Overfitting

WiMi Developed PBO-Based Dynamic Optimization Algorithm That Can Be Applied to Blockchain and Bitcoin Trading Strategies

Retrieved on: 
Monday, January 8, 2024

In order to obtain models that perform better out-of-sample, WiMi has developed a dynamic optimization algorithm based on PBO, namely the "PBO-DOA algorithm".

Key Points: 
  • In order to obtain models that perform better out-of-sample, WiMi has developed a dynamic optimization algorithm based on PBO, namely the "PBO-DOA algorithm".
  • This algorithm is important in quantitative trading because it can dynamically optimize the model's parameters to help investors build optimal portfolios.
  • WiMi's PBO-DOA allows for more scientific optimization of blockchain and bitcoin trading strategies, thereby increasing portfolio returns and controlling risk.
  • Algorithms in blockchain and bitcoin trading strategies are over-optimizing the weight allocation of portfolios, maximizing returns, and controlling risks.

Gretel Debuts on Microsoft Azure Marketplace & Selected for Microsoft for Startups Pegasus Program

Retrieved on: 
Wednesday, December 6, 2023

In a significant advancement for privacy-first AI applications, Gretel, a leader in multimodal synthetic data generation, today announces its software is now available on the Microsoft Azure Marketplace .

Key Points: 
  • In a significant advancement for privacy-first AI applications, Gretel, a leader in multimodal synthetic data generation, today announces its software is now available on the Microsoft Azure Marketplace .
  • Additionally, Gretel is joining Microsoft for Startups Pegasus Program to facilitate the adoption of responsible AI practices across industries.
  • That’s where Gretel provides enormous value.”
    “Through Microsoft Azure Marketplace, customers around the world can easily find, buy, and deploy partner solutions they can trust, all certified and optimized to run on Azure,” said Jake Zborowski, General Manager, Microsoft Azure Platform at Microsoft Corp. “We’re happy to welcome Gretel’s solutions to the growing Azure Marketplace ecosystem.”
    Azure customers can use their existing Azure credits and commitments to build with Gretel.
  • To learn more about these products, services, and solutions available check out Gretel on the Azure Marketplace .

INU Scientists Propose a Model to Predict Personal Learning Performance for Virtual Reality-Based Safety Training

Retrieved on: 
Monday, December 4, 2023

Firstly, VR-based construction safety training is essentially a passive exercise, with learners following one-way instructions that fail to adapt to their judgments and decisions.

Key Points: 
  • Firstly, VR-based construction safety training is essentially a passive exercise, with learners following one-way instructions that fail to adapt to their judgments and decisions.
  • Furthermore, among the individual characteristics that can affect learning performance, including personal, academic, social, and cognitive aspects, cognitive characteristics may undergo changes during VR-based safety training.
  • Explaining these results, Dr. Koo emphasizes, "This approach can have a significant impact on improving personal learning performance during VR-based construction safety training, preventing safety incidents, and fostering a safe working environment."
  • In conclusion, this study marks a significant stride in enhancing personalized safety in construction environments and improving the evaluation of learning performance!

GIST Scientists Advance Voice Pathology Detection via Adversarial Continual Learning

Retrieved on: 
Tuesday, October 17, 2023

In this context, voice pathology detection (VPD) has received much attention as a non-invasive way to automatically detect voice problems.

Key Points: 
  • In this context, voice pathology detection (VPD) has received much attention as a non-invasive way to automatically detect voice problems.
  • It consists of two processing modules: a feature extraction module to characterize normal voices and a voice detection module to detect abnormal ones.
  • Machine learning methods like support vector machines (SVM) and convolutional neural networks (CNN) have been successfully utilized as pathological voice detection modules to achieve good VPD performance.
  • Herein, they incorporated adversarial regularization during the continual learning process.

WiMi Developed a Deep Learning-Based Approach to Personalized Video Recommendations

Retrieved on: 
Friday, October 13, 2023

BEIJING, Oct. 13, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed a personalized video recommendation system based on deep learning according to the development needs of the industry, providing new ideas and directions for the research of personalized video recommendation under deep learning.

Key Points: 
  • BEIJING, Oct. 13, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it developed a personalized video recommendation system based on deep learning according to the development needs of the industry, providing new ideas and directions for the research of personalized video recommendation under deep learning.
  • In personalized video recommendation, different types of neural network models are used to model the association between the user and the video.
  • Feature Representation Learning: In a personalized video recommendation system, effective feature representations are critical to the performance of the model.
  • WiMi's deep learning-based personalized video recommendation technology solves information overload, personalizes user needs, improves user experience, and promotes market development in the online video industry.

