Publications

Michael Jenkins

Jakob Kaiser

Andreas Keil

Robert T. Knight

Silvia Kochen

Boris Kotchoubey

Olave E. Krigolson

Nicolas Langer

Heinrich R. Liesefeld

Sarah Lippé

Raquel E. London

Annmarie MacNamara

Scott Makeig

Welber Marinovic

Eduardo Martínez-Montes

Aleya A. Marzuki

Ryan K. Mathew

Christoph Michel

José d. R. Millán

Mark Mon-Williams

Lilia Morales-Chacón

Richard Naar

Gustav Nilsonne

Guiomar Niso

Erika Nyhus

Robert Oostenveld

Katharina Paul

Walter Paulus

Daniela M. Pfabigan

Gilles Pourtois

Stefan Rampp

Manuel Rausch

Kay Robbins

Paolo M. Rossini

Manuela Ruzzoli

Barbara Schmidt

Magdalena Senderecka

Narayanan Srinivasan

Yannik Stegmann

Paul M. Thompson

Mitchell Valdes-Sosa

Melle J. W. van der Molen

Domenica Veniero

Edelyn Verona

Bradley Voytek

Dezhong Yao

Alan C. Evans

Pedro Valdes-Sosa

2024-08-22

Nature Human Behaviour (publié)

One hundred years of EEG for brain and behaviour research.

Faisal Mushtaq

Dominik Welke

Anne Gallagher

Yuri G. Pavlov

Layla Kouara

Jorge Bosch-Bayard

Jasper JF van den Bosch

Mahnaz Arvaneh

Amy R. Bland

Maximilien Chaumon

Cornelius Borck

Xun He

Steven J. Luck

Maro G. Machizawa

Cyril Pernet

Aina Puce

Sidney J. Segalowitz

Christine Rogers

Muhammad Awais

Claudio Babiloni … (voir 75 de plus)

Neil W. Bailey

Sylvain Baillet

Robert C. A. Bendall

Daniel Brady

Maria L. Bringas-Vega

Niko A. Busch

Ana Calzada-Reyes

Armand Chatard

Peter E. Clayson

Michael X. Cohen

Jonathan Cole

Martin Constant

Alexandra Corneyllie

Damien Coyle

Damian Cruse

Ioannis Delis

Arnaud Delorme

Damien Fair

Tiago H. Falk

Matthias Gamer

Giorgio Ganis

Kilian Gloy

Samantha Gregory

Cameron D. Hassall

Katherine E. Hiley

Richard B. Ivry

Michael Jenkins

Jakob Kaiser

Andreas Keil

Robert T. Knight

Silvia Kochen

Boris Kotchoubey

Olave E. Krigolson

Nicolas Langer

Heinrich R. Liesefeld

Sarah Lippé

Raquel E. London

Annmarie MacNamara

Scott Makeig

Welber Marinovic

Eduardo Martínez-Montes

Aleya A. Marzuki

Ryan K. Mathew

Christoph Michel

José d. R. Millán

Mark Mon-Williams

Lilia Morales-Chacón

Richard Naar

Gustav Nilsonne

Guiomar Niso

Erika Nyhus

Robert Oostenveld

Katharina Paul

Walter Paulus

Daniela M. Pfabigan

Gilles Pourtois

Stefan Rampp

Manuel Rausch

Kay Robbins

Paolo M. Rossini

Manuela Ruzzoli

Barbara Schmidt

Magdalena Senderecka

Narayanan Srinivasan

Yannik Stegmann

Paul M. Thompson

Mitchell Valdes-Sosa

Melle J. W. van der Molen

Domenica Veniero

Edelyn Verona

Bradley Voytek

Dezhong Yao

Alan C. Evans

Pedro Valdes-Sosa

2024-08-22

Nature Human Behaviour (publié)

One hundred years of EEG for brain and behaviour research.

