3 citations · 4 across the 16 of their papers we have counts for
16 papers
Functional Degeneracy in Neural Networks: Measurement and Pruning
Maria Matveev, Pascal Esser, Ayush Bharadwaj +2
A central question in modern machine learning is how much a trained model can be compressed without changing its behavior, to reduce the memory, compute and energy required to depl…
Towards an Expressivity-Normalized Energy-Demand Comparison of ANNs and SNNs
Miriam Kranzlmüller, Pascal Esser, Gitta Kutyniok
Spiking neural networks (SNNs) are often regarded as energy-efficient alternatives to artificial neural networks (ANNs), yet their advantage depends critically on both network arch…
Beyond Linear and Overcomplete Regimes: A Mean-Field Analysis of Bottleneck Autoencoders
Santanu Das, Ramyak Bilas, Pascal Esser +1
Autoencoders (AEs) learn low-dimensional representations by mapping data into a latent space while minimizing reconstruction error. Despite their empirical success, theoretical und…
Theoretical Foundations of Representation Learning using Unlabeled Data: Statistics and Optimization
Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Representation learning from unlabeled data has been extensively studied in statistics, data science and signal processing with a rich literature on techniques for dimension reduct…
A Probabilistic Model for Non-Contrastive Learning
Maximilian Fleissner, Pascal Esser, Debarghya Ghoshdastidar
Self-supervised learning (SSL) aims to find meaningful representations from unlabeled data by encoding semantic similarities through data augmentations. Despite its current popular…
Cluster Specific Representation Learning
Mahalakshmi Sabanayagam, Omar Al-Dabooni, Pascal Esser
Representation learning aims to extract meaningful lower-dimensional embeddings from data, known as representations. Despite its widespread application, there is no established def…
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?
Gautham Govind Anil, Pascal Esser, Debarghya Ghoshdastidar
Contrastive learning is a paradigm for learning representations from unlabelled data that has been highly successful for image and text data. Several recent works have examined con…
Non-Parametric Representation Learning with Kernels
Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Unsupervised and self-supervised representation learning has become popular in recent years for learning useful features from unlabelled data. Representation learning has been most…
Representation Learning Dynamics of Self-Supervised Models
Pascal Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar
Self-Supervised Learning (SSL) is an important paradigm for learning representations from unlabelled data, and SSL with neural networks has been highly successful in practice. Howe…
Improved Representation Learning Through Tensorized Autoencoders
Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam +1
The central question in representation learning is what constitutes a good or meaningful representation. In this work we argue that if we consider data with inherent cluster struct…
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel
Mahalakshmi Sabanayagam, Pascal Esser, Debarghya Ghoshdastidar
The fundamental principle of Graph Neural Networks (GNNs) is to exploit the structural information of the data by aggregating the neighboring nodes using a `graph convolution' in c…
Characterizing Learning Dynamics under Relative Reparameterization of Singular Models
Pascal Mattia Esser, Frank Nielsen
A common way to analyze learning of statistical models is to consider operations in the models parameter space, however this becomes challenging when there is no one-to-one mapping…
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
Pascal Mattia Esser, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
In recent years, several results in the supervised learning setting suggested that classical statistical learning-theoretic measures, such as VC dimension, do not adequately explai…
Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds
Pascal Mattia Esser, Frank Nielsen
When analyzing parametric statistical models, a useful approach consists in modeling geometrically the parameter space. However, even for very simple and commonly used hierarchical…
New Insights into Graph Convolutional Networks using Neural Tangent Kernels
Mahalakshmi Sabanayagam, Pascal Esser, Debarghya Ghoshdastidar
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structured data. Although empirically successful, GCNs exhibit certain behaviour that has…
Near-Optimal Comparison Based Clustering
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. How…