Search Results for author:
Found 22 papers, 5 papers with code
Towards Training Reproducible Deep Learning Models
However, DL models are challenging to be reproduced due to issues like randomness in the software (e. g., DL algorithms) and non-determinism in the hardware (e. g., GPU).
Latent Network Embedding via Adversarial Auto-encoders
To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders.
Visual Anomaly Detection for Images: A Survey
Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision.
Deep Kernel Gaussian Process Based Financial Market Predictions
However, the deep kernel Gaussian Process has not been applied to forecast the conditional returns and volatility in financial market to the best of our knowledge.
Multiple HC$_3$N line observations towards 19 Galactic massive star-forming regions
We performed observations of the HC$_3$N (24-23, 17-16, 11-10, 8-7) lines towards a sample consisting of 19 Galactic massive star-forming regions with the Arizona Radio Observatory 12-m and Caltech Submillimeter Observatory 10. 4-m telescopes.
Astrophysics of Galaxies Solar and Stellar Astrophysics
Pyramid scheme in stock market: a kind of financial market simulation
Artificial stock market simulation based on agent is an important means to study financial market.
Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism
In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.
DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation
Concretely, we develop a multi-scale regional feature generator that can generate multiple spatial context-aware representations from pre-trained deep convolutional networks for every subregion of an image.
Ranked #67 on
Anomaly Detection
on MVTec AD
Learning to Incorporate Structure Knowledge for Image Inpainting
This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works.
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES
Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task.
s-LWSR: Super Lightweight Super-Resolution Network
Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance.
Learning from Label Proportions with Generative Adversarial Networks
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available.
A Novel Large-scale Ordinal Regression Model
Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels.
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection
Given that the internal structure and parameters of the detection system are unknown to attackers, the adversarial attack examples perform the black-box attacks against the detection system.
Research on Artificial Intelligence Ethics Based on the Evolution of Population Knowledge Base
The unclear development direction of human society is a deep reason for that it is difficult to form a uniform ethical standard for human society and artificial intelligence.
Diffusion Based Network Embedding
In order to solve the limitations, we propose in this paper a network diffusion based embedding method.
A generalized concept-cognitive learning: A machine learning viewpoint
However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described.
Three IQs of AI Systems and their Testing Methods
The rapid development of artificial intelligence has brought the artificial intelligence threat theory as well as the problem about how to evaluate the intelligence level of intelligent products.
Intelligence Quotient and Intelligence Grade of Artificial Intelligence
Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy.
Automatic Road Crack Detection Using Random Structured Forests
Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high- performance crack detector, which can identify arbitrarily com- plex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively.
A Study on Artificial Intelligence IQ and Standard Intelligent Model
Currently, potential threats of artificial intelligence (AI) to human have triggered a large controversy in society, behind which, the nature of the issue is whether the artificial intelligence (AI) system can be evaluated quantitatively.
Quantitative Analysis of Whether Machine Intelligence Can Surpass Human Intelligence
On the basis of traditional IQ, this article presents the Universal IQ test method suitable for both the machine intelligence and the human intelligence.
