We obtain state-of-the-art accuracy on the CoNLL-2012 datasets with 83. 3 F1-score for English (a 2. 3 higher F1-score than previous work (Dobrovolskii, 2021)) using only CoNLL data for training, 68. 5 F1-score for Arabic (+4. 1 higher than previous work) and 74. 3 F1-score for Chinese (+5. 3).
Ranked #1 on Coreference Resolution on OntoNotes
We consider the classic facility location problem in fully dynamic data streams, where elements can be both inserted and deleted.
Data Structures and Algorithms
While return-conditioning is at the heart of popular algorithms such as decision transformer (DT), these methods tend to perform poorly in highly stochastic environments, where an occasional high return can arise from randomness in the environment rather than the actions themselves.
If one sees the place name Houston Mercer Dog Run in New York, how does one know how to pronounce it?
We combine the capacity of sparsely gated Mixture-of-Experts (MoE) with the speed and stability of linear, mixing transformations to design the Sparse Mixer encoder model.
Then, we extensively evaluate three classes of Edge TPUs, covering different computing ecosystems, that are either currently deployed in Google products or are the product pipeline, across 423K unique convolutional neural networks.
We believe that this novel methodology for ML development can be demonstrated through a modularized representation of ML models and the definition of novel abstractions allowing to implement and execute diverse methods for the asynchronous use and extension of modular intelligent systems.