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Found 32 papers, 14 papers with code
S$Ω$I: Score-based O-INFORMATION Estimation
The analysis of scientific data and complex multivariate systems requires information quantities that capture relationships among multiple random variables.
Data Augmentation for Traffic Classification
Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance.
Toward Generative Data Augmentation for Traffic Classification
Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance.
MINDE: Mutual Information Neural Diffusion Estimation
In this work we present a new method for the estimation of Mutual Information (MI) between random variables.
Multi-modal Latent Diffusion
Multi-modal data-sets are ubiquitous in modern applications, and multi-modal Variational Autoencoders are a popular family of models that aim to learn a joint representation of the different modalities.
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images.
Continuous-Time Functional Diffusion Processes
We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces.
Ranked #21 on
Image Generation
on CelebA 64x64
"It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning
Some tasks may benefit from being learned together while others may be detrimental to one another.
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data.
Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning
When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and be capable of adapting its actions on-the-fly to changing conditions.
Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios
When learning to behave in a stochastic environment where safety is critical, such as driving a vehicle in traffic, it is natural for human drivers to plan fallback strategies as a backup to use if ever there is an unexpected change in the environment.
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?
In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets.
Improved optimization strategies for deep Multi-Task Networks
In Multi-Task Learning (MTL), it is a common practice to train multi-task networks by optimizing an objective function, which is a weighted average of the task-specific objective functions.
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
We revisit the theoretical properties of Hamiltonian stochastic differential equations (SDES) for Bayesian posterior sampling, and we study the two types of errors that arise from numerical SDE simulation: the discretization error and the error due to noisy gradient estimates in the context of data subsampling.
Model Selection for Bayesian Autoencoders
We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization.
Parametric Bootstrap Ensembles as Variational Inference
In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference.
Sparse within Sparse Gaussian Processes using Neighbor Information
In this work, we address one limitation of sparse GPs, which is due to the challenge in dealing with a large number of inducing variables without imposing a special structure on the inducing inputs.
An Identifiable Double VAE For Disentangled Representations
A large part of the literature on learning disentangled representations focuses on variational autoencoders (VAE).
USAD: UnSupervised Anomaly Detection on Multivariate Time Series
Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.
Maximum Roaming Multi-Task Learning
Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance.
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
In this work we define a unified mathematical framework to deepen our understanding of the role of stochastic gradient (SG) noise on the behavior of Markov chain Monte Carlo sampling (SGMCMC) algorithms.
A Variational View on Bootstrap Ensembles as Bayesian Inference
In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference.
Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting
Accurate travel products price forecasting is a highly desired feature that allows customers to take informed decisions about purchases, and companies to build and offer attractive tour packages.
Applications
LIBRE: Learning Interpretable Boolean Rule Ensembles
We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules.
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD
Large scale machine learning is increasingly relying on distributed optimization, whereby several machines contribute to the training process of a statistical model.
A comparative evaluation of novelty detection algorithms for discrete sequences
The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection.
Good Initializations of Variational Bayes for Deep Models
Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification.
Calibrating Deep Convolutional Gaussian Processes
The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions.
Flexible Scheduling of Distributed Analytic Applications
This work addresses the problem of scheduling user-defined analytic applications, which we define as high-level compositions of frameworks, their components, and the logic necessary to carry out work.
Distributed, Parallel, and Cluster Computing
Random Feature Expansions for Deep Gaussian Processes
The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty.
Measuring Password Strength: An Empirical Analysis
We present an in-depth analysis on the strength of the almost 10, 000 passwords from users of an instant messaging server in Italy.
Cryptography and Security
