MicroCloud Hologram Inc. announces optimization of stacked sparse autoencoders through DeepSeek model
MicroCloud Hologram (NASDAQ: HOLO) has announced the optimization of stacked sparse autoencoders through the DeepSeek open-source model, enhancing their anomaly detection capabilities. The company implements data normalization techniques to improve model training efficiency by scaling data to specific ranges.
HOLO's implementation utilizes a layered training strategy where the stacked sparse autoencoder learns features progressively, with each layer extracting deeper data patterns. The system employs denoising training and Dropout regularization to enhance model robustness and prevent overfitting.
The integration of the DeepSeek model enables distributed computing for parallel task execution, significantly reducing training time. The model uses pretraining and fine-tuning strategies to accelerate convergence and improve overall performance in anomaly detection applications.
MicroCloud Hologram (NASDAQ: HOLO) ha annunciato l'ottimizzazione degli autoencoder sparsi impilati attraverso il modello open-source DeepSeek, migliorando le loro capacità di rilevamento delle anomalie. L'azienda implementa tecniche di normalizzazione dei dati per migliorare l'efficienza dell'addestramento del modello scalando i dati a intervalli specifici.
L'implementazione di HOLO utilizza una strategia di addestramento stratificato in cui l'autoencoder sparso impilato apprende le caratteristiche in modo progressivo, con ogni strato che estrae modelli di dati più profondi. Il sistema impiega un addestramento di denoising e la regolarizzazione Dropout per migliorare la robustezza del modello e prevenire l'overfitting.
L'integrazione del modello DeepSeek consente il calcolo distribuito per l'esecuzione parallela dei compiti, riducendo significativamente il tempo di addestramento. Il modello utilizza strategie di pre-addestramento e fine-tuning per accelerare la convergenza e migliorare le prestazioni complessive nelle applicazioni di rilevamento delle anomalie.
MicroCloud Hologram (NASDAQ: HOLO) ha anunciado la optimización de autoencoders dispersos apilados a través del modelo de código abierto DeepSeek, mejorando sus capacidades de detección de anomalías. La empresa implementa técnicas de normalización de datos para mejorar la eficiencia del entrenamiento del modelo escalando los datos a rangos específicos.
La implementación de HOLO utiliza una estrategia de entrenamiento en capas donde el autoencoder disperso apilado aprende características de manera progresiva, con cada capa extrayendo patrones de datos más profundos. El sistema emplea entrenamiento de denoising y regularización Dropout para mejorar la robustez del modelo y prevenir el sobreajuste.
La integración del modelo DeepSeek permite la computación distribuida para la ejecución paralela de tareas, reduciendo significativamente el tiempo de entrenamiento. El modelo utiliza estrategias de preentrenamiento y ajuste fino para acelerar la convergencia y mejorar el rendimiento general en aplicaciones de detección de anomalías.
마이크로클라우드 홀로그램 (NASDAQ: HOLO)은 DeepSeek 오픈 소스 모델을 통해 스택된 희소 오토인코더 최적화를 발표하며, 이로 인해 이상 탐지 기능이 향상되었습니다. 이 회사는 데이터 정규화 기술을 구현하여 특정 범위로 데이터를 축소함으로써 모델 훈련 효율성을 개선합니다.
HOLO의 구현은 계층 훈련 전략을 활용하여 스택된 희소 오토인코더가 점진적으로 특징을 학습하며, 각 계층이 더 깊은 데이터 패턴을 추출합니다. 이 시스템은 모델의 강인성을 높이고 과적합을 방지하기 위해 노이즈 제거 훈련과 드롭아웃 정규화를 사용합니다.
DeepSeek 모델의 통합은 병렬 작업 실행을 위한 분산 컴퓨팅을 가능하게 하여 훈련 시간을 크게 단축합니다. 이 모델은 사전 훈련 및 미세 조정 전략을 사용하여 수렴을 가속화하고 이상 탐지 응용 프로그램에서의 전반적인 성능을 향상시킵니다.
