MicroCloud Hologram Inc. announces optimization of stacked sparse autoencoders through DeepSeek model

Core Viewpoint - MicroCloud Hologram Inc. has announced the optimization of stacked sparse autoencoders through the DeepSeek open-source model, enhancing anomaly detection technology and providing an efficient solution [1]. Data Preprocessing - Data quality is essential for model performance, and HOLO employs normalization to eliminate dimensional influences and improve model training effectiveness [2][3]. - Normalization scales data to a specific range, allowing for better comparison and analysis across different features, which enhances model training efficiency and aligns data with deep learning input requirements [3]. Model Architecture - The stacked sparse autoencoder model consists of multiple layers that extract features at different levels, utilizing the DeepSeek model to adjust sparsity constraints dynamically [4]. - The training approach is greedy and layer-wise, allowing the model to progressively learn complex relationships within the data by training lower layers first [5]. Training Techniques - HOLO incorporates denoising training by adding noise to input data, which helps the model learn robust feature representations for accurate anomaly detection in noisy real-world scenarios [6]. - Dropout is applied during training to reduce overfitting by randomly dropping a subset of neurons, ensuring the model learns general and robust feature representations [7]. Computational Efficiency - The DeepSeek model utilizes a distributed computing framework for parallel execution of training tasks, significantly reducing training time and improving efficiency [8]. - A pretraining and fine-tuning strategy is employed to accelerate model convergence and enhance performance [8]. Company Overview - MicroCloud Hologram Inc. specializes in providing advanced holographic technology services, including high-precision LiDAR solutions and holographic digital twin technology [10].