164 citations · 932 across the 24 of their papers we have counts for
24 papers
GPT-4o System Card
OpenAI, :, Aaron Hurst +416
GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's…
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
Eric Wallace, Kai Xiao, Reimar Leike +3
Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompt…
Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference
Gordian Edenhofer, Philipp Frank, Jakob Roth +7
Imaging is the process of transforming noisy, incomplete data into a space that humans can interpret. NIFTy is a Bayesian framework for imaging and has already successfully been ap…
A 3D View of Orion: I. Barnard's Loop
Michael M. Foley, Alyssa Goodman, Catherine Zucker +11
Barnard's Loop is a famous arc of H emission located in the Orion star-forming region. Here, we provide evidence of a possible formation mechanism for Barnard's Loop and compare…
Correlation as a Resource in Unitary Quantum Measurements
Vishal Johnson, Ashmeet Singh, Reimar Leike +2
Quantum measurement is a physical process. What physical resources and constraints does quantum mechanics require for measurement to produce the classical world we observe? Treatin…
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR)
Gordian Edenhofer, Reimar H. Leike, Philipp Frank +1
Gaussian Processes (GPs) are highly expressive, probabilistic models. A major limitation is their computational complexity. Naively, exact GP inference requires …
The Galactic 3D large-scale dust distribution via Gaussian process regression on spherical coordinates
R. H. Leike, G. Edenhofer, J. Knollmüller +3
Knowing the Galactic 3D dust distribution is relevant for understanding many processes in the interstellar medium and for correcting many astronomical observations for dust absorpt…
On the Three-Dimensional Structure of Local Molecular Clouds
Catherine Zucker, Alyssa Goodman, João Alves +9
We leverage the 1 pc spatial resolution of the Leike et al. 2020 3D dust map to characterize the three-dimensional structure of nearby molecular clouds ( pc). We st…
The Per-Tau Shell: A Giant Star-Forming Spherical Shell Revealed by 3D Dust Observations
Shmuel Bialy, Catherine Zucker, Alyssa Goodman +6
A major question in the field of star formation is how molecular clouds form out of the diffuse Interstellar Medium (ISM). Recent advances in 3D dust mapping are revolutionizing ou…
Geometric variational inference
Philipp Frank, Reimar Leike, Torsten A. Enßlin
Efficiently accessing the information contained in non-linear and high dimensional probability distributions remains a core challenge in modern statistics. Traditionally, estimator…
Optical reconstruction of dust in the region of SNR RX J1713.7-3946 from astrometric data
Reimar Leike, Silvia Celli, Alberto Krone-Martins +5
The origin of the radiation observed in the region of the supernova remnant RX J1713.7-3946, one of the brightest TeV emitters, has been debated since its discovery. The existence…
Towards Bayesian Data Compression
Johannes Harth-Kitzerow, Reimar Leike, Philipp Arras +1
In order to handle large data sets omnipresent in modern science, efficient compression algorithms are necessary. Here, a Bayesian data compression (BDC) algorithm that adapts to t…
Bayesian decomposition of the Galactic multi-frequency sky using probabilistic autoencoders
Sara Milosevic, Philipp Frank, Reimar H. Leike +2
All-sky observations of the Milky Way show both Galactic and non-Galactic diffuse emission, for example from interstellar matter or the cosmic microwave background (CMB). The diffe…
Spatially Resolved Ultraviolet Spectroscopy of the Great Dimming of Betelgeuse
Andrea K. Dupree, Klaus G. Strassmeier, Lynn D. Matthews +9
The bright supergiant, Betelgeuse (Alpha Orionis, HD 39801) experienced a visual dimming during 2019 December and the first quarter of 2020 reaching an historic minimum 2020 Februa…
Comparison of classical and Bayesian imaging in radio interferometry
Philipp Arras, Hertzog L. Bester, Richard A. Perley +4
CLEAN, the commonly employed imaging algorithm in radio interferometry, suffers from a number of shortcomings: in its basic version it does not have the concept of diffuse flux, an…
Resolving nearby dust clouds
R. H. Leike, M. Glatzle, T. A. Enßlin
Aims: Mapping the interstellar medium in 3D provides a wealth of insights into its inner working. The Milky Way is the only galaxy for which detailed 3D mapping can be achieved in…
Variable structures in M87* from space, time and frequency resolved interferometry
Philipp Arras, Philipp Frank, Philipp Haim +4
Observing the dynamics of compact astrophysical objects provides insights into their inner workings, thereby probing physics under extreme conditions. The immediate vicinity of an…
Unified Radio Interferometric Calibration and Imaging with Joint Uncertainty Quantification
Philipp Arras, Philipp Frank, Reimar Leike +2
The data reduction procedure for radio interferometers can be viewed as a combined calibration and imaging problem. We present an algorithm that unifies cross-calibration, self-cal…
Field dynamics inference for local and causal interactions
Philipp Frank, Reimar Leike, Torsten A. Enßlin
Inference of fields defined in space and time from observational data is a core discipline in many scientific areas. This work approaches the problem in a Bayesian framework. The p…
Charting nearby dust clouds using Gaia data only
R. H. Leike, T. A. Enßlin
Aims: Highly resolved maps of the local Galactic dust are an important ingredient for sky emission models. In nearly the whole electromagnetic spectrum one can see imprints of dust…
Towards information optimal simulation of partial differential equations
Reimar H. Leike, Torsten A. Enßlin
Most simulation schemes for partial differential equations (PDEs) focus on minimizing a simple error norm of a discretized version of a field. This paper takes a fundamentally diff…
NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters
Theo Steininger, Jait Dixit, Philipp Frank +10
NIFTy, "Numerical Information Field Theory", is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulat…
Optimal Belief Approximation
Reimar H. Leike, Torsten A. Enßlin
In Bayesian statistics probability distributions express beliefs. However, for many problems the beliefs cannot be computed analytically and approximations of beliefs are needed. W…
Operator Calculus for Information Field Theory
Reimar H. Leike, Torsten A. Enßlin
Signal inference problems with non-Gaussian posteriors can be hard to tackle. Through using the concept of Gibbs free energy these posteriors are rephrased as Gaussian posteriors f…