no code implementations • • Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
Contracts are arguably the most important type of business documents.
It attends to relevant segments for each query with a temporal attention mechanism, and can be trained using only the labels for each query.
no code implementations • 3 Nov 2020 • Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, tejas kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin Riedmiller
Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.
In this work, we illustrate how the neural network representations which underpin modern vision systems are subject to supervision collapse, whereby they lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains.
Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements.
We propose KeypointGAN, a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses.
In this work we aim to learn object representations that are useful for control and reinforcement learning (RL).
End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition.
We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision.
Ranked #1 on Unsupervised Facial Landmark Detection on MAFL