High-Fidelity Vector Space Models of Structured Data
Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most machine learning techniques would expect, i.e. as a real-valued vector of a fixed, pre-specified size. In this work, we introduce a novel approach that compiles structured data into a satisfiability problem which has in its set of solutions at least (and often only) the input data. The satisfiability problem is constructed from constraints which are generated automatically a priori from a given signature, thus trivially allowing for a bag-of-words-esque vector representation of the input to be constructed. The method is demonstrated in two areas, automated reasoning and natural language processing, where it is shown to produce vector representations of natural-language sentences and first-order logic clauses that can be precisely translated back to their original, structured input forms.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
Individualized non-uniform quantization for vector search
Embedding vectors are widely used for representing unstructured data and searching through it for semantically similar items. However, the large size of these vectors, due to their high-dimensionality, creates problems f…
PCA-VAE: Differentiable Subspace Quantization without Codebook Collapse
Vector-quantized autoencoders deliver high-fidelity latents but suffer inherent flaws: the quantizer is non-differentiable, requires straight-through hacks, and is prone to collapse. We address these issues at the root b…
High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization
Semantic shape completion is a challenging problem in 3D computer vision where the task is to generate a complete 3D shape using a partial 3D shape as input. We propose a learning-based approach to complete incomplete 3D…
DecoderRetrievalVocal Bursts Intensity PredictionLATTICE: Democratize High-Fidelity 3D Generation at Scale
We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis benefits from fixed spatial grids and w…
3D GenerationUniVector: Unified Vector Extraction via Instance-Geometry Interaction
Vector extraction retrieves structured vector geometry from raster images, offering high-fidelity representation and broad applicability. Existing methods, however, are usually tailored to a single vector type (e.g., pol…