Deterministic High-Dimensional Data Embedding via Deep Learning
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Solution Overview
Problem
Existing high-dimensional data embedding algorithms face challenges such as computational inefficiency, lack of determinism, and sensitivity to initial conditions, making them difficult to reuse and scale for applications like image recognition and similarity computation.
Innovation Solution
The use of deep learning techniques to create a deterministic embedding function that can efficiently process high-dimensional data objects on GPU and FPGA platforms, allowing for parallel computation and decoupling of embedding processes, enabling faster and more repeatable recognition and translation of high-dimensional objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional embedding algorithms are used to embed high-dimensional data objects, then the embedding process can capture relationships between objects, but the computation is slow, stochastic, and non-parallelizable
Solution Approach 1:
The patent segments the embedding computation into two distinct phases: (1) a training phase where the deep learning model learns the embedding function from data, and (2) an inference phase where the trained model deterministically embeds new objects. This segmentation allows the computationally intensive learning process to be performed once, while subsequent embeddings are fast and deterministic.
Solution Approach 2:
The patent replaces traditional mechanical/embedding algorithms (like force-directed layouts that require iterative optimization) with a deep learning neural network that learns the embedding function through gradient descent. The neural network's parallelizable architecture and deterministic forward propagation enable fast, repeatable embeddings without stochastic iterative processes.
2Productivity
If force diagram methods are used to embed high-dimensional objects into equilibrium, then relationships between objects are captured, but every embedding depends on the joint embedding of all other objects making computation serial and iterative
Solution Approach 1:
The patent extracts the computational interdependence from the embedding process by training the neural network to learn the embedding function independently for each object. Once trained, the model can embed any object in isolation without requiring other objects to be embedded simultaneously, enabling full parallelization.
Solution Approach 2:
The patent performs the complex learning of object relationships in advance during the training phase, where the neural network learns the embedding function from labeled data. This preliminary action captures all inter-object relationships, allowing subsequent embeddings to be computed independently and in parallel without re-computing relationships.
3Reliability
If stochastic initialization is used in embedding algorithms, then random exploration of solution space occurs, but results are sensitive to initial conditions and lack repeatability
Solution Approach 1:
The patent performs stochastic exploration and initialization during the preliminary training phase only. The neural network is trained with stochastic gradient descent and random initializations to learn the optimal embedding function. Once training is complete, the learned model provides deterministic and repeatable embeddings without requiring further stochastic processes.
Solution Approach 2:
The patent creates a fixed copy of the learned embedding function in the trained neural network model. This copied function can be applied repeatedly to embed new objects deterministically, without re-running the stochastic training process or being sensitive to new initial conditions.
Data Source
AI summary
Embodiments of the present invention are directed to providing new systems and methods for using deep learning techniques to generate embeddings for high dimensional data objects that can both simulate prior art embedding algorithms and also provide superior performance compared to the prior art methods. Deep learning techniques used by embodiments of the present invention to embed high dimensional data objects may comprise the following steps: (1) generating an initial formal embedding of selected high-dimensional data objects using any of the traditional formal embedding techniques; (2a) designing a deep embedding architecture, which includes choosing the types and numbers of inputs and outputs, types and number of layers, types of units/nonlinearities, and types of pooling, for example, among other design choices, typically in a convolutional neural network; (2b) designing a training strategy; (2c) tuning the parameters of a deep embedding architecture to reproduce, as reliably as possible, the generated embedding for each training sample; (3) optionally deploying the trained deep embedding architecture to convert new high dimensional data objects into approximately the same embedded space as found in step (1); and optionally (4) feeding the computed embeddings of high dimensional objects to an application in a deployed embodiment.