arXiv subjects

Anton Björklund

Publications and source records attributed to Anton Björklund.

5 recordsLinked to original sources

arXiv2026

Efficient Preimage Approximation for Neural Network Certification

The growing reliance on artificial intelligence in safety- and security-critical applications is raising concerns about the robustness of neural networks to erroneous or adversarial input. Certification is a methodology for ensuring model trustworthiness by providing formal guarantees on model behaviour. While most verification methods focus on worst-case analysis by bounding the network output, an alternative approach based on approximating the preimage can complement such analysis by estimating the proportion of inputs that satisfy a given specification. However, existing preimage-based methods, such as the state-of-the-art PREMAP, are limited to fully connected neural networks of moderate dimensionality. In this paper, we introduce PREMAP2, a collection of algorithmic extensions to PREMAP that enhance its scalability and efficiency through improved branching heuristics, adaptive Monte Carlo sampling, and reverse bound propagation. We further endow PREMAP2 with additional functionality such as support for non-uniform priors and confidence intervals. These advances enable the application of PREMAP2 to previously intractable settings, including real-world patch attacks against convolutional neural networks, where adversarial stickers or lighting conditions obscure parts of images. We showcase the effectiveness of our approach across several use cases, including certifying reliability, robustness, interpretability, and fairness, on domains ranging from computer vision to control tasks. Our implementation is available as open-source software.

cs.LG↗

arXiv2025

GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling

Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading to a decline in predictive performance on unseen data. A standard solution is early stopping using a hold-out validation set, which halts the minimisation when the validation loss stops decreasing. However, this hold-out set reduces the data available for training. This paper presents GRADSTOP, a novel stochastic early stopping method that only uses information in the gradients, which are produced by the gradient descent algorithm ``for free.'' Our main contributions are that we estimate the Bayesian posterior by the gradient information, define the early stopping problem as drawing sample from this posterior, and use the approximated posterior to obtain a stopping criterion. Our empirical evaluation shows that GRADSTOP achieves a small loss on test data and compares favourably to a validation-set-based stopping criterion. By leveraging the entire dataset for training, our method is particularly advantageous in data-limited settings, such as transfer learning. It can be incorporated as an optional feature in gradient descent libraries with only a small computational overhead. The source code is available at https://github.com/edahelsinki/gradstop.

cs.LG↗

arXiv2023

Using Slisemap to interpret physical data

Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper we apply a recently introduced manifold visualisation method, called Slise, on datasets from physics and chemistry. Slisemap combines manifold visualisation with explainable artificial intelligence. Explainable artificial intelligence is used to investigate the decision processes of black box machine learning models and complex simulators. With Slisemap we find an embedding such that data items with similar local explanations are grouped together. Hence, Slisemap gives us an overview of the different behaviours of a black box model. This makes Slisemap into a supervised manifold visualisation method, where the patterns in the embedding reflect a target property. In this paper we show how Slisemap can be used and evaluated on physical data and that Slisemap is helpful in finding meaningful information on classification and regression models trained on these datasets.

cs.LG↗

arXiv2023

$χ$iplot: web-first visualisation platform for multidimensional data

$χ$iplot is an HTML5-based system for interactive exploration of data and machine learning models. A key aspect is interaction, not only for the interactive plots but also between plots. Even though $χ$iplot is not restricted to any single application domain, we have developed and tested it with domain experts in quantum chemistry to study molecular interactions and regression models. $χ$iplot can be run both locally and online in a web browser (keeping the data local). The plots and data can also easily be exported and shared. A modular structure also makes $χ$iplot optimal for developing machine learning and new interaction methods.

cs.HC↗

arXiv2022

SLISEMAP: Supervised dimensionality reduction through local explanations

Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item. It is possible to create global explanations for all data items, but these explanations generally have low fidelity for complex black box models. We propose a new supervised manifold visualisation method, SLISEMAP, that simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby. We provide a mathematical derivation of our problem and an open source implementation implemented using the GPU-optimised PyTorch library. We compare SLISEMAP to multiple popular dimensionality reduction methods and find that SLISEMAP is able to utilise labelled data to create embeddings with consistent local white box models. We also compare SLISEMAP to other model-agnostic local explanation methods and show that SLISEMAP provides comparable explanations and that the visualisations can give a broader understanding of black box regression and classification models.

cs.LG↗