Search Results for author:
Found 19 papers, 4 papers with code
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation
Inspired by this discovery, we then propose approaches to improving it, with respect to model structure and model training, to make the deep decoder practical in NMT.
Robustness of the data-driven approach in limited angle tomography
The limited angle Radon transform is notoriously difficult to invert due to the ill-posedness.
SAMDA: Leveraging SAM on Few-Shot Domain Adaptation for Electronic Microscopy Segmentation
It has been shown that traditional deep learning methods for electronic microscopy segmentation usually suffer from low transferability when samples and annotations are limited, while large-scale vision foundation models are more robust when transferring between different domains but facing sub-optimal improvement under fine-tuning.
An Extreme Learning Machine-Based Method for Computational PDEs in Higher Dimensions
With the second method the high-dimensional PDE problem is reformulated through a constrained expression based on an Approximate variant of the Theory of Functional Connections (A-TFC), which avoids the exponential growth in the number of terms of TFC as the dimension increases.
A Projection-Based Approach for Distributed Energy Resources Aggregation
Aggregating distributed energy resources (DERs) is of great significance to improve the overall operational efficiency of smart grid.
Diffusion-Augmented Depth Prediction with Sparse Annotations
We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner.
Neural Video Depth Stabilizer
Video depth estimation aims to infer temporally consistent depth.
Ranked #16 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
We show that, our algorithm obtains an $\varepsilon$-optimal policy with only $\widetilde{O}(\frac{\text{poly}(d)}{\varepsilon^3})$ samples, where $\varepsilon$ is the suboptimality gap and $d$ is a complexity measure of the function class approximating the policy.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural Networks
The presented method has been compared with the physics-informed neural network method.
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis
We study the problem of novel view synthesis of objects from a single image.
Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
Temporal consistency is the key challenge of video depth estimation.
Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path
This paper presents a novel method for nested named entity recognition.
Ranked #12 on
Nested Named Entity Recognition
on GENIA
Inverse Problem of Nonlinear Schrödinger Equation as Learning of Convolutional Neural Network
In this work, we use an explainable convolutional neural network (NLS-Net) to solve an inverse problem of the nonlinear Schr\"odinger equation, which is widely used in fiber-optic communications.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
Kingsoft's Neural Machine Translation System for WMT19
This paper describes the Kingsoft AI Lab{'}s submission to the WMT2019 news translation shared task.
Convolutional neural networks in phase space and inverse problems
We study inverse problems consisting on determining medium properties using the responses to probing waves from the machine learning point of view.
