Search Results for author:
Found 22 papers, 6 papers with code
CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
Our work significantly outperforms the state-of-the-art for three-finger robotic hands.
CP$^3$: Channel Pruning Plug-in for Point-based Networks
CP$^3$ is elaborately designed to leverage the characteristics of point clouds and PNNs in order to enable 2D channel pruning methods for PNNs.
Label-Guided Auxiliary Training Improves 3D Object Detector
Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently.
I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding
To this end, we develop an Importance Sampling based distance metric -- I-SEA -- which enjoys the properties of a metric while consistently achieving superior performance for machine learning tasks such as classification and representation learning.
RGB-Depth Fusion GAN for Indoor Depth Completion
In this paper, we design a novel two-branch end-to-end fusion network, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map.
CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving
Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors.
CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization
Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have explored model pruning to obtain lightweight and efficient networks.
Human Pose Transfer with Augmented Disentangled Feature Consistency
Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others.
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
In deep model compression, the recent finding "Lottery Ticket Hypothesis" (LTH) (Frankle & Carbin, 2018) pointed out that there could exist a winning ticket (i. e., a properly pruned sub-network together with original weight initialization) that can achieve competitive performance than the original dense network.
Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning
Deep learning has made tremendous success in computer vision, natural language processing and even visual-semantic learning, which requires a huge amount of labeled training data.
Fast Object Detection with Latticed Multi-Scale Feature Fusion
In this work, we present a novel module, the Fluff block, to alleviate drawbacks of current multi-scale fusion methods and facilitate multi-scale object detection.
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction
In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos.
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search
The convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment.
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks.
D$^2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to boost the efficacy of such datasets to improve learning-based models.
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Multi-Rate Multivariate Time Series (MR-MTS) are the multivariate time series observations which come with various sampling rates and encode multiple temporal dependencies.
Relational Multi-Instance Learning for Concept Annotation from Medical Time Series
Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data.
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications.
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
We use this generative model together with a convolutional neural network (CNN) based prediction model to improve the onset prediction performance.
Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding
To account for high dimensionality, we use the embedding medical features in the CNN model which hold the natural medical concepts.
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
Ranked #4 on
Multivariate Time Series Forecasting
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Multivariate Time Series Forecasting
Multivariate Time Series Imputation
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Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research.