MicroCloud Hologram (NASDAQ: HOLO) a annoncé l'optimisation des autoencodeurs épars empilés grâce au modèle open-source DeepSeek, améliorant ainsi leurs capacités de détection des anomalies. L'entreprise met en œuvre des techniques de normalisation des données pour améliorer l'efficacité de l'entraînement du modèle en échelonnant les données à des plages spécifiques.
L'implémentation de HOLO utilise une stratégie d'entraînement en couches où l'autoencodeur épars empilé apprend progressivement des caractéristiques, chaque couche extrayant des motifs de données plus profonds. Le système utilise un entraînement de débruitage et la régularisation Dropout pour améliorer la robustesse du modèle et prévenir le surapprentissage.
L'intégration du modèle DeepSeek permet le calcul distribué pour l'exécution parallèle des tâches, réduisant ainsi considérablement le temps d'entraînement. Le modèle utilise des stratégies de pré-entraînement et de réglage fin pour accélérer la convergence et améliorer les performances globales dans les applications de détection des anomalies.
MicroCloud Hologram (NASDAQ: HOLO) hat die Optimierung gestapelter spärlicher Autoencoder durch das Open-Source-Modell DeepSeek angekündigt, wodurch die Anomalieerkennungsfähigkeiten verbessert werden. Das Unternehmen implementiert Daten-Normalisierung-Techniken, um die Effizienz des Modelltrainings zu steigern, indem die Daten auf spezifische Bereiche skaliert werden.
Die Implementierung von HOLO nutzt eine gestufte Trainingsstrategie, bei der der gestapelte spärliche Autoencoder schrittweise Merkmale erlernt, wobei jede Schicht tiefere Datenmuster extrahiert. Das System verwendet Denoising-Training und Dropout-Regularisierung, um die Robustheit des Modells zu erhöhen und Überanpassung zu verhindern.
Die Integration des DeepSeek-Modells ermöglicht verteiltes Rechnen für die parallele Ausführung von Aufgaben, wodurch die Trainingszeit erheblich verkürzt wird. Das Modell verwendet Vortraining und Feinabstimmungsstrategien, um die Konvergenz zu beschleunigen und die Gesamtleistung in Anomalieerkennungsanwendungen zu verbessern.
- Implementation of distributed computing framework for improved training efficiency
- Enhanced model robustness through denoising training and Dropout regularization
- Optimization of data processing through advanced normalization techniques
- None.
Insights
The implementation of DeepSeek model optimization represents a significant technical advancement for MicroCloud Hologram's AI capabilities, though its immediate business impact remains uncertain. The key technical improvements focus on three critical areas:
- Enhanced Data Processing: The normalization and preprocessing improvements should reduce computational overhead and improve model accuracy, potentially leading to faster service delivery and reduced operational costs.
- Improved Feature Learning: The optimized stacked sparse autoencoder architecture, combined with DeepSeek's distributed computing framework, could significantly enhance the company's anomaly detection capabilities. This may open new revenue streams in quality control and security applications.
- Robust Model Performance: The implementation of denoising and dropout techniques suggests a focus on real-world application reliability, which is important for commercial deployment.
However, the choice of an open-source model like DeepSeek presents both opportunities and challenges. While it reduces development costs and allows for community-driven improvements, it also means competitors can access similar capabilities. The true value proposition will depend on HOLO's ability to build proprietary applications and use cases on top of this foundation.
From a market perspective, this technical optimization appears more evolutionary than revolutionary. Without specific performance metrics or clear business applications, it's difficult to quantify the competitive advantage these improvements might provide in the rapidly evolving AI technology sector.
Data quality is crucial for model performance, so the behavioral data collected in the data preprocessing stage typically contains multiple features with different dimensions and numerical ranges. In order to eliminate the dimensional influence between different features and improve the effectiveness of model training, HOLO uses normalization processing method.
Normalization is a common data preprocessing technique that scales the data to a specific range, typically between 0 and 1 or -1 and 1. By doing so, data from different features can be compared and analyzed on the same scale, avoiding the situation where certain features dominate model training due to their large value ranges. In HOLO's detection project, normalization not only improved the efficiency of model training but also laid a solid foundation for subsequent feature extraction. The data processed through normalization is more aligned with the input requirements of deep learning models, enable the model to learn intrinsic patterns more accurately.
