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08/21/2023
Image-free Classifier Injection for Zero-Shot Classification
Zero-shot learning models achieve remarkable results on image classifica...
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07/20/2023
Addressing caveats of neural persistence with deep graph persistence
Neural Persistence is a prominent measure for quantifying neural network...
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05/29/2023
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
Computing properties of molecular systems rely on estimating expectation...
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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...
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02/23/2023
Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks
In recent years, large strides have been taken in developing machine lea...
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01/29/2023
Unifying Molecular and Textual Representations via Multi-task Language Modelling
The recent advances in neural language models have also been successfull...
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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...
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09/23/2022
Variational Open-Domain Question Answering
We introduce the Variational Open-Domain (VOD) framework for end-to-end ...
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09/10/2022
Explainable Image Quality Assessments in Teledermatological Photography
Image quality is a crucial factor in the success of teledermatological c...
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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...
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07/20/2022
NeuralNEB – Neural Networks can find Reaction Paths Fast
Quantum mechanical methods like Density Functional Theory (DFT) are used...
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07/17/2022
Can large language models reason about medical questions?
Although large language models (LLMs) often produce impressive outputs, ...
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05/30/2022
Few-Shot Diffusion Models
Denoising diffusion probabilistic models (DDPM) are powerful hierarchica...
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04/18/2022
Inductive Biases for Object-Centric Representations of Complex Textures
Understanding which inductive biases could be useful for the unsupervise...
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03/17/2022
Image Super-Resolution With Deep Variational Autoencoders
Image super-resolution (SR) techniques are used to generate a high-resol...
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02/14/2022
DermX: an end-to-end framework for explainable automated dermatological diagnosis
Dermatological diagnosis automation is essential in addressing the high ...
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10/23/2021
Hierarchical Few-Shot Generative Models
A few-shot generative model should be able to generate data from a distr...
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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...
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07/12/2021
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning
Learning data representations that are useful for various downstream tas...
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07/01/2021
Generalization and Robustness Implications in Object-Centric Learning
The idea behind object-centric representation learning is that natural s...
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10/27/2020
On the Transfer of Disentangled Representations in Realistic Settings
Learning meaningful representations that disentangle the underlying stru...
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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...
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07/06/2020
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative m...
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02/06/2020
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Flow models have recently made great progress at modeling quantized sens...
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09/25/2019
LAVAE: Disentangling Location and Appearance
We propose a probabilistic generative model for unsupervised learning of...
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02/06/2019
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
With the introduction of the variational autoencoder (VAE), probabilisti...
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12/18/2018
Attend, Copy, Parse - End-to-end information extraction from documents
Document information extraction tasks performed by humans create data co...
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11/21/2017
Recurrent Relational Networks for Complex Relational Reasoning
Humans possess an ability to abstractly reason about objects and their i...
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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...
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09/12/2017
Hash Embeddings for Efficient Word Representations
We present hash embeddings, an efficient method for representing words i...
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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...
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07/16/2017
End-to-End Information Extraction without Token-Level Supervision
Most state-of-the-art information extraction approaches rely on token-le...
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04/03/2017
Semi-Supervised Generation with Cluster-aware Generative Models
Deep generative models trained with large amounts of unlabelled data hav...
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10/20/2016
Neural Machine Translation with Characters and Hierarchical Encoding
Most existing Neural Machine Translation models use groups of characters...
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05/24/2016
Sequential Neural Models with Stochastic Layers
How can we efficiently propagate uncertainty in a latent state represent...
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04/07/2016
An Adaptive Resample-Move Algorithm for Estimating Normalizing Constants
The estimation of normalizing constants is a fundamental step in probabi...
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02/17/2016
Auxiliary Deep Generative Models
Deep generative models parameterized by neural networks have recently ac...
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02/06/2016
Ladder Variational Autoencoders
Variational Autoencoders are powerful models for unsupervised learning. ...
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12/31/2015
Autoencoding beyond pixels using a learned similarity metric
We present an autoencoder that leverages learned representations to bett...
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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...
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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...
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03/06/2015
Convolutional LSTM Networks for Subcellular Localization of Proteins
Machine learning is widely used to analyze biological sequence data. Non...
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01/18/2015
Deep Belief Nets for Topic Modeling
Applying traditional collaborative filtering to digital publishing is ch...
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12/25/2014
Protein Secondary Structure Prediction with Long Short Term Memory Networks
Prediction of protein secondary structure from the amino acid sequence i...
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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...
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01/12/2013
Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
Expectation Propagation (EP) provides a framework for approximate infere...
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02/22/2011
