Search Results for author:
Found 11 papers, 8 papers with code
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science.
A Transformer Model for Symbolic Regression towards Scientific Discovery
Once trained, we apply our best model to the SRSD datasets (Symbolic Regression for Scientific Discovery datasets) which yields state-of-the-art results using the normalized tree-based edit distance, at no extra computational cost.
WeaveNet for Approximating Two-sided Matching Problems
Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society.
NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields
We present NeuralLabeling, a labeling approach and toolset for annotating a scene using either bounding boxes or meshes and generating segmentation masks, affordance maps, 2D bounding boxes, 3D bounding boxes, 6DOF object poses, depth maps and object meshes.
Neural Structure Fields with Application to Crystal Structure Autoencoders
We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks.
SRSD: Rethinking Datasets of Symbolic Regression for Scientific Discovery
Symbolic Regression (SR) is a task of recovering mathematical expressions from given data and has been attracting attention from the research community to discuss its potential for scientific discovery.
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
For each of the 120 SRSD datasets, we carefully review the properties of the formula and its variables to design reasonably realistic sampling ranges of values so that our new SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method can (re)discover physical laws from such datasets.
3D Point Cloud Registration with Learning-based Matching Algorithm
Instead of only optimizing the feature extractor for a matching algorithm, we propose a learning-based matching module optimized to the jointly-trained feature extractor.
Point Cloud Pre-Training With Natural 3D Structures
Moreover, the PC-FractalDB pre-trained model is especially effective in training with limited data.
Ranked #18 on
3D Object Detection
on SUN-RGBD val
(using extra training data)
WeaveNet: A Differentiable Solver for Non-linear Assignment Problems
Assignment, a task to match a limited number of elements, is a fundamental problem in informatics.
WeaveNet for Approximating Assignment Problems
Assignment, a task to match a limited number of elements, is a fundamental problem in informatics.
