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Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning. In this paper, we study the problem of using LLMs to detect tabular anomalies and show that pre-trained LLMs are zero-shot…
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications. However, it has received relatively little attention in the broader ML…
Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the anomaly detection model generates a "normal" version of an…
We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. Here, regularized autoencoders provide a popular approach by learning the identity mapping on the…
The Standard Model of particle physics is extremely well tested and yet is not believed to be a theory of everything. Many extensions of the Standard Model predict the existence of new particles. The Large Hadron Collider (LHC) is a high…
Supervisory Control and Data Acquisition (SCADA) systems often serve as the nervous system for substations within power grids. These systems facilitate real-time monitoring, data acquisition, control of equipment, and ensure smooth and…
Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research…
Results are reported from a search for new particles that decay into a photon and two gluons, in events with jets. Novel jet substructure techniques are developed that allow photons to be identified in an environment densely populated with…
Anomaly detection techniques are growing in importance at the Large Hadron Collider (LHC), motivated by the increasing need to search for new physics in a model-agnostic way. In this work, we provide a detailed comparative study between a…
The challenge of visual grounding and masking in multimodal machine translation (MMT) systems has encouraged varying approaches to the detection and selection of visually-grounded text tokens for masking. We introduce new methods for…
In this article we present the application of classical and quantum-classical hybrid anomaly detection schemes to explore exotic configuration with anomalous features. We consider the Anderson model as a prototype where we define two types…
We present an interpretable implementation of the autoencoding algorithm, used as an anomaly detector, built with a forest of deep decision trees on FPGA, field programmable gate arrays. Scenarios at the Large Hadron Collider at CERN are…
In recent years, interest has grown in alternative strategies for the search for New Physics beyond the Standard Model. One envisaged solution lies in the development of anomaly detection algorithms based on unsupervised machine learning…
Experiments at a future $e^{+}e^{-}$ collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine…
We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural network to encode simulated QCD "event space" dijets into a…
Frame prediction based on AutoEncoder plays a significant role in unsupervised video anomaly detection. Ideally, the models trained on the normal data could generate larger prediction errors of anomalies. However, the correlation between…
In the Standard Model, the couplings between gauge bosons are tightly constrained by the principles of gauge symmetry and renormalizability. However, the presence of anomalous couplings suggests the possibility of new physics beyond the…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…
High-momentum top quarks are a natural physical system in collider experiments for testing models of new physics, and jet substructure methods are key both to exploiting their largest decay mode and to assuaging resolution difficulties as…
We propose a robust method to identify anomalous jets by vetoing QCD-jets. The robustness of this method ensures that the distribution of the proposed discriminating variable (which allows us to veto QCD-jets) remains unaffected by the…