Faisal Mushtaq

Dominik Welke

Anne Gallagher

Yuri G. Pavlov

Layla Kouara

Jorge Bosch-Bayard

Jasper JF van den Bosch

Mahnaz Arvaneh

Amy R. Bland

Maximilien Chaumon

Cornelius Borck

Xun He

Steven J. Luck

Maro G. Machizawa

Cyril Pernet

Aina Puce

Sidney J. Segalowitz

Christine Rogers

Muhammad Awais

Claudio Babiloni … (voir 75 de plus)

Neil W. Bailey

Sylvain Baillet

Robert C. A. Bendall

Daniel Brady

Maria L. Bringas-Vega

Niko A. Busch

Ana Calzada-Reyes

Armand Chatard

Peter E. Clayson

Michael X. Cohen

Jonathan Cole

Martin Constant

Alexandra Corneyllie

Damien Coyle

Damian Cruse

Ioannis Delis

Arnaud Delorme

Damien Fair

Tiago H. Falk

Matthias Gamer

Giorgio Ganis

Kilian Gloy

Samantha Gregory

Cameron D. Hassall

Katherine E. Hiley

Richard B. Ivry

Michael Jenkins

Jakob Kaiser

Andreas Keil

Robert T. Knight

Silvia Kochen

Boris Kotchoubey

Olave E. Krigolson

Nicolas Langer

Heinrich R. Liesefeld

Sarah Lippé

Raquel E. London

Annmarie MacNamara

Scott Makeig

Welber Marinovic

Eduardo Martínez-Montes

Aleya A. Marzuki

Ryan K. Mathew

Christoph Michel

José d. R. Millán

Mark Mon-Williams

Lilia Morales-Chacón

Richard Naar

Gustav Nilsonne

Guiomar Niso

Erika Nyhus

Robert Oostenveld

Katharina Paul

Walter Paulus

Daniela M. Pfabigan

Gilles Pourtois

Stefan Rampp

Manuel Rausch

Kay Robbins

Paolo M. Rossini

Manuela Ruzzoli

Barbara Schmidt

Magdalena Senderecka

Narayanan Srinivasan

Yannik Stegmann

Paul M. Thompson

Mitchell Valdes-Sosa

Melle J. W. van der Molen

Domenica Veniero

Edelyn Verona

Bradley Voytek

Dezhong Yao

Alan C. Evans

Pedro Valdes-Sosa

2024-08-22

Nature Human Behaviour (publié)

One hundred years of EEG for brain and behaviour research.

Faisal Mushtaq

Dominik Welke

Anne Gallagher

Yuri G. Pavlov

Layla Kouara

Jorge Bosch-Bayard

Jasper JF van den Bosch

Mahnaz Arvaneh

Amy R. Bland

Maximilien Chaumon

Cornelius Borck

Xun He

Steven J. Luck

Maro G. Machizawa

Cyril Pernet

Aina Puce

Sidney J. Segalowitz

Christine Rogers

Muhammad Awais

Claudio Babiloni … (voir 75 de plus)

Neil W. Bailey

Sylvain Baillet

Robert C. A. Bendall

Daniel Brady

Maria L. Bringas-Vega

Niko A. Busch

Ana Calzada-Reyes

Armand Chatard

Peter E. Clayson

Michael X. Cohen

Jonathan Cole

Martin Constant

Alexandra Corneyllie

Damien Coyle

Damian Cruse

Ioannis Delis

Arnaud Delorme

Damien Fair

Tiago H. Falk

Matthias Gamer

Giorgio Ganis

Kilian Gloy

Samantha Gregory

Cameron D. Hassall

Katherine E. Hiley

Richard B. Ivry

Michael Jenkins

Jakob Kaiser

Andreas Keil

Robert T. Knight

Silvia Kochen

Boris Kotchoubey

Olave E. Krigolson

Nicolas Langer

Heinrich R. Liesefeld

Sarah Lippé

Raquel E. London

Annmarie MacNamara

Scott Makeig

Welber Marinovic

Eduardo Martínez-Montes

Aleya A. Marzuki

Ryan K. Mathew

Christoph Michel

José d. R. Millán

Mark Mon-Williams

Lilia Morales-Chacón

Richard Naar

Gustav Nilsonne

Guiomar Niso

Erika Nyhus

Robert Oostenveld

Katharina Paul

Walter Paulus

Daniela M. Pfabigan

Gilles Pourtois

Stefan Rampp

Manuel Rausch

Kay Robbins

Paolo M. Rossini

Manuela Ruzzoli

Barbara Schmidt

Magdalena Senderecka

Narayanan Srinivasan

Yannik Stegmann

Paul M. Thompson

Mitchell Valdes-Sosa

Melle J. W. van der Molen

Domenica Veniero

Edelyn Verona

Bradley Voytek

Dezhong Yao

Alan C. Evans

Pedro Valdes-Sosa

2024-08-22

Nature Human Behaviour (publié)

One hundred years of EEG for brain and behaviour research.

