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Found 13 papers, 2 papers with code
Non-Denoising Forward-Time Diffusions
The scope of this paper is generative modeling through diffusion processes.
Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling
The dynamic Schr\"odinger bridge problem seeks a stochastic process that defines a transport between two target probability measures, while optimally satisfying the criteria of being closest, in terms of Kullback-Leibler divergence, to a reference process.
Large-width asymptotics for ReLU neural networks with $α$-Stable initializations
As a difference with respect to the Gaussian setting, our result shows that the choice of the activation function affects the scaling of the NN, that is: to achieve the infinitely wide $\alpha$-Stable process, the ReLU activation requires an additional logarithmic term in the scaling with respect to sub-linear activations.
Deep Stable neural networks: large-width asymptotics and convergence rates
Then, we establish sup-norm convergence rates of the rescaled deep Stable NN to the Stable SP, under the ``joint growth" and a ``sequential growth" of the width over the NN's layers.
Learning Stochastic Optimal Policies via Gradient Descent
We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies.
Large-width functional asymptotics for deep Gaussian neural networks
In this paper, we consider fully connected feed-forward deep neural networks where weights and biases are independent and identically distributed according to Gaussian distributions.
Learning-augmented count-min sketches via Bayesian nonparametrics
Under this more general framework, we apply the arguments of the ``Bayesian" proof of the CMS-DP, suitably adapted to the PYP prior, in order to compute the posterior distribution of a point query, given the hashed data.
A Bayesian nonparametric approach to count-min sketch under power-law data streams
The count-min sketch (CMS) is a randomized data structure that provides estimates of tokens' frequencies in a large data stream using a compressed representation of the data by random hashing.
Infinite-channel deep stable convolutional neural networks
The interplay between infinite-width neural networks (NNs) and classes of Gaussian processes (GPs) is well known since the seminal work of Neal (1996).
Doubly infinite residual neural networks: a diffusion process approach
Our results highlight a limited expressive power of doubly infinite ResNets when the unscaled network's parameters are i. i. d.
Stable behaviour of infinitely wide deep neural networks
We consider fully connected feed-forward deep neural networks (NNs) where weights and biases are independent and identically distributed as symmetric centered stable distributions.
An empirical study of pretrained representations for few-shot classification
Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model.
Infinitely deep neural networks as diffusion processes
When the parameters are independently and identically distributed (initialized) neural networks exhibit undesirable properties that emerge as the number of layers increases, e. g. a vanishing dependency on the input and a concentration on restrictive families of functions including constant functions.
