4 citations · 6 across the 5 of their papers we have counts for
6 papers
Gauss What You Need: Compact Gaussian Splatting Across Scene Scales
Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu +1
3D Gaussian Splatting reconstructs a scene as a collection of Gaussian primitives from a set of posed photographs called the capture. The number of primitives used to represent the…
ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations
Afif Boudaoud, Lisa Gaedke-Merzhäuser, Alexandros Nikolaos Ziogas +6
Spatio-temporal Bayesian inference drives environmental and health sciences using latent Gaussian models. Integrated Nested Laplace Approximations (INLA) enable inference for these…
PerfDojo: Automated ML Library Generation for Heterogeneous Architectures
Andrei Ivanov, Siyuan Shen, Gioele Gottardo +5
The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a signifi…
LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization
Massinissa Merouani, Afif Boudaoud, Riyadh Baghdadi
The advancement of machine learning for compiler optimization, particularly within the polyhedral model, is constrained by the scarcity of large-scale, public performance datasets.…
DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing
Afif Boudaoud, Alexandru Calotoiu, Marcin Copik +1
Automatic differentiation (AD) is a set of techniques that systematically applies the chain rule to compute the gradients of functions without requiring human intervention. Althoug…
LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers
Massinissa Merouani, Afif Boudaoud, Iheb Nassim Aouadj +7
While polyhedral compilers have shown success in implementing advanced code transformations, they still face challenges in selecting the ones that lead to the most profitable speed…