Integrate cutting-edge LLM technology quickly and easily into your apps - microsoft/semantic-kernel

We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions...

Visual illusions traditionally rely on spatial manipulations such as multi-view consistency. In this work, we introduce Progressive Semantic Illusions, a novel vector sketching task where a single ske...

# I Hereby Abolish Rules # Semantic Liquidation, Territorial Gatekeeping, and the Failure of Marxism as Identity **Lee Sharks** **Journal:** *Transactions of the Semantic Economy Institute* **Instit...

# A Typed Functional Dictionary for Rongorongo A Non-Semantic, Architecture-Based Approach # Abstract This document presents a functional, non-semantic dictionary for the Rongorongo script of...

The motivation of semantic wikis is to make acquisition, maintenance, and mining of formal knowledge simpler, faster, and more flexible. However, most existing semantic wikis have a very technical int...

This project is the result of my undergraduate dissertation. The localization in dynamic environment is to deploy TensorRT optimized YOLOX in the front end of ORB-SLAM3 for object detection, and then ...

Learn about Semantic Kernel

Discover openinference-semantic-conventions in the com.arize namespace. Explore metadata, contributors, the Maven POM file, and more.

This repository provides a semantic search system over the lewtun/github-issues dataset using sentence-transformers/multi-qa-mpnet-base-dot-v1. It allows developers to find the most relevant issue tit...

It is developed at the Allen Institute for AI and was publicly released in November 2015. Semantic Scholar uses modern techniques in natural language

Semantic SEO is the way you align your content with how modern search engines understand meaning, entities, and search intent, not just keywords. Instead of asking “how many times should I repeat th...

Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With...

Multimodal large language models (MLLMs) have shown satisfactory effects in many autonomous driving tasks. In this paper, MLLMs are utilized to solve joint semantic scene understanding and risk locali...

We present a novel framework that integrates Large Language Models (LLMs) into the Git bisect process for semantic fault localization. Traditional bisect assumes deterministic predicates and binary fa...

Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery. Existing data preparation and analysis sys...

Rather than using text for scientific research reports, we have proposed developing highly-structured reports with rich semantic models. In this paper, we consider detailed structures for the componen...

LiDAR loop closure detection (LCD) is crucial for consistent Simultaneous Localization and Mapping (SLAM) but faces challenges in robustness and accuracy. Existing methods, including semantic graph ap...

Advances in machine learning technology have enabled real-time extraction of semantic information in signals which can revolutionize signal processing techniques and improve their performance signific...