Modulo allows optimal selection of vehicles for effective drive-by sensing
Обновлено 2024-04-08 18:44:31 +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
ICLR 2022 Paper, SOTA Table Pre-training Model, TAPEX: Table Pre-training via Learning a Neural SQL Executor
Обновлено 2023-02-06 11:06:18 +03:00
Code for the ICLR 2019 paper "Learning to Represent Edits"
Обновлено 2022-12-08 05:44:37 +03:00
Source code for paper Conservative Uncertainty Estimation By Fitting Prior Networks (ICLR 2020)
Обновлено 2022-11-28 22:09:08 +03:00
ICLR 2021: Pre-Training for Context Representation in Conversational Semantic Parsing
Обновлено 2021-08-30 22:08:54 +03:00
Demonstration of Jackknife Variational Inference for Variational Autoencoders, related to ICLR 2018 paper.
Обновлено 2018-02-21 13:36:23 +03:00