32 citations · 89 across the 19 of their papers we have counts for
21 papers
Improving the matrix multiplication exponent with modern optimization and AlphaEvolve
Emilien Dupont, Marvin Eisenberger, Borislav Kozlovskii +7
The current best bounds on the matrix multiplication exponent are obtained through a refinement of the laser method called combination loss analysis (Duan et al., 2022; William…
ArchAgent v2: A Case Study with the Data Prefetching Championship
Abraham Gonzalez, Raghav Gupta, Akanksha Jain +12
Agentic artificial intelligence has shown great promise in automating algorithm design, but scaling similar techniques to computer microarchitecture discovery remains challenging d…
Graph Neural Networks Are Not Continuous Across Graph Resolutions
Christian Koke, Yuesong Shen, Abhishek Saroha +4
We show that contrary to conventional wisdom in the community, graph neural networks (GNNs) are not continuous with respect to all natural modes of graph convergence. As a result,…
ArchAgent: Agentic AI-driven Computer Architecture Discovery
Raghav Gupta, Akanksha Jain, Abraham Gonzalez +10
Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems have demonstrated significant…
AlphaEvolve: A coding agent for scientific and algorithmic discovery
Alexander Novikov, Ngân Vũ, Marvin Eisenberger +15
In this white paper, we present AlphaEvolve, an evolutionary coding agent that substantially enhances capabilities of state-of-the-art LLMs on highly challenging tasks such as tack…
Partial-to-Partial Shape Matching with Geometric Consistency
Viktoria Ehm, Maolin Gao, Paul Roetzer +3
Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. A prominent challenge are partial-to-partial shape matc…
Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation
Dongliang Cao, Marvin Eisenberger, Nafie El Amrani +2
Although 3D shape matching and interpolation are highly interrelated, they are often studied separately and applied sequentially to relate different 3D shapes, thus resulting in su…
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation
Aysim Toker, Marvin Eisenberger, Daniel Cremers +1
In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet, a prevalent limitation of supervised learning techniques rem…
ResolvNet: A Graph Convolutional Network with multi-scale Consistency
Christian Koke, Abhishek Saroha, Yuesong Shen +2
It is by now a well known fact in the graph learning community that the presence of bottlenecks severely limits the ability of graph neural networks to propagate information over l…
Geometrically Consistent Partial Shape Matching
Viktoria Ehm, Paul Roetzer, Marvin Eisenberger +3
Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics, which is for example relevant for tasks like shape interpolation, pose transfer, or…
SIGMA: Scale-Invariant Global Sparse Shape Matching
Maolin Gao, Paul Roetzer, Marvin Eisenberger +4
We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes. To this end, we introduce a projected Lapl…
G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +1
We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence. Rather than treating a collection of input poses as…
A Unified Framework for Implicit Sinkhorn Differentiation
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +2
The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning framework…
Implicit Shape Completion via Adversarial Shape Priors
Abhishek Saroha, Marvin Eisenberger, Tarun Yenamandra +1
We present a novel neural implicit shape method for partial point cloud completion. To that end, we combine a conditional Deep-SDF architecture with learned, adversarial shape prio…
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
Aysim Toker, Lukas Kondmann, Mark Weber +10
Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires b…
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
Marvin Eisenberger, David Novotny, Gael Kerchenbaum +4
We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i.e. in a single feed forward pass, a smooth interpolation and po…
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +1
We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network. This approach is based on smooth…
Hamiltonian Dynamics for Real-World Shape Interpolation
Marvin Eisenberger, Daniel Cremers
We revisit the classical problem of 3D shape interpolation and propose a novel, physically plausible approach based on Hamiltonian dynamics. While most prior work focuses on synthe…
Smooth Shells: Multi-Scale Shape Registration with Functional Maps
Marvin Eisenberger, Zorah Lähner, Daniel Cremers
We propose a novel 3D shape correspondence method based on the iterative alignment of so-called smooth shells. Smooth shells define a series of coarse-to-fine shape approximations…
Divergence-Free Shape Interpolation and Correspondence
Marvin Eisenberger, Zorah Lähner, Daniel Cremers
We present a novel method to model and calculate deformation fields between shapes embedded in . Our framework combines naturally interpolating the two input shapes a…
Fast sampling of parameterised Gaussian random fields
Jonas Latz, Marvin Eisenberger, Elisabeth Ullmann
Gaussian random fields are popular models for spatially varying uncertainties, arising for instance in geotechnical engineering, hydrology or image processing. A Gaussian random fi…