Pascal Mattia Esser

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 16

Top Topics

Gaussian Mixture Model

Feature Hierarchies

Parameter Space

Top Venues

Trans. Mach. Learn. Res.

Publications

Gautham Govind Anil, Pascal Mattia Esser, Debarghya Ghoshdastidar
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? CoRR (2024)
Pascal Mattia Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Non-parametric Representation Learning with Kernels. AAAI (2024)
Pascal Mattia Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar
Representation Learning Dynamics of Self-Supervised Models. Trans. Mach. Learn. Res. 2024 (2024)
Pascal Mattia Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar
Representation Learning Dynamics of Self-Supervised Models. CoRR (2023)
Pascal Mattia Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Non-Parametric Representation Learning with Kernels. CoRR (2023)
Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Improved Representation Learning Through Tensorized Autoencoders. AISTATS (2023)
Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel. Trans. Mach. Learn. Res. 2023 (2023)
Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar
Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis. CoRR (2022)
Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Improved Representation Learning Through Tensorized Autoencoders. CoRR (2022)
Pascal Mattia Esser, Frank Nielsen
On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models. CoRR (2022)
Pascal Mattia Esser, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. CoRR (2021)
Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. NeurIPS (2021)
Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar
New Insights into Graph Convolutional Networks using Neural Tangent Kernels. CoRR (2021)
Pascal Mattia Esser, Frank Nielsen
Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds. CoRR (2021)
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
Near-Optimal Comparison Based Clustering. NeurIPS (2020)
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
Near-Optimal Comparison Based Clustering. CoRR (2020)