University of Amsterdam

research

∙ 09/19/2019

HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities

Multimodal datasets contain an enormous amount of relational information...

Devanshu Arya, et al. ∙

research

∙ 10/07/2019

Increasing Expressivity of a Hyperspherical VAE

Learning suitable latent representations for observed, high-dimensional ...

Tim R. Davidson, et al. ∙

research

∙ 04/02/2019

Cooperative Embeddings for Instance, Attribute and Category Retrieval

The goal of this paper is to retrieve an image based on instance, attrib...

William Thong, et al. ∙

research

∙ 04/30/2020

How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking

Attribution methods assess the contribution of inputs (e.g., words) to t...

Nicola De Cao, et al. ∙

research

∙ 02/07/2022

Message Passing Neural PDE Solvers

The numerical solution of partial differential equations (PDEs) is diffi...

Johannes Brandstetter, et al. ∙

research

∙ 03/30/2022

Weakly supervised causal representation learning

Learning high-level causal representations together with a causal model ...

Johann Brehmer, et al. ∙

research

∙ 06/13/2022

Compositional Mixture Representations for Vision and Text

Learning a common representation space between vision and language allow...

Stephan Alaniz, et al. ∙

research

∙ 05/11/2020

Conditional Image Generation and Manipulation for User-Specified Content

In recent years, Generative Adversarial Networks (GANs) have improved st...

David Stap, et al. ∙

research

∙ 11/18/2021

Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction

Magnetic Resonance Imaging can produce detailed images of the anatomy an...

George Yiasemis, et al. ∙

research

∙ 11/29/2019

Learning Likelihoods with Conditional Normalizing Flows

Normalizing Flows (NFs) are able to model complicated distributions p(y)...

Christina Winkler, et al. ∙

research

∙ 06/21/2018

Learning to Rank from Samples of Variable Quality

Training deep neural networks requires many training samples, but in pra...

Mostafa Dehghani, et al. ∙

research

∙ 05/13/2019

VideoGraph: Recognizing Minutes-Long Human Activities in Videos

Many human activities take minutes to unfold. To represent them, related...

Noureldien Hussein, et al. ∙

research

∙ 12/07/2018

Color Constancy by GANs: An Experimental Survey

In this paper, we formulate the color constancy task as an image-to-imag...

Partha Das, et al. ∙

research

∙ 04/03/2020

TimeGate: Conditional Gating of Segments in Long-range Activities

When recognizing a long-range activity, exploring the entire video is ex...

Noureldien Hussein, et al. ∙

research

∙ 08/17/2021

Deep MRI Reconstruction with Radial Subsampling

In spite of its extensive adaptation in almost every medical diagnostic ...

George Yiasemis, et al. ∙

research

∙ 01/26/2022

An Assessment of the Impact of OCR Noise on Language Models

Neural language models are the backbone of modern-day natural language p...

Konstantin Todorov, et al. ∙

research

∙ 09/03/2020

Physics-based Shading Reconstruction for Intrinsic Image Decomposition

We investigate the use of photometric invariance and deep learning to co...

Anil S. Baslamisli, et al. ∙

research

∙ 08/08/2021

Skeleton-Contrastive 3D Action Representation Learning

This paper strives for self-supervised learning of a feature space suita...

Fida Mohammad Thoker, et al. ∙

research

∙ 09/11/2021

Making Table Understanding Work in Practice

Understanding the semantics of tables at scale is crucial for tasks like...

Madelon Hulsebos, et al. ∙

research

∙ 04/09/2019

Block Neural Autoregressive Flow

Normalising flows (NFS) map two density functions via a differentiable b...

Nicola De Cao, et al. ∙

research

∙ 11/19/2019

Open Cross-Domain Visual Search

This paper introduces open cross-domain visual search, where categories ...

William Thong, 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...

Didrik Nielsen, et al. ∙

research

∙ 05/24/2019

DIVA: Domain Invariant Variational Autoencoders

We consider the problem of domain generalization, namely, how to learn r...

Maximilian Ilse, et al. ∙

research

∙ 07/15/2021

Variational Topic Inference for Chest X-Ray Report Generation

Automating report generation for medical imaging promises to reduce work...

Ivona Najdenkoska, et al. ∙

research

∙ 01/10/2022

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

Recommender Systems (RSs) aim to model and predict the user preference w...

Kosar Seyedhoseinzadeh, et al. ∙

research

∙ 09/10/2018

Probabilistic Binary Neural Networks

Low bit-width weights and activations are an effective way of combating ...

