research
∙
08/03/2023
Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models
Large Language Models (LLMs) demonstrate remarkable performance on a var...
share
research
∙
07/07/2023
Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization
This paper introduces a smooth method for (structured) sparsity in ℓ_q a...
research
∙
06/01/2023
A New PHO-rmula for Improved Performance of Semi-Structured Networks
Recent advances to combine structured regression models and deep neural ...
research
∙
05/25/2023
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction
Undersampling is a common method in Magnetic Resonance Imaging (MRI) to ...
research
∙
04/06/2023
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Bayesian inference in deep neural networks is challenging due to the hig...
research
∙
03/20/2023
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
While recent advances in large-scale foundational models show promising ...
research
∙
02/04/2023
mixdistreg: An R Package for Fitting Mixture of Experts Distributional Regression with Adaptive First-order Methods
This paper presents a high-level description of the R software package m...
research
∙
11/24/2022
Estimating Conditional Distributions with Neural Networks using R package deeptrafo
Contemporary empirical applications frequently require flexible regressi...
research
∙
11/17/2022
Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods
In this work, we propose an efficient implementation of mixtures of expe...
research
∙
11/04/2022
Uncertainty-aware predictive modeling for fair data-driven decisions
Both industry and academia have made considerable progress in developing...
research
∙
10/14/2022
Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models
Various privacy-preserving frameworks that respect the individual's priv...
research
∙
08/31/2022
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling
The autoregressive moving average (ARMA) model is a classical, and argua...
research
∙
05/28/2022
Additive Higher-Order Factorization Machines
In the age of big data and interpretable machine learning, approaches ne...
research
∙
05/25/2022
Factorized Structured Regression for Large-Scale Varying Coefficient Models
Recommender Systems (RS) pervade many aspects of our everyday digital li...
research
∙
02/16/2022
Cross-Modal Common Representation Learning with Triplet Loss Functions
Common representation learning (CRL) learns a shared embedding between t...
research
∙
02/14/2022
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens
Handwriting is one of the most frequently occurring patterns in everyday...
research
∙
02/12/2022
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis
Survival analysis (SA) is an active field of research that is concerned ...
research
∙
11/09/2021
Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach
Europe was hit by several, disastrous heat and drought events in recent ...
research
∙
10/21/2021
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation
The application of deep learning in survival analysis (SA) gives the opp...
research
∙
10/21/2021
Survival-oriented embeddings for improving accessibility to complex data structures
Deep learning excels in the analysis of unstructured data and recent adv...
research
∙
10/07/2021
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization
Componentwise boosting (CWB), also known as model-based boosting, is a v...
research
∙
09/12/2021
Automatic Componentwise Boosting: An Interpretable AutoML System
In practice, machine learning (ML) workflows require various different s...
research
∙
07/28/2021
Developing Open Source Educational Resources for Machine Learning and Data Science
Education should not be a privilege but a common good. It should be open...
research
∙
04/06/2021
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
This paper describes the implementation of semi-structured deep distribu...
research
∙
02/06/2021
Deep Semi-Supervised Learning for Time Series Classification
While Semi-supervised learning has gained much attention in computer vis...
research
∙
01/03/2021
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
During 2020, the infection rate of COVID-19 has been investigated by man...
research
∙
11/11/2020
Semi-Structured Deep Piecewise Exponential Models
We propose a versatile framework for survival analysis that combines adv...
research
∙
10/15/2020
Deep Conditional Transformation Models
Learning the cumulative distribution function (CDF) of an outcome variab...
research
∙
10/14/2020
Neural Mixture Distributional Regression
We present neural mixture distributional regression (NMDR), a holistic f...
research
∙
07/15/2020
Selective Inference for Additive and Linear Mixed Models
This work addresses the problem of conducting valid inference for additi...
research
∙
06/27/2020
A General Machine Learning Framework for Survival Analysis
The modeling of time-to-event data, also known as survival analysis, req...
research
∙
06/10/2020
A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction
Motivated by the application to German interest rates, we propose a time...
research
∙
02/13/2020
A Unifying Network Architecture for Semi-Structured Deep Distributional Learning
We propose a unifying network architecture for deep distributional learn...
research
∙
05/04/2018
Selective Inference for L_2-Boosting
We review several recently proposed post-selection inference frameworks ...
research
∙
05/04/2018
Valid Inference for L_2-Boosting
We review several recently proposed post-selection inference frameworks ...
research
∙
03/15/2018
Conditional Model Selection in Mixed-Effects Models with cAIC4
Model selection in mixed models based on the conditional distribution is...
research
∙
09/20/2016