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Found 11 papers, 3 papers with code
Implicit Regularization for Group Sparsity
We study the implicit regularization of gradient descent towards structured sparsity via a novel neural reparameterization, which we call a diagonally grouped linear neural network.
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
In this paper, we study the implicit bias of gradient descent for sparse regression.
Provable Compressed Sensing with Generative Priors via Langevin Dynamics
Deep generative models have emerged as a powerful class of priors for signals in various inverse problems such as compressed sensing, phase retrieval and super-resolution.
Active learning of deep surrogates for PDEs: Application to metasurface design
Surrogate models for partial-differential equations are widely used in the design of meta-materials to rapidly evaluate the behavior of composable components.
Learning Robust Models for e-Commerce Product Search
Showing items that do not match search query intent degrades customer experience in e-commerce.
Nano-Material Configuration Design with Deep Surrogate Langevin Dynamics
We consider the problem of optimizing by sampling under multiple black-box constraints in nano-material design.
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis
Starting from a randomly initialized autoencoder network, we rigorously prove the linear convergence of gradient descent in two learning regimes, namely: (i) the weakly-trained regime where only the encoder is trained, and (ii) the jointly-trained regime where both the encoder and the decoder are trained.
Autoencoders Learn Generative Linear Models
For each of these models, we prove that under suitable choices of hyperparameters, architectures, and initialization, autoencoders learned by gradient descent can successfully recover the parameters of the corresponding model.
On Learning Sparsely Used Dictionaries from Incomplete Samples
Second, we propose an initialization algorithm that utilizes a small number of extra fully observed samples to produce such a coarse initial estimate.
A Forward-Backward Approach for Visualizing Information Flow in Deep Networks
We introduce a new, systematic framework for visualizing information flow in deep networks.
Provably Accurate Double-Sparse Coding
To our knowledge, our work introduces the first computationally efficient algorithm for double-sparse coding that enjoys rigorous statistical guarantees.
