Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph

Representing knowledge as high-dimensional vectors in a continuous semantic vector space can help overcome the brittleness and incompleteness of traditional knowledge bases. We present a method for performing deductive reasoning directly in such a vector space, combining analogy, association, and deduction in a straightforward way at each step in a chain of reasoning, drawing on knowledge from diverse sources and ontologies.Comment: AGI 201

Productive Vision: Methods for Automatic Image Comprehension

Image comprehension is the ability to summarize, translate, and answer basic questions about images. Using original techniques for scene object parsing, material labeling, and activity recognition, a system can gather information about the objects and actions in a scene. When this information is integrated into a deep knowledge base capable of inference, the system becomes capable of performing tasks that, when performed by students, are considered by educators to demonstrate comprehension. The vision components of the system consist of the following: object scene parsing by means of visual filters, material scene parsing by superpixel segmentation and kernel descriptors, and activity recognition by action grammars. These techniques are characterized and compared with the state-of-the-art in their respective fields. The output of the vision components is a list of assertions in a Cyc microtheory. By reasoning on these assertions and the rest of the Cyc knowledge base, the system is able to perform a variety of tasks, including the following: Recognize essential parts of objects are likely present in the scene despite not having an explicit detector for them. Recognize the likely presence of objects due to the presence of their essential parts. Improve estimates of both object and material labels by incorporating knowledge about the typical pairings. Label ambiguous objects with a more general label that encompasses both possible labelings. Answer questions about the scene that require inference and give justifications for the answers in natural language. Create a visual representation of the scene in a new medium. Recognize scene similarity even when there is little visual similarity

Recognising Complex Activities with Histograms of Relative Tracklets

One approach to the recognition of complex human activities is to use feature descriptors that encode visual inter-actions by describing properties of local visual features with respect to trajectories of tracked objects. We explore an example of such an approach in which dense tracklets are described relative to multiple reference trajectories, providing a rich representation of complex interactions between objects of which only a subset can be tracked. Specifically, we report experiments in which reference trajectories are provided by tracking inertial sensors in a food preparation sce-nario. Additionally, we provide baseline results for HOG, HOF and MBH, and combine these features with others for multi-modal recognition. The proposed histograms of relative tracklets (RETLETS) showed better activity recognition performance than dense tracklets, HOG, HOF, MBH, or their combination. Our comparative evaluation of features from accelerometers and video highlighted a performance gap between visual and accelerometer-based motion features and showed a substantial performance gain when combining features from these sensor modalities. A considerable further performance gain was observed in combination with RETLETS and reference tracklet features

Mapping Distributional Semantics to Property Norms with Deep Neural Networks

Word embeddings have been very successful in many natural language processing tasks, but they characterize the meaning of a word/concept by uninterpretable “context signatures”. Such a representation can render results obtained using embeddings difficult to interpret. Neighboring word vectors may have similar meanings, but in what way are they similar? That similarity may represent a synonymy, metonymy, or even antonymy relation. In the cognitive psychology literature, in contrast, concepts are frequently represented by their relations with properties. These properties are produced by test subjects when asked to describe important features of concepts. As such, they form a natural, intuitive feature space. In this work, we present a neural-network-based method for mapping a distributional semantic space onto a human-built property space automatically. We evaluate our method on word embeddings learned with different types of contexts, and report state-of-the-art performances on the widely used McRae semantic feature production norms

Gluing Neural Networks Symbolically Through Hyperdimensional Computing

Hyperdimensional Computing affords simple, yet powerful operations to create long Hyperdimensional Vectors (hypervectors) that can efficiently encode information, be used for learning, and are dynamic enough to be modified on the fly. In this paper, we explore the notion of using binary hypervectors to directly encode the final, classifying output signals of neural networks in order to fuse differing networks together at the symbolic level. This allows multiple neural networks to work together to solve a problem, with little additional overhead. Output signals just before classification are encoded as hypervectors and bundled together through consensus summation to train a classification hypervector. This process can be performed iteratively and even on single neural networks by instead making a consensus of multiple classification hypervectors. We find that this outperforms the state of the art, or is on a par with it, while using very little overhead, as hypervector operations are extremely fast and efficient in comparison to the neural networks. This consensus process can learn online and even grow or lose models in real time. Hypervectors act as memories that can be stored, and even further bundled together over time, affording life long learning capabilities. Additionally, this consensus structure inherits the benefits of Hyperdimensional Computing, without sacrificing the performance of modern Machine Learning. This technique can be extrapolated to virtually any neural model, and requires little modification to employ - one simply requires recording the output signals of networks when presented with a testing example.Comment: 10 pages, 3 figures, 6 tables, accepted to IJCNN 2022 / IEEE WCCI 202