WiMi Proposed Data Enhancement for Convolutional Neural Networks

Retrieved on: 
Thursday, October 12, 2023

BEIJING, Oct. 12, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it proposed data enhancement for convolutional neural networks (CNN).

Key Points: 
  • BEIJING, Oct. 12, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it proposed data enhancement for convolutional neural networks (CNN).
  • In the future, WiMi will study the combination of data enhancement with the feedback mechanism of the model through learning algorithms to realize adaptive data enhancement, so that the network can automatically choose the appropriate data enhancement method according to the characteristics of the input data and the task requirements, thus improving the performance and robustness of the model.
  • In addition, the development of generative models (e.g., generative adversarial networks) also provides new ideas for data enhancement, and its application in data enhancement has a broad prospect.
  • In the future, WiMi will also study the combination of generative models with data enhancement, generating new data samples through generative models and using them for data enhancement, effectively solving the problem of data scarcity, and further improving the generalization ability of models.

WiMi Developed Optimized Video Personalized Recommendation System Based on Multi-modal Deep Learning Method

Retrieved on: 
Friday, September 15, 2023

BEIJING, Sept. 15, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that an innovative personalized multi-modal video recommendation system is developed.

Key Points: 
  • BEIJING, Sept. 15, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that an innovative personalized multi-modal video recommendation system is developed.
  • The system utilizes deep learning algorithms to mine hidden features of movies and users, and is trained with multi-modal data to further predict video ratings to provide more accurate personalized recommendation results.
  • The video recommendation system includes, data collection and pre-processing, feature extraction and representation learning, model training and optimization, and recommendation algorithm and personalized recommendation.
  • WiMi's personalized video recommendation system has better recommendation accuracy and user satisfaction compared to traditional recommendation algorithm such as collaborative filtering, content-based filtering and singular value decomposition.

WiMi Developed a BPR-based CNN Image Classification Technology to Better Solve the Image Classification Problems

Retrieved on: 
Wednesday, August 23, 2023

In traditional CNN image recognition classification models, the softmax function is used for classification.

Key Points: 
  • In traditional CNN image recognition classification models, the softmax function is used for classification.
  • WiMi BPR-based CNN image classification uses a mapping function to display CNN features in a high-dimensional feature space.
  • By combining BPR and CNN, this technique can overcome some of the shortcomings of traditional pattern recognition, improve the performance of image classification, and can handle complex image classification problems.
  • And it can deal with complex image classification problems, such as image recognition, target detection and image segmentation.

WiMi Developed a Novel Image Classification System Based on a Model Network of Continuous Multi-scale Feature Learning System

Retrieved on: 
Monday, August 21, 2023

A continuous multi-scale feature learning system model network of WiMi employs a continuous feature learning approach based on using various feature maps with different receptive fields to achieve faster training/inference and higher accuracy.

Key Points: 
  • A continuous multi-scale feature learning system model network of WiMi employs a continuous feature learning approach based on using various feature maps with different receptive fields to achieve faster training/inference and higher accuracy.
  • In the data learning phase, useful features of the images are extracted using a model based on continuous multi-scale feature learning.
  • In the data learning phase, this network system of WiMi, uses a continuous multi-scale feature learning method to extract useful features from images.
  • WiMi continuous multi-scale feature learning network for image recognition classification method has important market value and significance.

WiMi Developed a 3D Human Behavior Recognition Algorithm System Based on Convolutional Neural Network

Retrieved on: 
Wednesday, August 16, 2023

BEIJING, Aug. 16, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that a 3D human behavior recognition algorithm system based on convolutional neural network (CNN), which has the good representational ability, was developed.

Key Points: 
  • BEIJING, Aug. 16, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that a 3D human behavior recognition algorithm system based on convolutional neural network (CNN), which has the good representational ability, was developed.
  • Human Behavior Recognition (HBR) is the process of deciphering human behaviors through sophisticated techniques in order to enable machines to understand, analyze, comprehend, and classify these behaviors and give any kind of valid input or stimulus.
  • More specifically, the system is using data for a 3D human behavior recognition task by extracting four types of informative features (distance, distance velocity, angle, and angle velocity features) from the 3D skeleton data and encoding them into an image using a suitable encoding scheme.
  • The requirements of human behavior recognition networks for the dataset include comprehensive behavioral categories, high-quality of behavior, clear video and so on.