Search Results for author:
Found 11 papers, 4 papers with code
Robust Graph Representation Learning via Neural Sparsification
Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection.
Neural Lithography: Close the Design-to-Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Photolithography Simulator
To bridge this gap, we, for the first time, propose a fully differentiable design framework that integrates a pre-trained photolithography simulator into the model-based optical design loop.
Learning to Read Analog Gauges from Synthetic Data
We propose a two-stage CNN pipeline that identifies the key structural components of an analog gauge and outputs an angular reading.
DEEP$^2$: Deep Learning Powered De-scattering with Excitation Patterning
Limited throughput is a key challenge in in-vivo deep-tissue imaging using nonlinear optical microscopy.
Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels
To provide an alternative, we propose a novel approach that utilizes monocular RGB images and point clouds to generate pseudo scene flow labels for training scene flow networks.
CoRGi: Content-Rich Graph Neural Networks with Attention
Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges).
SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation
We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds.
High Quality Disparity Remapping With Two-Stage Warping
In the second stage, we derive another warping model to refine warping results in less important regions by eliminating serious distortions in shape, disparity and 3D structure.
Vocal Bursts Intensity Prediction
Vocal Bursts Valence Prediction
A Simulation-free Group Sequential Design with Max-combo Tests in the Presence of Non-proportional Hazards
The integration and application of maxcombo tests in interim analyses often require extensive simulation studies.
Methodology
Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection
A new statistical procedure (Model-X \cite{candes2018}) has provided a way to identify important factors using any supervised learning method controlling for FDR.
Methodology
Learning Deep Network Representations with Adversarially Regularized Autoencoders
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.
