Maximilian Nickel

research

∙ 09/18/2023

Graph topological property recovery with heat and wave dynamics-based features on graphs

In this paper, we propose Graph Differential Equation Network (GDeNet), ...

0 Dhananjay Bhaskar, et al. ∙

research

∙ 07/11/2023

Weisfeiler and Lehman Go Measurement Modeling: Probing the Validity of the WL Test

The expressive power of graph neural networks is usually measured by com...

0 Arjun Subramonian, et al. ∙

research

∙ 06/11/2023

On Kinetic Optimal Probability Paths for Generative Models

Recent successful generative models are trained by fitting a neural netw...

0 Neta Shaul, et al. ∙

research

∙ 05/19/2023

Group fairness without demographics using social networks

Group fairness is a popular approach to prevent unfavorable treatment of...

0 David Liu, et al. ∙

research

∙ 04/18/2023

Hyperbolic Image-Text Representations

Visual and linguistic concepts naturally organize themselves in a hierar...

0 Karan Desai, et al. ∙

research

∙ 12/28/2022

Latent Discretization for Continuous-time Sequence Compression

Neural compression offers a domain-agnostic approach to creating codecs ...

0 Ricky T. Q. Chen, et al. ∙

research

∙ 10/06/2022

Flow Matching for Generative Modeling

We introduce a new paradigm for generative modeling built on Continuous ...

2 Yaron Lipman, et al. ∙

research

∙ 07/11/2022

Matching Normalizing Flows and Probability Paths on Manifolds

Continuous Normalizing Flows (CNFs) are a class of generative models tha...

12 Heli Ben-Hamu, et al. ∙

research

∙ 03/14/2022

Semi-Discrete Normalizing Flows through Differentiable Tessellation

Mapping between discrete and continuous distributions is a difficult tas...

0 Ricky T. Q. Chen, et al. ∙

research

∙ 03/11/2022

Can I see an Example? Active Learning the Long Tail of Attributes and Relations

There has been significant progress in creating machine learning models ...

0 Tyler L. Hayes, et al. ∙

research

∙ 08/18/2021

Moser Flow: Divergence-based Generative Modeling on Manifolds

We are interested in learning generative models for complex geometries d...

0 Noam Rozen, et al. ∙

research

∙ 11/09/2020

Neural Spatio-Temporal Point Processes

We propose a new class of parameterizations for spatio-temporal point pr...

0 Ricky T. Q. Chen, et al. ∙

research

∙ 11/08/2020

Learning Neural Event Functions for Ordinary Differential Equations

The existing Neural ODE formulation relies on an explicit knowledge of t...

0 Ricky T. Q. Chen, et al. ∙

research

∙ 10/06/2020

CURI: A Benchmark for Productive Concept Learning Under Uncertainty

Humans can learn and reason under substantial uncertainty in a space of ...

4 Ramakrishna Vedantam, et al. ∙

research

∙ 06/18/2020

Riemannian Continuous Normalizing Flows

Normalizing flows have shown great promise for modelling flexible probab...

14 Emile Mathieu, et al. ∙

research

∙ 02/28/2020

Learning Multivariate Hawkes Processes at Scale

Multivariate Hawkes Processes (MHPs) are an important class of temporal ...

6 Maximilian Nickel, et al. ∙

research

∙ 10/28/2019

Hyperbolic Graph Neural Networks

Learning from graph-structured data is an important task in machine lear...

28 Qi Liu, et al. ∙

research

∙ 05/15/2019

Task-Driven Modular Networks for Zero-Shot Compositional Learning

One of the hallmarks of human intelligence is the ability to compose lea...

0 Senthil Purushwalkam, et al. ∙

research

∙ 02/03/2019

Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

We consider the task of inferring is-a relationships from large text cor...

0 Matt Le, et al. ∙

research

∙ 06/09/2018

Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry

We are concerned with the discovery of hierarchical relationships from l...

0 Maximilian Nickel, et al. ∙

research

∙ 06/08/2018

Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora

Methods for unsupervised hypernym detection may broadly be categorized a...

0 Stephen Roller, et al. ∙

research

∙ 11/27/2017

Separating Self-Expression and Visual Content in Hashtag Supervision

The variety, abundance, and structured nature of hashtags make them an i...

0 Andreas Veit, et al. ∙

research

∙ 10/30/2017

Fast Linear Model for Knowledge Graph Embeddings

This paper shows that a simple baseline based on a Bag-of-Words (BoW) re...

0 Armand Joulin, et al. ∙

research

∙ 07/19/2017

Learning Visually Grounded Sentence Representations

We introduce a variety of models, trained on a supervised image captioni...

0 Douwe Kiela, et al. ∙

research

∙ 07/05/2017

Complex and Holographic Embeddings of Knowledge Graphs: A Comparison

Embeddings of knowledge graphs have received significant attention due t...

0 Maximilian Nickel, et al. ∙

research

∙ 05/22/2017

Poincaré Embeddings for Learning Hierarchical Representations

Representation learning has become an invaluable approach for learning f...

0 Maximilian Nickel, et al. ∙

research

∙ 09/11/2016

Relational Models

We provide a survey on relational models. Relational models describe com...

0 Volker Tresp, et al. ∙

research

∙ 10/16/2015

Holographic Embeddings of Knowledge Graphs

Learning embeddings of entities and relations is an efficient and versat...

0 Maximilian Nickel, et al. ∙

research

∙ 03/02/2015

A Review of Relational Machine Learning for Knowledge Graphs

Relational machine learning studies methods for the statistical analysis...

0 Maximilian Nickel, et al. ∙

research

∙ 06/10/2013

Logistic Tensor Factorization for Multi-Relational Data

Tensor factorizations have become increasingly popular approaches for va...

0 Maximilian Nickel, et al. ∙

Maximilian Nickel

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