Structure Regular Languages – We propose a two-level structure-invariant-regular language model, the Regular Language Model (RNML). This model is trained with an external grammar. NMLMLs are similar to regular language models, but can be trained end-to-end. The main innovation of NMLML is to be a recursive encoder of language. The encoder is a recursive encoder of language, and learns a recursive structure to learn. We study the performance of RNMLs on two benchmark domains: Arabic and Vietnamese scripts, and show that their performance is comparable to that of a regular language model, in order to be shown a good application of NMLML.
In this paper, a new type of sparse representation for visual semantic object classification based on similarity is proposed. The proposed representation is based on the use of two-dimensional representation of visual information by a low level memory unit (memory architecture), and uses such representation (memory architecture) to build a set of semantic structures. We apply the proposed approach to semantic segmentation and retrieval. The proposed representation is achieved by combining the two-dimensional representation representation with the low level memory representation and using such representation to construct a model. Our experiments show the proposed approach is superior to the state-of-the-art semantic segmentation and retrieval methods.
On the Runtime and Fusion of Two Generative Adversarial Networks
Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning
Structure Regular Languages
On the Reliable Detection of Non-Linear Noise in Continuous Background Subtasks
Spacetimes in the Brain: A Brain-Inspired Approach to Image Retrieval and Text AnalysisIn this paper, a new type of sparse representation for visual semantic object classification based on similarity is proposed. The proposed representation is based on the use of two-dimensional representation of visual information by a low level memory unit (memory architecture), and uses such representation (memory architecture) to build a set of semantic structures. We apply the proposed approach to semantic segmentation and retrieval. The proposed representation is achieved by combining the two-dimensional representation representation with the low level memory representation and using such representation to construct a model. Our experiments show the proposed approach is superior to the state-of-the-art semantic segmentation and retrieval methods.
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