Search Results for author:
Found 15 papers, 5 papers with code
CoDo: Contrastive Learning with Downstream Background Invariance for Detection
To bridge the performance gap, we propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo).
Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning
With this method, Yuan 1. 0, the current largest singleton language model with 245B parameters, achieves excellent performance on thousands GPUs during training, and the state-of-the-art results on NLP tasks.
Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing
This paper proposes a set of testing strategies for testing machine learning applications in the framework of the datamorphism testing methodology.
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
It is still an open question to leverage various types of information under the BERT framework.
End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph
We conduct our experiments on the proposed real-world interior layout dataset that contains $191208$ designs from the professional designers.
Deep Layout of Custom-size Furniture through Multiple-domain Learning
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene.
Structural Plan of Indoor Scenes with Personalized Preferences
In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferences of the property owners.
Adversarial Model for Rotated Indoor Scenes Planning
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated.
Hyper-Parameter Optimization: A Review of Algorithms and Applications
This study next reviews major services and toolkits for HPO, comparing their support for state-of-the-art searching algorithms, feasibility with major deep learning frameworks, and extensibility for new modules designed by users.
Morphy: A Datamorphic Software Test Automation Tool
This paper focuses on the datamorphism combination strategies by giving their definitions and implementation algorithms.
Datamorphic Testing: A Methodology for Testing AI Applications
With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their quality.
Less Is Better: Unweighted Data Subsampling via Influence Function
In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.
Anion charge-lattice volume dependent Li ion migration in compounds with the face-centered cubic anion frameworks
In this work, the face-centered cubic (fcc) anion frameworks were creatively constructed to study the effects of anion charge and lattice volume on the stability of lithium ion occupation and lithium ion migration.
Applied Physics
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition
In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments.
Ranked #35 on
Skeleton Based Action Recognition
on NTU RGB+D
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors
In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors.
