Hierarchical Transformers for Knowledge Graph Embeddings (EMNLP 2021)
Обновлено 2024-07-25 14:00:19 +03:00
A python library for intelligently building networks and network embeddings, and for analyzing connected data.
Обновлено 2024-07-11 21:49:18 +03:00
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding (AAAI 2020) - PyTorch Implementation
Обновлено 2023-07-25 19:48:26 +03:00
edX XBlock for services supporting the OEmbed protocol
Обновлено 2023-06-14 18:18:54 +03:00
A novel embedding training algorithm leveraging ANN search and achieved SOTA retrieval on Trec DL 2019 and OpenQA benchmarks
Обновлено 2023-06-13 00:27:31 +03:00
Recipe prediction model from emojis
Обновлено 2023-06-12 22:30:52 +03:00
The overall purpose of this document is to showcase an example of Azure Machine Learning on IoT Edge Devices using Microsoft Embedded Learning Library (ELL)
Обновлено 2023-06-12 22:02:06 +03:00
Truly Conversational Search is the next logic step in the journey to generate intelligent and useful AI. To understand what this may mean, researchers have voiced a continuous desire to study how people currently converse with search engines. Traditionally, the desire to produce such a comprehensive dataset has been limited because those who have this data (Search Engines) have a responsibility to their users to maintain their privacy and cannot share the data publicly in a way that upholds the trusts users have in the Search Engines. Given these two powerful forces we believe we have a dataset and paradigm that meets both sets of needs: A artificial public dataset that approximates the true data and an ability to evaluate model performance on the real user behavior. What this means is we released a public dataset which is generated by creating artificial sessions using embedding similarity and will test on the original data. To say this again: we are not releasing any private user data but are releasing what we believe to be a good representation of true user interactions.
Обновлено 2023-06-12 21:21:58 +03:00
Обновлено 2019-04-01 15:31:37 +03:00