Search Results for author:
Found 11 papers, 1 papers with code
BezierFormer: A Unified Architecture for 2D and 3D Lane Detection
B\'{e}zierFormer formulate queries as B\'{e}zier control points and incorporate a novel B\'{e}zier curve attention mechanism.
REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models
Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG).
A survey on deep learning approaches for data integration in autonomous driving system
The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment.
NeMO: Neural Map Growing System for Spatiotemporal Fusion in Bird's-Eye-View and BDD-Map Benchmark
We also provide a new scene-level BEV map evaluation setting along with the corresponding baseline for a more comprehensive comparison.
Anchor Free remote sensing detector based on solving discrete polar coordinate equation
Finally, referring to the calculation idea of horizontal IoU, we design a rotating IoU based on the split polar coordinate plane, namely JIoU, which is expressed as the intersection ratio following discretization of the inner ellipse of the rotating bounding box, to solve the correlation between angle and side length in the regression process of the rotating bounding box.
Tensor Network for Supervised Learning at Finite Temperature
Tensor network is chosen since it is the best platform to introduce thermal fluctuation.
Overcoming Language Priors with Self-supervised Learning for Visual Question Answering
Our method can compensate for the data biases by generating balanced data without introducing external annotations.
Symmetrical Graph Neural Network for Quantum Chemistry, with Dual R/K Space
Most of current neural network models in quantum chemistry (QC) exclude the molecular symmetry, separate the well-correlated real space (R space), and momenta space (K space) into two individuals, which lack the essential physics in molecular chemistry.
Chemical Physics
Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation
In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem.
The Newton Scheme for Deep Learning
Then, by classifying the training model as a quick procedure of 'force pattern' recognition, we developed the Newton physics-based NS scheme.
Pixel Chem: A Representation for Predicting Material Properties with Neural Network
In this work we developed a new representation of the chemical information for the machine learning models, with benefits from both the real space (R-space) and energy space (K-space).
