AI News, NIPS Proceedingsβ

NIPS Proceedingsβ

Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014) Multiple lines of evidence support the notion that the brain performs probabilistic inference in multiple cognitive domains, including perception and decision making.

However, time becomes a fundamental bottleneck in such sampling-based probabilistic representations: the quality of inferences depends on how fast the neural circuit generates new, uncorrelated samples from its stationary distribution (the posterior).

Intriguingly, although a detailed balance of excitation and inhibition is dynamically maintained, detailed balance of Markov chain steps in the resulting sampler is violated, consistent with recent findings on how statistical irreversibility can overcome the speed limitation of random walks in other domains.

NIPS Proceedingsβ

Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014) Multiple lines of evidence support the notion that the brain performs probabilistic inference in multiple cognitive domains, including perception and decision making.

However, time becomes a fundamental bottleneck in such sampling-based probabilistic representations: the quality of inferences depends on how fast the neural circuit generates new, uncorrelated samples from its stationary distribution (the posterior).

Intriguingly, although a detailed balance of excitation and inhibition is dynamically maintained, detailed balance of Markov chain steps in the resulting sampler is violated, consistent with recent findings on how statistical irreversibility can overcome the speed limitation of random walks in other domains.

Adversarially Learned Inference

The adversarially learned inference (ALI) model is a deep directed generative model

which jointly learns a generation network and an inference network using an

What makes ALI unique is that unlike other approaches to learning inference in deep

making some mistakes in capturing exact object placement, color, style and (in

wasted to model trivial factors of variation in the input, and 2) the learned

The generator tries to mimic examples from a training dataset, which is sampled from

random source of noise received as input into a synthetic sample.

The discriminator receives a sample, but it is not told where the sample comes from.

Its job is to predict whether it is a data sample or a synthetic sample.

The discriminator is trained to make accurate predictions, and the generator is trained

Two marginal distributions are defined: The generator operates by sampling \(\mathbf{z} \sim p(\mathbf{z})\) and then

by the following value function: On one hand, the discriminator is trained to maximize the probability of correctly classifying

It can be shown that for a fixed generator, the optimal discriminator is and that given an optimal discriminator, minimizing the value function with respect

In other words, as training progresses, the generator produces synthetic samples that

Inference can loosely be defined as the answer to the following question: Given \(\mathbf{x}\), what \(\mathbf{z}\) is likely to have produced it?

data sample as input and produces a synthetic \(\mathbf{z}\) as output.

Expressed in probabilistic terms, ALI defines two joint distributions: ALI also modifies the discriminator’s goal.

by the following value function: In analogy to GAN, it can be shown that for a fixed generator, the optimal discriminator

is and that given an optimal discriminator, minimizing the value function with respect

\mid \mathbf{x}) \sim q(\mathbf{z} \mid \mathbf{x})\) and \(q(\mathbf{x}

Regarding reconstructions: odd columns are validation set examples, even columns

As a sanity check for overfitting, we look at latent space interpolations between

We observe smooth transitions between pairs of example, and intermediary images remain

We apply the conditional version of ALI to CelebA using the dataset’s 40 binary attributes.

We observe how a single element of the latent space z changes with

held-out validation set is taken from the training set and is used for model

we achieve a misclassification rate that is roughly 3.00 ± 0.50% lower than

When label information is available for \(q(x, z)\) samples, the discriminator

the discriminator is expected to predict \(K + 1\) for \(p(x, z)\) samples

that ALI did not require feature matching to achieve comparable results.

To highlight the role of the inference network during learning, we performed an experiment

been chosen such that the distribution exhibits lots of modes separated by large

low-probability regions, which makes it a decently hard task despite the 2D

Each model was trained 10 times using Adam with random learning rate and

We measured the extent to which the trained models covered all 25 modes by drawing

Using this definition, we found that ALI models covered 13.4 ± 5.8 modes on

average (min: 8, max: 25) while GAN models covered 10.4 ± 9.2 modes on average

with an encoder using the following procedures: The encoders learned for GAN inference have the same architecture as ALI’s encoder.

We observe the following: In summary, this experiment provides evidence that adversarial training benefits from

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