can be combined, with shared inputs and model parameters but separate outputs to update each term.
In Appendix A, we derive the stochastic gradients and for the cases where takes a Gaussian and Bernoulli form, though any output distribution can be used. Generally, these gradients are comprised of (1) errors, expressing the mismatch in distributions, and (2) Jacobian matrices, which invert the generative mappings. For instance, assuming a Gaussian output density, , the gradient for is
where the Jacobian (), bottom-up errors (), and top-down errors () are defined as
Here, we have assumed is a function of and is a global parameter. The gradient is comprised of similar terms as well as an additional term penalizing approximate posterior entropy. Inspecting and understanding the composition of the gradients reveals the forces pushing the approximate posterior toward agreement with the data, through , and agreement with the prior, through . In other words, inference is as much a top-down process as it is a bottom-up process, and the optimal combination of these terms is given by the approximate posterior gradients. As discussed in Section 4.1, standard inference models have traditionally been purely bottom-up, only encoding the data.
4.1 Reinterpreting Top-Down Inference
To increase the model capacity of latent variable models, it is common to add higher-level latent variables, thereby providing flexible empirical priors on lower-level variables. Traditionally, corresponding standard inference models were parmeterized as purely bottom-up (e.g. Fig. 1 of (Rezende et al., 2014)). It was later found to be beneficial to incorporate top-down information from higher-level variables in the inference model, the given intuition being that “a purely bottom-up inference process …does not correspond well with real perception” (Sønderby et al., 2016), however, a rigorous justification of this technique was lacking.
Iterative inference models, or rather, the gradients that they encode, provide a theoretical explanation for this previously empirical heuristic. As seen in eq.9, the approximate posterior parameters are optimized to agree with the prior, while also fitting the conditional likelihood to the data. Analogous terms appear in the gradients for hierarchical models. For instance, in a chain-structured hierarchical model, the gradient of , the approximate posterior mean at layer , is
where is the Jacobian of the generative mapping at layer and is defined similarly to eq. 12. depends on the top-down prior at layer , which, unlike the single-level case, varies across data examples. Thus, a purely bottom-up inference procedure may struggle, as it must model both the bottom-up data dependence as well as the top-down prior. Top-down inference (Sønderby et al., 2016) explicitly uses the prior to perform inference. Iterative inference models instead rely on approximate posterior gradients, which naturally account for both bottom-up and top-down influences.
4.2 Approximating Approximate Posterior Derivatives
In the formulation of iterative inference models given in eq. 6, inference optimization is restricted to first-order approximate posterior derivatives. Thus, it may require many inference iterations to reach reasonable approximate posterior estimates. Rather than calculate costly higher-order derivatives, we can take a different approach.
Approximate posterior derivatives (e.g. eq. 9 and higher-order derivatives) are essentially defined by the errors at the current estimate, as the other factors, such as the Jacobian matrices, are internal to the model. Thus, the errors provide more general information about the curvature beyond the gradient. As iterative inference models already learn to perform approximate posterior updates, it is natural to ask whether the errors provide a sufficient signal for faster inference optimization. In other words, we may be able to offload approximate posterior derivative calculation onto the inference model, yielding a model that requires fewer inference iterations while maintaining or possibly improving computational efficiency.
where, again, these models can be shared, with separate outputs per parameter. In Section 5.2, we empirically find that models of this form converge to better solutions than gradient-encoding models when given fewer inference iterations. It is also worth noting that this error encoding scheme is similar to DRAW (Gregor et al., 2015). However, in addition to architectural differences in the generative model, DRAW and later extensions do not include top-down errors (Gregor et al., 2016), nor error precision-weighting.
4.3 Generalizing Standard Inference Models
Under certain assumptions on single-level latent Gaussian models, iterative inference models of the form in Section 4.2 generalize standard inference models. First, note that (eq. 11) is a stochastic affine transformation of :
Reasonably assuming that the initial approximate posterior and prior are both constant, then in expectation, , , and are constant across all data examples at the first inference iteration. Using proper weight initialization and input normalization, it is equivalent to input or an affine transformation of into a fully-connected neural network. Therefore, standard inference models are equivalent to the special case of a one-step iterative inference model. Thus, we can interpret standard inference models as learning a map of local curvature around a fixed approximate posterior estimate. Iterative inference models, on the other hand, learn to traverse the optimization landscape more generally.
Using latent Gaussian models, we performed an empirical evaluation of iterative inference models on both image and text data. For images, we used MNIST (LeCun et al., 1998), Omniglot (Lake et al., 2013), Street View House Numbers (SVHN) (Netzer et al., 2011), and CIFAR-10 (Krizhevsky & Hinton, 2009). MNIST and Omniglot were dynamically binarized and modeled with Bernoulli output distributions, and SVHN and CIFAR-10 were modeled with Gaussian output densities, using the procedure from (Gregor et al., 2016). For text, we used RCV1 (Lewis et al., 2004), with word count data modeled with a multinomial output.
