2017. A joint many-task model: Growing a neural network for multiple nlp tasks. In EMNLP.Hashimoto, K.; Tsuruoka, Y.; Socher, R.; et al. 2017. A joint many-task model: Growing a neural network for multi- ple nlp tasks. In Proceedings of the 2017 Conference on EMNLP, 1923-1933....
In recent years, joint triple extraction methods have received extensive attention because they have significantly promoted the progress of information extraction and many related downstream tasks in the field of natural language processing. However, due to the inherent complexity of language such as rela...
Key: a joint embedding model between state-action pairs and concept-based explanations ExpEnv: connect4, lunar lander Efficient Exploration in Continuous-time Model-based Reinforcement Learning Lenart Treven, Jonas Hübotter, Bhavya, Florian Dorfler, Andreas Krause Key: nonlinear ordinary differential ...
Create or update your marketing pages/website (such as integration page, partner page, pricing page, and so on) to include the availability of the joint integration. Example: Pingboard integration Page, Smartsheet integration page, Monday.com pricing page Create a help center article or ...
(Zhao et al., 2021) proposed a multi-ethnic population development analysis method that used multiple data sources to construct an accurate SDI system and a joint multi-factor weighting model to assign specific SDI values to indicators; however, the indicators in this SDI did not interfere with...
Awesome_Multimodel is a curated GitHub repository that provides a comprehensive collection of resources for Multimodal Large Language Models (MLLM). It covers datasets, tuning techniques, in-context learning, visual reasoning, foundational models, and mo
Figure 4: Diagram of Z-code architecture. Z-code uses transfer learning in two ways. First, the model is trained multilingually across many languages, such that knowledge is transferred between languages. Second, we use multi-task training so that we transfer knowledge between tasks. For example...
However, given a large-scale cross-modal foundation model like our BriVL, we can visualize any text input by using the joint image-text embedding space as the bridge. Concretely, we first input a piece of text and obtain its text embedding through the text encoder of BriVL. Next, we ...
By defining the 3-dimensional end-effector position as the primary task, the robot gains three additional functional redundant degrees-of-freedom (DOF). This redundancy allows the robot to be moved to different configurations for calibration. It is important to select spherical joint locations where...
23-05-24 JTFT NN 2024 A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting None 23-05-30 HSTTN IJCAI 2023 Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer None 23-05-30 Client Arxiv 2023 Client: Cross-variable Linear Integrated Enhanced ...