Search Results for author:
Found 17 papers, 7 papers with code
Probing the Robustness of Time-series Forecasting Models with CounterfacTS
Because most of the training data does not reflect such changes, the models present poor performance on the new out-of-distribution scenarios and, therefore, the impact of such events cannot be reliably anticipated ahead of time.
Lecture Notes in Probabilistic Diffusion Models
The diffusion model learns the data manifold to which the original and thus the reconstructed data samples belong, by training on a large number of data points.
Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders
An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e. g., a user's gender or age should not influence the model.
A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process
With ModularHI, the operator chooses which sensor inputs are available, and then ModularHI will compute a baseline model based on data collected during a burn-in state.
Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities
Several explanation methods have been developed, but they do not provide mechanisms for users to interact with the explanations.
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles
The ensembled prediction intervals are aggregated as a split normal mixture accounting for possible multimodality and asymmetricity of the posterior predictive distribution, and resulting in prediction intervals that capture aleatoric and epistemic uncertainty.
Learning similarity measures from data
The main motivation for this work is to automate the construction of similarity measures using machine learning, while keeping training time as low as possible.
Probabilistic Models with Deep Neural Networks
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling.
Forecasting Intra-Hour Imbalances in Electric Power Systems
Keeping the electricity production in balance with the actual demand is becoming a difficult and expensive task in spite of an involvement of experienced human operators.
Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)
This work addresses the challenges related to attacks on collaborative tagging systems, which often comes in a form of malicious annotations or profile injection attacks.
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention.
Detecting Offensive Language in Tweets Using Deep Learning
This paper addresses the important problem of discerning hateful content in social media.
A Deep Network Model for Paraphrase Detection in Short Text Messages
Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus.
Bayesian Models of Data Streams with Hierarchical Power Priors
Making inferences from data streams is a pervasive problem in many modern data analysis applications.
Inter-Session Modeling for Session-Based Recommendation
In recent years, research has been done on applying Recurrent Neural Networks (RNNs) as recommender systems.
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data.
