Image similarity solution with Microsoft Machine Learning Server
Обновлено 2023-06-12 22:32:06 +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
Self-Supervised Document-to-Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Обновлено 2022-11-28 22:08:28 +03:00
An automated and scalable approach to generate tasklets from a natural language task query and a website URL. Glider does not require any pre-training. Glider models tasklet extraction as a state space search, where agents can explore a website’s UI and get rewarded when making progress towards task completion. The reward is computed based on the agent’s navigating pattern and the similarity between its trajectory and the task query.
Обновлено 2021-09-03 06:52:47 +03:00
Deep Neural Network based Image Similarity ranking on Azure using CNTK
Обновлено 2020-03-10 20:38:16 +03:00