22 citations · 29 across the 6 of their papers we have counts for
7 papers
Can LLMs predict the outcomes of machine learning experiments?
Ayush Bansal, Aldo Pareja, Sean Klein +2
Recent advances in AI make Recursive Self-Improvement (RSI) increasingly plausible. Large Language Models (LLMs) can already propose, select, and run Machine Learning (ML) experime…
Locality in Image Diffusion Models Emerges from Data Statistics
Artem Lukoianov, Chenyang Yuan, Justin Solomon +1
Recent work has shown that the generalization ability of image diffusion models arises from the locality properties of the trained neural network. In particular, when denoising a p…
Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning
Haitz Sáez de Ocáriz Borde, Artem Lukoianov, Anastasis Kratsios +2
We propose Scalable Message Passing Neural Networks (SMPNNs) and demonstrate that, by integrating standard convolutional message passing into a Pre-Layer Normalization Transformer-…
Score Distillation via Reparametrized DDIM
Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan Greenewald +4
While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cart…
HybridSDF: Combining Deep Implicit Shapes and Geometric Primitives for 3D Shape Representation and Manipulation
Subeesh Vasu, Nicolas Talabot, Artem Lukoianov +3
Deep implicit surfaces excel at modeling generic shapes but do not always capture the regularities present in manufactured objects, which is something simple geometric primitives a…
DeepMesh: Differentiable Iso-Surface Extraction
Benoit Guillard, Edoardo Remelli, Artem Lukoianov +4
Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary…
MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan R. Richter +4
Geometric Deep Learning has recently made striking progress with the advent of continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of arbitrary…