Publications

Our research contributions spanning computational neuroscience, machine learning, and computer vision. For a complete list, visit our Google Scholar page.

Year:

Type:

2025

Arnold: a generalist muscle transformer policy

AS Chiappa, B An, M Simos, C Li, A Mathis

arXiv preprint

sensorimotor control

robotics

reinforcement learning

arXiv

LLaVAction: evaluating and training multi-modal large language models for action recognition

S Ye*, H Qi*, A Mathis**, MW Mathis**

arXiv preprint

action recognition

multimodal learning

LLMs

arXiv Website

Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control

M Simos, AS Chiappa, A Mathis

arXiv preprint

reinforcement learning

motor control

biomechanics

arXiv

DLC2Action: A Deep Learning-based Toolbox for Automated Behavior Segmentation

E Kozlova, A Bonnetto, A Mathis

bioRxiv

behavior analysis

deep learning

neuroscience

bioRxiv Code

EPFL-Smart-Kitchen-30: Densely annotated cooking dataset with 3D kinematics to challenge video and language models

A Bonnetto*, H Qi*, F Leong, M Tashkovska, M Rad, S Shokur, F Hummel, S Micera, M Pollefeys, A Mathis

NeurIPS (in press)

datasets

3D kinematics

multimodal learning

2024

MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

V Gabeff, H Qi, B Flaherty, G Sumbül, A Mathis*, D Tuia*

CVPR (highlight)

wildlife conservation

datasets

computer vision

arXiv

Decoding the brain: From neural representations to mechanistic models

MW Mathis, AP Rotondo, EF Chang, AS Tolias, A Mathis

CellVol. 187 (21)pp. 5814-5832

neuroscience

neural decoding

mechanistic models

Acquiring musculoskeletal skills with curriculum-based reinforcement learning

AS Chiappa, P Tano, N Patel, A Ingster, A Pouget, A Mathis

Neuron

reinforcement learning

motor control

curriculum learning

Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders

L Stoffl, A Bonnetto, S d'Ascoli, A Mathis

European Conference on Computer Vision

behavior analysis

self-supervised learning

ECCV

Code

Task-driven neural network models predict neural dynamics of proprioception

A Marin Vargas*, A Bisi*, AS Chiappa, C Versteeg, LE Miller, A Mathis

Cell

proprioception

neural networks

sensorimotor control

SuperAnimal models pretrained for plug-and-play analysis of animal behavior

S Ye, A Filippova, J Lauer, M Vidal, St Schneider, T Qiu, A Mathis, MW Mathis

Nature Communications

pose estimation

transfer learning

animal behavior

Code

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

V Gabeff, M Russwurm, D Tuia, A Mathis

International Journal of Computer Vision (in press)

wildlife conservation

vision-language models

CLIP

Code

2023

Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity

M Zhou*, L Stoffl*, MW Mathis, A Mathis

ICCV

pose estimation

crowded scenes

computer vision

arXiv

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

S Ye, J Lauer, M Zhou, A Mathis, MW Mathis

NeurIPS

NLP

behavior analysis

GPT

Code

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Kai J. Sandbrink*, Pranav Mamidanna*, Claudio Michaelis, Matthias Bethge*, Mackenzie W. Mathis*, Alexander Mathis*

eLife

proprioception

neural networks

sensorimotor control

2022

DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body

Alberto Silvio Chiappa, Alessandro Marin Vargas, Alexander Mathis

NeurIPS

reinforcement learning

locomotion

morphology

Code

Multi-animal pose estimation, identification and tracking with DeepLabCut

Jessy Lauer, Mu Zhou, Shaokai Ye, et al., Mackenzie W Mathis*, Alexander Mathis*

Nature Neuroscience

pose estimation

tracking

DeepLabCut

Code

2021

Seeing biodiversity: perspectives in machine learning for wildlife conservation

Devis Tuia, Benjamin Kellenberger, Sara Beery, et al., Alexander Mathis, Mackenzie W Mathis, et al.

Nature Communications

wildlife conservation

machine learning

computer vision

2020

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives

Alexander Mathis, Steffen Schneider, Jessy Lauer, Mackenzie W. Mathis

NeuronVol. 108pp. P44-65

deep learning

motion capture

pose estimation

2019

Using DeepLabCut for 3D markerless pose estimation across species and behaviors

Tanmay Nath*, Alexander Mathis*, An Chi Chen, Amir Patel, Matthias Bethge, Mackenzie W. Mathis

Nature ProtocolsVol. 14pp. 2152-2176

pose estimation

DeepLabCut

3D tracking

Code

2018

DeepLabCut: Markerless tracking of user-defined features with deep learning

Alexander Mathis, Pranav Mamidanna, Kevin M. Cury, Taiga Abe, Venkatesh N. Murthy, Mackenzie W. Mathis*, Matthias Bethge*

Nature NeuroscienceVol. 21pp. 1281-1289

pose estimation

DeepLabCut

markerless tracking

Code