Jorn W. T. Peters, et al. ∙

research

∙ 03/17/2020

Feedback Graph Convolutional Network for Skeleton-based Action Recognition

Skeleton-based action recognition has attracted considerable attention i...

Hao Yang, et al. ∙

research

∙ 04/01/2020

High-Performance Long-Term Tracking with Meta-Updater

Long-term visual tracking has drawn increasing attention because it is m...

Kenan Dai, et al. ∙

research

∙ 06/02/2020

The Convolution Exponential and Generalized Sylvester Flows

This paper introduces a new method to build linear flows, by taking the ...

Emiel Hoogeboom, et al. ∙

research

∙ 08/14/2020

PointMixup: Augmentation for Point Clouds

This paper introduces data augmentation for point clouds by interpolatio...

Yunlu Chen, et al. ∙

research

∙ 04/26/2021

GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization

Traditional video summarization methods generate fixed video representat...

Jia-Hong Huang, et al. ∙

research

∙ 08/18/2019

RefNet: A Reference-aware Network for Background Based Conversation

Existing conversational systems tend to generate generic responses. Rece...

Chuan Meng, et al. ∙

research

∙ 03/28/2020

Actor-Transformers for Group Activity Recognition

This paper strives to recognize individual actions and group activities ...

Kirill Gavrilyuk, et al. ∙

research

∙ 10/30/2020

Experimental design for MRI by greedy policy search

In today's clinical practice, magnetic resonance imaging (MRI) is routin...

Tim Bakker, et al. ∙

research

∙ 11/11/2021

Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling

In-Stent Restenosis is a recurrence of coronary artery narrowing due to ...

Dongwei Ye, et al. ∙

research

∙ 06/06/2019

Combining Generative and Discriminative Models for Hybrid Inference

A graphical model is a structured representation of the data generating ...

Victor Garcia Satorras, et al. ∙

research

∙ 08/09/2020

Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search

Recent research on conversational search highlights the importance of mi...

Antonios Minas Krasakis, et al. ∙

research

∙ 11/11/2021

Improving Novelty Detection using the Reconstructions of Nearest Neighbours

We show that using nearest neighbours in the latent space of autoencoder...

Michael Mesarcik, et al. ∙

research

∙ 04/20/2022

NFormer: Robust Person Re-identification with Neighbor Transformer

Person re-identification aims to retrieve persons in highly varying sett...

Haochen Wang, et al. ∙

research

∙ 06/27/2022

Continual Learning of Dynamical Systems with Competitive Federated Reservoir Computing

Machine learning recently proved efficient in learning differential equa...

Leonard Bereska, et al. ∙

research

∙ 02/02/2019

A Layer-Based Sequential Framework for Scene Generation with GANs

The visual world we sense, interpret and interact everyday is a complex ...

Mehmet Ozgur Turkoglu, et al. ∙

research

∙ 05/17/2019

Integer Discrete Flows and Lossless Compression

Lossless compression methods shorten the expected representation size of...

Emiel Hoogeboom, et al. ∙

research

∙ 07/10/2018

Universal Transformers

Self-attentive feed-forward sequence models have been shown to achieve i...

Mostafa Dehghani, et al. ∙

research

∙ 12/05/2018

Unsupervised Generation of Optical Flow Datasets from Videos in the Wild

Dense optical flow ground truth of non-rigid motion for real-world image...

Hoang-An Le, et al. ∙

research

∙ 12/09/2019

ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

In general, intrinsic image decomposition algorithms interpret shading a...

Anil S. Baslamisli, et al. ∙

research

∙ 08/06/2021

Feature-Supervised Action Modality Transfer

This paper strives for action recognition and detection in video modalit...

Fida Mohammad Thoker, et al. ∙

research

∙ 11/18/2021

Wiggling Weights to Improve the Robustness of Classifiers

Robustness against unwanted perturbations is an important aspect of depl...

Sadaf Gulshad, et al. ∙

research

∙ 11/25/2021

BoxeR: Box-Attention for 2D and 3D Transformers

In this paper, we propose a simple attention mechanism, we call Box-Atte...

Duy-Kien Nguyen, et al. ∙

research

∙ 03/31/2022

3D Equivariant Graph Implicit Functions

In recent years, neural implicit representations have made remarkable pr...

Yunlu Chen, et al. ∙

research

∙ 05/31/2022

Contrasting quadratic assignments for set-based representation learning

The standard approach to contrastive learning is to maximize the agreeme...

Artem Moskalev, et al. ∙

University of Amsterdam

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