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Found 14 papers, 0 papers with code
Optimal time sampling in physics-informed neural networks
Physics-informed neural networks (PINN) is a extremely powerful paradigm used to solve equations encountered in scientific computing applications.
Transformer for Times Series: an Application to the S&P500
The transformer models have been extensively used with good results in a wide area of machine learning applications including Large Language Models and image generation.
On the Convergence Rate of the Stochastic Gradient Descent (SGD) and application to a modified policy gradient for the Multi Armed Bandit
We present a self-contained proof of the convergence rate of the Stochastic Gradient Descent (SGD) when the learning rate follows an inverse time decays schedule; we next apply the results to the convergence of a modified form of policy gradient Multi-Armed Bandit (MAB) with $L2$ regularization.
Onflow: an online portfolio allocation algorithm
We introduce Onflow, a reinforcement learning technique that enables online optimization of portfolio allocation policies based on gradient flows.
High order universal portfolios
The Cover universal portfolio (UP from now on) has many interesting theoretical and numerical properties and was investigated for a long time.
Deep Conditional Measure Quantization
Quantization of a probability measure means representing it with a finite set of Dirac masses that approximates the input distribution well enough (in some metric space of probability measures).
Huber-energy measure quantization
We describe a measure quantization procedure i. e., an algorithm which finds the best approximation of a target probability law (and more generally signed finite variation measure) by a sum of $Q$ Dirac masses ($Q$ being the quantization parameter).
Diversity in deep generative models and generative AI
The decoder-based machine learning generative algorithms such as Generative Adversarial Networks (GAN), Variational Auto-Encoders (VAE), Transformers show impressive results when constructing objects similar to those in a training ensemble.
Algorithms that get old : the case of generative deep neural networks
Generative deep neural networks used in machine learning, like the Variational Auto-Encoders (VAE), and Generative Adversarial Networks (GANs) produce new objects each time when asked to do so with the constraint that the new objects remain similar to some list of examples given as input.
Convergence dynamics of Generative Adversarial Networks: the dual metric flows
Fitting neural networks often resorts to stochastic (or similar) gradient descent which is a noise-tolerant (and efficient) resolution of a gradient descent dynamics.
Architectures of epidemic models: accommodating constraints from empirical and clinical data
Deterministic compartmental models have been used extensively in modeling epidemic propagation.
Stochastic Runge-Kutta methods and adaptive SGD-G2 stochastic gradient descent
The minimization of the loss function is of paramount importance in deep neural networks.
Radon Sobolev Variational Auto-Encoders
The quality of generative models (such as Generative adversarial networks and Variational Auto-Encoders) depends heavily on the choice of a good probability distance.
Stochastic learning control of inhomogeneous quantum ensembles
In quantum control, the robustness with respect to uncertainties in the system's parameters or driving field characteristics is of paramount importance and has been studied theoretically, numerically and experimentally.