After the data preprocessing is completed, the next step is to input the processed data into the stacked sparse autoencoder model. The stacked sparse autoencoder is a powerful deep learning architecture composed of multiple autoencoder layers, with each layer responsible for extracting features at different levels. HOLO utilizes the DeepSeek model to dynamically adjust the strength and manner of the sparsity constraint, ensuring that the features learned by each layer of the autoencoder are sparse and representative. By appropriately setting the sparsity constraint, the model can better capture key information in the data and reduce redundant features. An autoencoder is an unsupervised learning model designed to encode input data into a lower-dimensional feature representation through the encoder, and then reconstruct the original input data as accurately as possible through the decoder. Between the encoder and decoder, the autoencoder learns the feature representation of the data through a hidden layer.
HOLO has innovated and optimized the stacked sparse autoencoder by utilizing the DeepSeek model. This technique employs a greedy, layer-wise training approach, optimizing the parameters of each autoencoder layer step by step. The core of this layered training strategy is to first train the lower layers of the autoencoder to learn the basic features of the input data, then use the output of the lower-layer autoencoder as the input for the next layer, continuing training and progressively extracting deeper features. In this way, the model is able to gradually capture the complex relationships within the data, enhancing its expressive power. Each layer of the autoencoder is constrained by sparsity, ensuring that the learned features are sparse, meaning that only a few neurons are activated, allowing the model to learn more compact and effective feature representations.
HOLO's stacked sparse autoencoder, trained with the DeepSeek model, adds noise to the input data and requires the model to reconstruct the original input despite the noise interference. This denoising training approach encourages the model to learn more robust feature representations, enabling it to perform accurate anomaly detection even when faced with noisy data in real-world scenarios, thus improving the model's robustness. Specifically, during training, random noise is added to the input data, and the model is tasked with reconstructing the original input. This process forces the model to learn more resilient feature representations, ensuring high accuracy even in the presence of various types of noise in real-world conditions.
In addition to denoising, HOLO also applies Dropout during the training process. Dropout is a commonly used regularization technique primarily aimed at reducing model overfitting. In deep learning models, overfitting refers to the phenomenon where a model performs well on training data but poorly on unseen samples. To avoid this, HOLO randomly drops a subset of neurons during the training of the stacked sparse autoencoder. In each training iteration, the model randomly selects a portion of neurons and sets their outputs to zero. The benefit of this approach is that the model cannot rely on any specific neuron to learn the features of the data, but must instead learn more general and robust feature representations.
In addition, the DeepSeek model utilizes a distributed computing framework, which allocates training tasks across multiple computational nodes for parallel execution. This significantly shortens training time and improves training efficiency. Using the DeepSeek model, pretraining can first be conducted on the stacked sparse autoencoder to learn general feature representations. This pretraining + fine-tuning strategy can greatly accelerate model convergence and improve performance. By introducing the DeepSeek model, HOLO has injected new vitality into optimizing stacked sparse autoencoders. The DeepSeek model provides comprehensive support in areas such as architecture design, training, strategic feature learning, and generalization ability.
About MicroCloud Hologram Inc.
MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ("LiDAR") solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ("ADAS"). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/
Safe Harbor Statement
This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as "may," "will," "intend," "should," "believe," "expect," "anticipate," "project," "estimate," or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company's expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company's goals and strategies; the Company's future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in
View original content:https://www.prnewswire.com/news-releases/microcloud-hologram-inc-announces-optimization-of-stacked-sparse-autoencoders-through-deepseek-model-302377133.html
SOURCE MicroCloud Hologram Inc.
FAQ
What is the purpose of HOLO's DeepSeek model implementation?
How does HOLO's new normalization process improve model performance?
What technical improvements does HOLO's stacked sparse autoencoder offer?
How does HOLO's DeepSeek model handle data preprocessing?