Faisal Mushtaq

Dominik Welke

Anne Gallagher

Yuri G. Pavlov

Layla Kouara

Jorge Bosch-Bayard

Jasper JF van den Bosch

Mahnaz Arvaneh

Amy R. Bland

Maximilien Chaumon

Cornelius Borck

Xun He

Steven J. Luck

Maro G. Machizawa

Cyril Pernet

Aina Puce

Sidney J. Segalowitz

Christine Rogers

Muhammad Awais

Claudio Babiloni … (voir 75 de plus)

Neil W. Bailey

Sylvain Baillet

Robert C. A. Bendall

Daniel Brady

Maria L. Bringas-Vega

Niko A. Busch

Ana Calzada-Reyes

Armand Chatard

Peter E. Clayson

Michael X. Cohen

Jonathan Cole

Martin Constant

Alexandra Corneyllie

Damien Coyle

Damian Cruse

Ioannis Delis

Arnaud Delorme

Damien Fair

Tiago H. Falk

Matthias Gamer

Giorgio Ganis

Kilian Gloy

Samantha Gregory

Cameron D. Hassall

Katherine E. Hiley

Richard B. Ivry

Michael Jenkins

Jakob Kaiser

Andreas Keil

Robert T. Knight

Silvia Kochen

Boris Kotchoubey

Olave E. Krigolson

Nicolas Langer

Heinrich R. Liesefeld

Sarah Lippé

Raquel E. London

Annmarie MacNamara

Scott Makeig

Welber Marinovic

Eduardo Martínez-Montes

Aleya A. Marzuki

Ryan K. Mathew

Christoph Michel

José d. R. Millán

Mark Mon-Williams

Lilia Morales-Chacón

Richard Naar

Gustav Nilsonne

Guiomar Niso

Erika Nyhus

Robert Oostenveld

Katharina Paul

Walter Paulus

Daniela M. Pfabigan

Gilles Pourtois

Stefan Rampp

Manuel Rausch

Kay Robbins

Paolo M. Rossini

Manuela Ruzzoli

Barbara Schmidt

Magdalena Senderecka

Narayanan Srinivasan

Yannik Stegmann

Paul M. Thompson

Mitchell Valdes-Sosa

Melle J. W. van der Molen

Domenica Veniero

Edelyn Verona

Bradley Voytek

Dezhong Yao

Alan C. Evans

Pedro Valdes-Sosa

2024-08-22

Nature Human Behaviour (publié)

Switching between tasks can cause AI to lose the ability to learn

Clare Lyle

Razvan Pascanu

2024-08-21

Nature (publié)

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (voir plus). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.

2024-08-17

ArXiv (prépublication)

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (voir plus). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.

2024-08-17

ArXiv (prépublication)

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (voir plus). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.

2024-08-17

ArXiv (prépublication)

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (voir plus). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.

2024-08-17

ArXiv (prépublication)

Zero-Shot Object-Centric Representation Learning

Aniket Rajiv Didolkar

Andrii Zadaianchuk

Michael Curtis Mozer

Georg Martius

Maximilian Seitzer

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities… (voir plus). Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing pre-trained self-supervised features. However, so far, object-centric methods have mostly been applied in-distribution, with models trained and evaluated on the same dataset. This is in contrast to the wider trend in machine learning towards general-purpose models directly applicable to unseen data and tasks. Thus, in this work, we study current object-centric methods through the lens of zero-shot generalization by introducing a benchmark comprising eight different synthetic and real-world datasets. We analyze the factors influencing zero-shot performance and find that training on diverse real-world images improves transferability to unseen scenarios. Furthermore, inspired by the success of task-specific fine-tuning in foundation models, we introduce a novel fine-tuning strategy to adapt pre-trained vision encoders for the task of object discovery. We find that the proposed approach results in state-of-the-art performance for unsupervised object discovery, exhibiting strong zero-shot transfer to unseen datasets.

2024-08-17

ArXiv (prépublication)

Understanding the Local Geometry of Generative Model Manifolds

Ahmed Imtiaz Humayun

Candice Schumann

Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training. For a pr… (voir plus)e-trained generative model, the common way to evaluate the quality of the manifold representation learned, is by computing global metrics like Fr\'echet Inception Distance using a large number of generated and real samples. However, generative model performance is not uniform across the learned manifold, e.g., for \textit{foundation models} like Stable Diffusion generation performance can vary significantly based on the conditioning or initial noise vector being denoised. In this paper we study the relationship between the \textit{local geometry of the learned manifold} and downstream generation. Based on the theory of continuous piecewise-linear (CPWL) generators, we use three geometric descriptors - scaling (

2024-08-15

ArXiv (prépublication)