@phdthesis {dSchurholt24, title = {Hyper-Representations: Learning from Populations of Neural Networks}, volume = {PhD}, year = {2024}, month = {05/2024}, abstract = {

Neural Networks (NNs) have undergone a remarkable evolution, transitioning from academic labs to key technologies across various domains. This proliferation underscores their capability and versatility. As these models become integral to critical decision-making processes, the demand for methods to understand their inner workings likewise becomes more pronounced. This thesis addresses the challenge of understanding NNs through the lens of their most fundamental component: the weights, which encapsulate the learned information and determine the model behavior.

The NN weight space contains complex local and global structures which makes it a challenging domain. Addressing these challenges, this thesis develops innovative representation learning methods for the domain of weight spaces. The proposed methods embed and disentangle model weights in a representation space. The representation space allows not only to analyze existing models but also to generate new models with specified characteristics. Such an analysis builds on populations of models, to develop a nuanced understanding of the structure of NN weights.

At the core of this thesis is a fundamental question: Can we learn general, task-agnostic representations from populations of Neural Network models? The key contribution of this thesis to answer that question are hyper-representations, a self-supervised method to learn representations of NN weights. Work in this thesis finds that trained NN models indeed occupy meaningful structures in the weight space, that can be learned and used. Through extensive experiments, this thesis demonstrates that hyper-representations uncover model properties, such as their performance, state of training, or hyperparameters.

Moreover, the identification of regions with specific properties in hyper-representation space allows to sample and generate model weights with targeted properties. This thesis demonstrates applications for fine-tuning, and transfer learning to great success. Lastly, it presents methods that allow hyper-representations to generalize beyond model sizes, architectures, and tasks. The practical implications of that are profound, as it opens the door to foundation models of Neural Networks, which aggregate and instantiate their knowledge across models and architectures.

Ultimately, this thesis contributes to the deeper understanding of Neural Networks by investigating structures in their weights which leads to more interpretable, efficient, and adaptable models. By laying the groundwork for representation learning of NN weights, this research demonstrates the potential to change the way Neural Networks are developed, analyzed, and used.

[My tweet]

Congratulatuon to Dr Konstantin Sch{\

}, author = {Sch{\"u}rholt, Konstantin}, editor = {Mahoney, Michael and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @conference {cSchurholt22, title = {Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights}, booktitle = {NeurIPS 2022 - Neural Information Processing Systems}, year = {2022}, abstract = {

Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation. Recently, an autoencoder trained on a model zoo was able to learn a hyper-representation, which captures intrinsic and extrinsic properties of the models in the zoo. In this work, we extend hyper-representations for generative use to sample new model weights as pre-training. We propose layer-wise loss normalization which we demonstrate is key to generate high-performing models and a sampling method based on the empirical density of hyper-representations. The models generated using our methods are diverse, performant and capable to outperform conventional baselines for transfer learning. Our results indicate the potential of knowledge aggregation from model zoos to new models via hyper-representations thereby paving the avenue for novel research directions.

}, author = {Sch{\"u}rholt, Konstantin and Knyazev, Boris and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @conference {cSchurholt22b, title = {Model Zoos: A Dataset of Diverse Populations of Neural Network Models}, booktitle = {NeurIPS 2022 Track Datasets and Benchmarks}, year = {2022}, month = {12/2022}, address = {New Orleans, Louisiana, USA.}, abstract = {

In the last years, neural networks (NN) have evolved from laboratory environments to the state-of-the-art for many real-world problems. It was shown that NN models (i.e., their weights and biases) evolve on unique trajectories in weight space during training. Following, a population of such neural network models (referred to as model zoo) would form structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can reveal latent properties of individual models. With such model zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of NN weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of NNs. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of NN models for further research. In total the proposed model zoo dataset is based on eight image datasets, consists of 27 model zoos trained with varying hyperparameter combinations and includes 50{\textquoteright}360 unique NN models as well as their sparsified twins, resulting in over 3{\textquoteright}844{\textquoteright}360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks.The dataset can be found at www.modelzoos.cc.

}, author = {Sch{\"u}rholt, Konstantin and Taskiran, Diyar and Knyazev, Boris and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @conference {cSchurholt22a, title = {Model Zoos: A Dataset of Diverse Populations of Neural Network Models}, booktitle = {NeurIPS}, year = {2022}, month = {09/2022}, abstract = {

In the last years, neural networks (NN) have evolved from laboratory environments to the state-of-the-art for many real-world problems. It was shown that NN models (i.e., their weights and biases) evolve on unique trajectories in weight space during training. Following, a population of such neural network models (referred to as model zoo) would form structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can reveal latent properties of individual models. With such model zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of NN weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of NNs. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of NN models for further research. In total the proposed model zoo dataset is based on eight image datasets, consists of 27 model zoos trained with varying hyperparameter combinations and includes 50{\textquoteright}360 unique NN models as well as their sparsified twins, resulting in over 3,844,360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks.

}, author = {Sch{\"u}rholt, Konstantin and Taskiran, Diyar and Knyazev, Boris and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @mastersthesis {xCaselles21, title = {Disentangling neural network structure from the weights space}, year = {2021}, abstract = {

Deep Neural Networks have been used to tackle a wide variety of tasks achieving great performance. However, there is still a lack of knowledge of how the training of these models converge and how weights relate to their properties. In this thesis we investigate the structure of the weight space and try to disentangle its properties. Attention mechanisms are introduced to capture relations among neurons{\textquoteright} weights that help in weight reconstruction, hyper-parameter classification and accuracy prediction. Our approach further has the potential to work with variable input size allowing different network width, depth or even architecture types.

}, author = {Caselles, Pol}, editor = {Sch{\"u}rholt, Konstantin and Borth, Damian and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xGranero, title = {A Video Database for Analyzing Affective Physiological Responses}, year = {2019}, abstract = {

Affective computing, leveraged by machine learning techniques, is advancing rapidly in the task of affect recognition in videos. However, there is a need for more annotated data. Several studies have built huge video datasets with emotions annotations. Others have collected music videos or film scenes datasets with physiological signals. However, none of them approached a solution with both physiological signals and user-generated videos. The work introduced here presents GALLUS, a novel database of user-generated videos with affective physiological responses. The database is composed of 775 videos that have been previously annotated through an online crowdsourcing platform. Physiological responses such as electroencephalography, electrocardiography, galvanic skin response, facial emotion recognition, and eye-gaze have been collected from 30 participants while they watched the stimuli. Our dataset will be made public to foster research in affect recognition.

}, author = {Granero, Marcel}, editor = {Borth, Damian and Weber, Barbara and Xavier Gir{\'o}-i-Nieto} }