b'Ole Winther'

research

∙ 08/21/2023

Image-free Classifier Injection for Zero-Shot Classification

Zero-shot learning models achieve remarkable results on image classifica...

0 Anders Christensen, et al. ∙

research

∙ 07/20/2023

Addressing caveats of neural persistence with deep graph persistence

Neural Persistence is a prominent measure for quantifying neural network...

0 Leander Girrbach, et al. ∙

research

∙ 05/29/2023

Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics

Computing properties of molecular systems rely on estimating expectation...

0 Mathias Schreiner, et al. ∙

research

∙ 05/29/2023

Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation

Generative models have had a profound impact on vision and language, pav...

0 Giorgio Giannone, et al. ∙

research

∙ 02/23/2023

Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks

In recent years, large strides have been taken in developing machine lea...

0 Raluca Jalaboi, et al. ∙

research

∙ 01/29/2023

Unifying Molecular and Textual Representations via Multi-task Language Modelling

The recent advances in neural language models have also been successfull...

26 Dimitrios Christofidellis, et al. ∙

research

∙ 01/27/2023

ThoughtSource: A central hub for large language model reasoning data

Large language models (LLMs) such as GPT-3 and ChatGPT have recently dem...

0 Simon Ott, et al. ∙

research

∙ 09/23/2022

Variational Open-Domain Question Answering

We introduce the Variational Open-Domain (VOD) framework for end-to-end ...

0 Valentin Liévin, et al. ∙

research

∙ 09/10/2022

Explainable Image Quality Assessments in Teledermatological Photography

Image quality is a crucial factor in the success of teledermatological c...

0 Raluca Jalaboi, et al. ∙

research

∙ 07/25/2022

Transition1x – a Dataset for Building Generalizable Reactive Machine Learning Potentials

Machine Learning (ML) models have, in contrast to their usefulness in mo...

0 Mathias Schreiner, et al. ∙

research

∙ 07/20/2022

NeuralNEB – Neural Networks can find Reaction Paths Fast

Quantum mechanical methods like Density Functional Theory (DFT) are used...

0 Mathias Schreiner, et al. ∙

research

∙ 07/17/2022

Can large language models reason about medical questions?

Although large language models (LLMs) often produce impressive outputs, ...

0 Valentin Liévin, et al. ∙

research

∙ 05/30/2022

Few-Shot Diffusion Models

Denoising diffusion probabilistic models (DDPM) are powerful hierarchica...

3 Giorgio Giannone, et al. ∙

research

∙ 04/18/2022

Inductive Biases for Object-Centric Representations of Complex Textures

Understanding which inductive biases could be useful for the unsupervise...

0 Samuele Papa, et al. ∙

research

∙ 03/17/2022

Image Super-Resolution With Deep Variational Autoencoders

Image super-resolution (SR) techniques are used to generate a high-resol...

0 Darius Chira, et al. ∙

research

∙ 02/14/2022

DermX: an end-to-end framework for explainable automated dermatological diagnosis

Dermatological diagnosis automation is essential in addressing the high ...

6 Raluca Jalaboi, et al. ∙

research

∙ 10/23/2021

Hierarchical Few-Shot Generative Models

A few-shot generative model should be able to generate data from a distr...

4 Giorgio Giannone, et al. ∙

research

∙ 07/13/2021

Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks

Data-driven methods based on machine learning have the potential to acce...

0 Jonas Busk, et al. ∙

research

∙ 07/12/2021

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning

Learning data representations that are useful for various downstream tas...

48 Andrea Dittadi, et al. ∙

research

∙ 07/01/2021

Generalization and Robustness Implications in Object-Centric Learning

The idea behind object-centric representation learning is that natural s...

1 Andrea Dittadi, et al. ∙

research

∙ 10/27/2020

On the Transfer of Disentangled Representations in Realistic Settings

Learning meaningful representations that disentangle the underlying stru...

24 Andrea Dittadi, et al. ∙

research

∙ 08/05/2020

Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds

This paper introduces novel results for the score function gradient esti...

6 Valentin Liévin, et al. ∙

research

∙ 07/06/2020

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Normalizing flows and variational autoencoders are powerful generative m...

12 Didrik Nielsen, et al. ∙

research

∙ 02/06/2020

Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow

Flow models have recently made great progress at modeling quantized sens...

