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Found 22 papers, 12 papers with code
A Lorentz-Equivariant Transformer for All of the LHC
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider.
Does equivariance matter at scale?
Given large data sets and sufficient compute, is it beneficial to design neural architectures for the structure and symmetries of each problem?
Differentiable and Learnable Wireless Simulation with Geometric Transformers
Modelling the propagation of electromagnetic wireless signals is critical for designing modern communication systems.
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architecture for high-energy physics.
FoMo Rewards: Can we cast foundation models as reward functions?
We explore the viability of casting foundation models as generic reward functions for reinforcement learning.
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
The Geometric Algebra Transformer (GATr) is a versatile architecture for geometric deep learning based on projective geometric algebra.
Geometric Algebra Transformer
In this paper we introduce the Geometric Algebra Transformer (GATr), a general-purpose architecture for geometric data.
Deconfounding Imitation Learning with Variational Inference
Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent.
Weakly supervised causal representation learning
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone.
Simulation Intelligence: Towards a New Generation of Scientific Methods
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
We introduce a video compression algorithm based on instance-adaptive learning.
Hierarchical clustering in particle physics through reinforcement learning
Particle physics experiments often require the reconstruction of decay patterns through a hierarchical clustering of the observed final-state particles.
Simulation-based inference methods for particle physics
Our predictions for particle physics processes are realized in a chain of complex simulators.
Flows for simultaneous manifold learning and density estimation
We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.
The frontier of simulation-based inference
Many domains of science have developed complex simulations to describe phenomena of interest.
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics.
MadMiner: Machine learning-based inference for particle physics
Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods.
Effective LHC measurements with matrix elements and machine learning
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled.
Likelihood-free inference with an improved cross-entropy estimator
We extend recent work (Brehmer, et.
Mining gold from implicit models to improve likelihood-free inference
Simulators often provide the best description of real-world phenomena.
A Guide to Constraining Effective Field Theories with Machine Learning
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments.
Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field theories at the LHC.
