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Found 38 papers, 14 papers with code
GAN-based Garment Generation Using Sewing Pattern Images
The generation of realistic apparel model has become increasingly popular as a result of the rapid pace of change in fashion trends and the growing need for garment models in various applications such as virtual try-on.
Sensitivity-Informed Augmentation for Robust Segmentation
Then, we model the sensitivity curve using the adaptive SA and sample perturbation hyperparameter values accordingly.
SHARE: Single-view Human Adversarial REconstruction
We perform a comprehensive analysis on the impact of camera poses on HPS reconstruction outcomes.
Towards General and Efficient Online Tuning for Spark
The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance.
OpenBox: A Python Toolkit for Generalized Black-box Optimization
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning.
Transfer Learning for Bayesian Optimization: A Survey
A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization.
Rover: An online Spark SQL tuning service via generalized transfer learning
When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production.
DivBO: Diversity-aware CASH for Ensemble Learning
To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems.
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence
In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.
Efficient End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
DFG-NAS: Deep and Flexible Graph Neural Architecture Search
Graph neural networks (GNNs) have been intensively applied to various graph-based applications.
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
Transfer Learning based Search Space Design for Hyperparameter Tuning
The extensive experiments show that our approach considerably boosts BO by designing a promising and compact search space instead of using the entire space, and outperforms the state-of-the-arts on a wide range of benchmarks, including machine learning and deep learning tuning tasks, and neural architecture search.
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning
With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important.
AutoJoin: Efficient Adversarial Training for Robust Maneuvering via Denoising Autoencoder and Joint Learning
As a result of increasingly adopted machine learning algorithms and ubiquitous sensors, many 'perception-to-control' systems are developed and deployed.
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats
Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics.
PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
Through deconstructing the message passing mechanism, PasCa presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs.
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck.
Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios
To enhance the visual perception capability of human-vehicle cooperative driving, this paper proposed a cooperative visual perception model.
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering
We introduce a simple yet effective framework for improving the robustness of learning algorithms against image corruptions for autonomous driving.
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup
In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2).
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies
Designing neural architectures requires immense manual efforts.
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization
Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets.
ROD: Reception-aware Online Distillation for Sparse Graphs
Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
OpenBox: A Generalized Black-box Optimization Service
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
Ranked #2 on
2D Semantic Segmentation
on xBD
Secure Artificial Intelligence of Things for Implicit Group Recommendations
The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications such as group recommender systems.
GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing
In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-based tasks.
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images
For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.
Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples
To ensure the wide adoption and safety of autonomous driving, the vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.
A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data
Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway.
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery
Fast and effective responses are required when a natural disaster (e. g., earthquake, hurricane, etc.)
Ranked #3 on
2D Semantic Segmentation
on xBD
Design of phase III trials with long-term survival outcomes based on short-term binary results
We propose to base the comparison between arms on the difference of the restricted mean survival times, and show how the effect size and sample size for overall survival rely on the probability of the binary response and the survival distribution by response status, both for each treatment arm.
Methodology Applications
Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification
Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification.