Details on implementing iterative inference models are found in Appendix B. The primary difficulty of training iterative inference models comes from shifting gradient and error distributions during the course of inference and learning. In some cases, we found it necessary to normalize these inputs using layer normalization (Ba et al., 2016). We also found it beneficial, though never necessary, to additionally encode the data itself, particularly when given few inference iterations (see Figure 6(a)). For comparison, all experiments use feedforward networks, though we observed similar results with recurrent inference models. Reported values of were estimated using 1 sample, and reported values of and perplexity (Tables 1 & 2) were estimated using 5,000 importance weighted samples. Additional experiment details, including model architectures, can be found in Appendix C. Accompanying code can be found on GitHub at joelouismarino/iterative_inference.
Section 5.1 demonstrates the optimization capabilities of iterative inference models. Section 5.2 explores two methods by which to further improve the modeling performance of these models. Section 5.3 provides a quantitative comparison between standard and iterative inference models.
5.1 Approximate Inference Optimization
We begin with a series of experiments that demonstrate the inference optimization capabilities of iterative inference models. These experiments confirm that iterative inference models indeed learn to perform inference optimization through an adaptive iterative estimation procedure. These results highlight the qualitative differences between this inference optimization procedure and that of standard inference models. That is, iterative inference models are able to effectively utilize multiple inference iterations rather than collapsing to static, one-step encoders.
Direct Visualization As in Section 3.1, we directly visualize iterative inference optimization in a 2-D latent Gaussian model trained on MNIST with a point estimate approximate posterior. Model architectures are identical to those used in Section 3.1, with additional details found in Appendix C.1. Shown in Figure 3 is a 16-step inference optimization trajectory taken by the iterative inference model for a particular example. The model adaptively adjusts inference update step sizes to navigate the optimization surface, quickly arriving and remaining at a near-optimal estimate.
During Inference We can quantify and compare optimization performance through the ELBO. In Figure 4, we plot the average ELBO on the MNIST validation set during inference, comparing iterative inference models with conventional optimizers. Details are in Appendix C.2. On average, the iterative inference model converges significantly faster to better estimates than the optimizers. The model actually has less derivative information than the optimizers; it only has access to the local gradient, whereas the optimizers use momentum and similar terms. The model’s final estimates are also stable, despite only being trained using 16 inference iterations.
Reconstructions Approximate inference optimization can also be visualized through image reconstructions. As the reconstruction term is typically the dominant term in , the output reconstructions should improve in terms of visual quality during inference optimization, resembling . We demonstrate this phenomenon with iterative inference models for several data sets in Figure 5. Additional reconstructions are shown in Appendix C.3.
Gradient Magnitudes During inference optimization, iterative inference models should ideally obtain approximate posterior estimates near local maxima. The approximate posterior gradient magnitudes should thus decrease during inference. Using a model trained on RCV1, we recorded average gradient magnitudes for the approximate posterior mean during inference. In Figure 6, we plot these values throughout training, finding that they do, indeed, decrease. See Appendix C.4 for more details.
5.2 Additional Inference Iterations & Latent Samples
We highlight two sources that allow iterative inference models to further improve modeling performance: additional inference iterations and samples. Additional inference iterations allow the model to further refine approximate posterior estimates. Using MNIST, we trained models by encoding approximate posterior gradients or errors , with or without the data , for 2, 5, 10, and 16 inference iterations. While we kept the model architectures identical, the encoded terms affect the number of input parameters to each model. For instance, the small size of relative to gives the gradient encoding model fewer input parameters than a standard inference model. The other models have more input parameters. Results are shown in Figure 6(a), where we observe improved performance with increasing inference iterations. All iterative inference models outperformed standard inference models. Note that encoding errors to approximate higher-order derivatives helps when training with fewer inference iterations.
Additional approximate posterior samples provide more precise gradient and error estimates, potentially allowing an iterative inference model to output improved updates. To verify this, we trained standard and iterative inference models on MNIST using 1, 5, 10, and 20 approximate posterior samples. Iterative inference models were trained by encoding the data () and approximate posterior gradients () for 5 iterations. Results are shown in Figure 6(b). Iterative inference models improve by more than 1 nat with additional samples, further widening the improvement over similar standard inference models.
additional samples. Iterative inference models improve significantly with both quantities. Lines do not imply interpolation.
