Search Results for author:
Found 34 papers, 9 papers with code
MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information
Here we consider a matrix factorization problem by utilizing auxiliary information, which is massively available in real applications, to overcome the challenges caused by poor data quality.
Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation
In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues.
Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions
The learned knowledge flows across branches along two directions: a cross direction (disparity guides distribution in ACS) and a parallel direction (disparity guides disparity in APS).
Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs
Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae.
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics
The mechanism reduction is modeled as an optimization problem on Boolean space, where a Boolean vector, each entry corresponding to a species, represents a reduced mechanism.
Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion
Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e. g., subcortical nuclei, etc.
Deep Natural Language Processing for LinkedIn Search
Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help.
ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction Pipeline
The purpose of the task was to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph.
Power System Transient Modeling and Simulation using Integrated Circuit
Transient stability analysis (TSA) plays an important role in power system analysis to investigate the stability of power system.
An Upper Limit of Decaying Rate with Respect to Frequency in Deep Neural Network
frequency in DNN training.
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on
Reading Comprehension (Zero-Shot)
on CMRC 2018
Cloze (multi-choices) (Few-Shot)
Cloze (multi-choices) (One-Shot)
+18
Fourier-domain Variational Formulation and Its Well-posedness for Supervised Learning
A supervised learning problem is to find a function in a hypothesis function space given values on isolated data points.
Defocus Blur Detection via Salient Region Detection Prior
Defocus blur Detection aims to separate the out-of-focus and depth-of-field areas in photos, which is an important work in computer vision.
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences.
Evaluating Load Models and Their Impacts on Power Transfer Limits
Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits.
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models
Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society.
Siamese Neural Networks for Class Activity Detection
Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms.
2D-Dirac surface states and bulk gap probed via quantum capacitance in a 3D topological insulator
BiSbTeSe$_2$ is a 3D topological insulator (3D-TI) with Dirac type surface states and low bulk carrier density, as donors and acceptors compensate each other.
Mesoscale and Nanoscale Physics
Learning Multi-level Dependencies for Robust Word Recognition
Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken with the presence of noises.
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach
However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters.
Automatic Short Answer Grading via Multiway Attention Networks
Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads.
Deep Knowledge Tracing with Side Information
Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems.
Recommender Systems with Heterogeneous Side Information
In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items.
R-Transformer: Recurrent Neural Network Enhanced Transformer
Recurrent Neural Networks have long been the dominating choice for sequence modeling.
Ranked #1 on
Music Modeling
on Nottingham
Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning
Modern power grids are experiencing grand challenges caused by the stochastic and dynamic nature of growing renewable energy and demand response.
Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems.
Probabilistic Load Forecasting via Point Forecast Feature Integration
In the first stage, all related features are utilized to train a point forecast model and also obtain the feature importance.
Submodular Load Clustering with Robust Principal Component Analysis
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS).
Semi-supervised mp-MRI Data Synthesis with StitchLayer and Auxiliary Distance Maximization
Experimental results show that our method can effectively synthesize a large variety of mpMRI images which contain meaningful CS PCa lesions, display a good visual quality and have the correct paired relationship.
Synthesizing Multi-Parameter Magnetic Resonance Imaging (Mp-Mri) Data
A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation
This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT).
A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties
Conventional control strategies usually produce large disturbances to buses during charging and discharging (C&D) processes of UCs, which significantly degrades the power quality and system performance, especially under fast C&D modes.
Power Market Price Forecasting via Deep Learning
Then the raw input and output data are preprocessed by unit scaling, and the trained network is tested on the real price data under different input lengths, forecasting horizons and data sizes.
Linked Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation.
Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI
Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions’ locations.