0 Didrik Nielsen, et al. ∙

research

∙ 09/25/2019

LAVAE: Disentangling Location and Appearance

We propose a probabilistic generative model for unsupervised learning of...

7 Andrea Dittadi, et al. ∙

research

∙ 02/06/2019

BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

With the introduction of the variational autoencoder (VAE), probabilisti...

8 Lars Maaløe, et al. ∙

research

∙ 12/18/2018

Attend, Copy, Parse - End-to-end information extraction from documents

Document information extraction tasks performed by humans create data co...

0 Rasmus Berg Palm, et al. ∙

research

∙ 11/21/2017

Recurrent Relational Networks for Complex Relational Reasoning

Humans possess an ability to abstractly reason about objects and their i...

0 Rasmus Berg Palm, et al. ∙

research

∙ 10/16/2017

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

This paper takes a step towards temporal reasoning in a dynamically chan...

0 Marco Fraccaro, et al. ∙

research

∙ 09/12/2017

Hash Embeddings for Efficient Word Representations

We present hash embeddings, an efficient method for representing words i...

0 Dan Svenstrup, et al. ∙

research

∙ 08/24/2017

CloudScan - A configuration-free invoice analysis system using recurrent neural networks

We present CloudScan; an invoice analysis system that requires zero conf...

0 Rasmus Berg Palm, et al. ∙

research

∙ 07/16/2017

End-to-End Information Extraction without Token-Level Supervision

Most state-of-the-art information extraction approaches rely on token-le...

0 Rasmus Berg Palm, et al. ∙

research

∙ 04/03/2017

Semi-Supervised Generation with Cluster-aware Generative Models

Deep generative models trained with large amounts of unlabelled data hav...

0 Lars Maaløe, et al. ∙

research

∙ 10/20/2016

Neural Machine Translation with Characters and Hierarchical Encoding

Most existing Neural Machine Translation models use groups of characters...

0 Alexander Rosenberg Johansen, et al. ∙

research

∙ 05/24/2016

Sequential Neural Models with Stochastic Layers

How can we efficiently propagate uncertainty in a latent state represent...

0 Marco Fraccaro, et al. ∙

research

∙ 04/07/2016

An Adaptive Resample-Move Algorithm for Estimating Normalizing Constants

The estimation of normalizing constants is a fundamental step in probabi...

0 Marco Fraccaro, et al. ∙

research

∙ 02/17/2016

Auxiliary Deep Generative Models

Deep generative models parameterized by neural networks have recently ac...

0 Lars Maaløe, et al. ∙

research

∙ 02/06/2016

Ladder Variational Autoencoders

Variational Autoencoders are powerful models for unsupervised learning. ...

0 Casper Kaae Sønderby, et al. ∙

research

∙ 12/31/2015

Autoencoding beyond pixels using a learned similarity metric

We present an autoencoder that leverages learned representations to bett...

0 Anders Boesen Lindbo Larsen, et al. ∙

research

∙ 09/15/2015

Bayesian inference for spatio-temporal spike-and-slab priors

In this work, we address the problem of solving a series of underdetermi...

0 Michael Riis Andersen, et al. ∙

research

∙ 08/19/2015

Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems

We are interested in solving the multiple measurement vector (MMV) probl...

0 Michael Riis Andersen, et al. ∙

research

∙ 03/06/2015

Convolutional LSTM Networks for Subcellular Localization of Proteins

Machine learning is widely used to analyze biological sequence data. Non...

0 Søren Kaae Sønderby, et al. ∙

research

∙ 01/18/2015

Deep Belief Nets for Topic Modeling

Applying traditional collaborative filtering to digital publishing is ch...

0 Lars Maaløe, et al. ∙

research

∙ 12/25/2014

Protein Secondary Structure Prediction with Long Short Term Memory Networks

Prediction of protein secondary structure from the amino acid sequence i...

0 Søren Kaae Sønderby, et al. ∙

research

∙ 12/23/2014

Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models

The future predictive performance of a Bayesian model can be estimated u...

0 Aki Vehtari, et al. ∙

research

∙ 01/12/2013

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models

Expectation Propagation (EP) provides a framework for approximate infere...

0 Manfred Opper, et al. ∙

research

∙ 02/22/2011

Predictive Active Set Selection Methods for Gaussian Processes

We propose an active set selection framework for Gaussian process classi...

0 Ricardo Henao, et al. ∙

Ole Winther

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