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Found 9 papers, 0 papers with code
Variable-Complexity Weighted-Tempered Gibbs Samplers for Bayesian Variable Selection
However, the Rao-Backwellized estimator associated with this sampler has a high variance as the ratio between the signal dimension and the number of conditional PIP estimations is large.
Global Convergence Rate of Deep Equilibrium Models with General Activations
In a recent paper, Ling et al. investigated the over-parametrized Deep Equilibrium Model (DEQ) with ReLU activation.
Generative Adversarial Nets: Can we generate a new dataset based on only one training set?
In this work, we aim to generate a new dataset that has a different distribution from the training set.
On Rademacher Complexity-based Generalization Bounds for Deep Learning
We show that the Rademacher complexity-based approach can generate non-vacuous generalisation bounds on Convolutional Neural Networks (CNNs) for classifying a small number of classes of images.
Generalization Bounds on Multi-Kernel Learning with Mixed Datasets
This paper presents novel generalization bounds for the multi-kernel learning problem.
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity
We establish exact asymptotic expressions for the normalized mutual information and minimum mean-square-error (MMSE) of sparse linear regression in the sub-linear sparsity regime.
Replica Analysis of the Linear Model with Markov or Hidden Markov Signal Priors
This paper estimates free energy, average mutual information, and minimum mean square error (MMSE) of a linear model under two assumptions: (1) the source is generated by a Markov chain, (2) the source is generated via a hidden Markov model.
Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limits
The support recovery problem consists of determining a sparse subset of variables that is relevant in generating a set of observations.
