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Found 9 papers, 2 papers with code
Annealing between distributions by averaging moments
Many powerful Monte Carlo techniques for estimating partition functions, such as annealed importance sampling (AIS), are based on sampling from a sequence of intermediate distributions which interpolate between a tractable initial distribution and an intractable target distribution.
Testing MCMC code
Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively.
Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing
Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function.
Sandwiching the marginal likelihood using bidirectional Monte Carlo
Using the ground truth log-ML estimates obtained from our method, we quantitatively evaluate a wide variety of existing ML estimators on several latent variable models: clustering, a low rank approximation, and a binary attributes model.
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples.
The Reversible Residual Network: Backpropagation Without Storing Activations
Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider.
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.
On the Invertibility of Invertible Neural Networks
Guarantees in deep learning are hard to achieve due to the interplay of flexible modeling schemes and complex tasks.
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks
For two-layer ReLU neural networks (i. e. with one hidden layer), we prove that these two conditions do hold throughout the training under the assumptions that the inputs do not degenerate and the network is over-parameterized.