5.3 Comparison with Standard Inference Models
We now provide a quantitative performance comparison between standard and iterative inference models on MNIST, CIFAR-10, and RCV1. Inference model architectures are identical across each comparison, with the exception of input parameters. Details are found in Appendix C.7. Table 1 contains estimated marginal log-likelihood performance on MNIST and CIFAR-10. Table 2 contains estimated perplexity on RCV1222Perplexity re-weights log-likelihood by document length.. In each case, iterative inference models outperform standard inference models. This holds for both single-level and hierarchical models. We observe larger improvements on the high-dimensional RCV1 data set, consistent with (Krishnan et al., 2018). Because the generative model architectures are kept fixed, performance improvements demonstrate improvements in inference optimization.
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We have proposed iterative inference models, which learn to refine inference estimates by encoding approximate posterior gradients or errors. These models generalize and extend standard inference models, and by naturally accounting for priors during inference, these models provide insight and justification for top-down inference. Through empirical evaluations, we have demonstrated that iterative inference models learn to perform variational inference optimization, with advantages over current inference techniques shown on several benchmark data sets. However, this comes with the limitation of requiring additional computation over similar standard inference models. While we discussed the relevance of iterative inference models to hierarchical latent variable models, sequential latent variable models also contain empirical priors. In future work, we hope to apply iterative inference models to the online filtering setting, where fewer inference iterations, and thus less additional computation, may be required at each time step.
We would like to thank the reviewers as well as Peter Carr, Oisin Mac Aodha, Grant Van Horn, and Matteo Ruggero Ronchi for their insightful feedback. This research was supported in part by JPL PDF 1584398 and NSF 1564330.
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Appendix A Approximate Posterior Gradients for Latent Gaussian Models
a.1 Model & Variational Objective
Consider a latent variable model, , where the prior on is a factorized Gaussian density, , and the conditional likelihood, , depends on the type of data (e.g. Bernoulli for binary observations or Gaussian for continuous observations). We introduce an approximate posterior distribution, , which can be any parametric probability density defined over real values. Here, we assume that also takes the form of a factorized Gaussian density, . The objective during variational inference is to maximize w.r.t. the parameters of , i.e. and :
To solve this optimization problem, we will use the gradients and , which we now derive. The objective can be written as:
Plugging in and :
Since expectation and differentiation are linear operators, we can take the expectation and derivative of each term individually.
a.2 Gradient of the Log-Prior
We can write the log-prior as:
where is the dimensionality of . We want to evaluate the following terms:
To take these derivatives, we will use the reparameterization trick (Kingma & Welling, 2014; Rezende et al., 2014) to re-express , where is an auxiliary standard Gaussian variable, and denotes the element-wise product. We can now perform the expectations over , allowing us to bring the gradient operators inside the expectation brackets. The first term in eqs. 24 and 25 does not depend on or , so we can write:
To simplify notation, we define the following term:
We must now find and :
Putting everything together, we can express the gradients as:
a.3 Gradient of the Log-Approximate Posterior
We can write the log-approximate posterior as:
where is the dimensionality of . Again, we will use the reparameterization trick to re-express the gradients. However, notice what happens when plugging the reparameterized into the second term of eq. 37:
This term does not depend on or . Also notice that the first term in eq. 37 depends only on . Therefore, the gradient of the entire term w.r.t. is zero:
The gradient w.r.t. is
Note that the expectation has been dropped, as the term does not depend on the value of the sampled . Thus, the gradient of the entire term w.r.t. is:
a.4 Gradient of the Log-Conditional Likelihood
The form of the conditional likelihood will depend on the data, e.g. binary, discrete, continuous, etc. Here, we derive the gradient for Bernoulli (binary) and Gaussian (continuous) conditional likelihoods.
Bernoulli Output Distribution
The of a Bernoulli output distribution takes the form:
where is the mean of the output distribution. We drop the explicit dependence on and to simplify notation. We want to compute the gradients
where is re-expressed as function of and . Distributing the gradient operators yields:
Taking the partial derivatives and combining terms gives:
Gaussian Output Density
The of a Gaussian output density takes the form:
where is the mean of the output distribution and
is the variance. We assumeis not a function of to simplify the derivation, however, using is possible and would simply result in additional gradient terms in and . We want to compute the gradients
The first term in eqs. 52 and 53 is zero, since does not depend on or . To take the gradients, we will again use the reparameterization trick to re-express . We now implicitly express as . We can then write:
To simplify notation, we define the following term:
We must now find and :
Despite having different distribution forms, Bernoulli and Gaussian output distributions result in approximate posterior gradients of a similar form: the Jacobian of the output model multiplied by a weighted error term.
Putting the gradient terms from , , and together, we arrive at
Bernoulli Output Distribution:
Gaussian Output Distribution:
a.6 Approximate Posterior Gradients in Hierarchical Latent Variable Models
Hierarchical latent variable models factorize the latent variables over multiple levels, . Latent variables at higher levels provide empirical priors on latent variables at lower levels. Here, we assume a first-order Markov graphical structure, as shown in Figure 8, though more general structures are possible. For an intermediate latent level, we use the notation