HAXHOLLI , Etrit

Density estimation is the statistical process of constructing a probabilistic model that represents the distribution of a given dataset.By estimating this distribution, we can better understand the statistics and behavior of our data, enhancing predictions, anomaly detection, and data generation. Density estimation thus forms a crucial step in...

Density estimation is the statistical process of constructing a probabilistic model that represents the distribution of a given dataset.By estimating this distribution, we can better understand the statistics and behavior of our data, enhancing predictions, anomaly detection, and data generation. Density estimation thus forms a crucial step in...

Density estimation is the statistical process of constructing a probabilistic model that represents the distribution of a given dataset.By estimating this distribution, we can better understand the statistics and behavior of our data, enhancing predictions, anomaly detection, and data generation. Density estimation thus forms a crucial step in...

Normalizing flows based on neural ODEs, as implemented in FFJORD, provide a powerful theoretical framework for density estimation and data generation. While the neural ODE formulation enables us to calculate the determinants of free form Jacobians in O(D) time, the flexibility of the transformation underlying neural ODEs has been shown to be...

The study of loss function distributions is critical to characterize a model's behaviour on a given machine learning problem. For example, while the quality of a model is commonly determined by the average loss assessed on a testing set, this quantity does not reflect the existence of the true mean of the loss distribution. Indeed, the...

The modeling of the score evolution by a single time-dependent neural network in Diffusion Probabilistic Models (DPMs) requires long training periods and potentially reduces modeling flexibility and capacity. In order to mitigate such shortcomings, we propose to leverage the independence of the learning tasks at different time points in DPMs....

The study of loss-function distributions is critical to characterize a model's behaviour on a given machine-learning problem. While model quality is commonly measured by the average loss assessed on a testing set, this quantity does not ascertain the existence of the mean of the loss distribution. Conversely, the existence of a distribution's...

While the neural ODE formulation of normalizing flows such as in FFJORD enables us to calculate the determinants of free form Jacobians in O(D) time, the flexibility of the transformation underlying neural ODEs has been shown to be suboptimal. In this paper, we present AFFJORD, a neural ODE-based normalizing flow which enhances the...