This proposal is shown in the bottom half of Figure 1, for a sequence-to-sequence encoder-decoder architecture. We call this proposed architecture adifferentiable search index(DSI), and implement it with a largepre-trained Transformer (Vaswanietal.,2017)model,

When a model's predicted number of events within any time interval is similar to the observed number, it is called well-calibrated . A survival model's calibration can be measured using, for instance, distributional calibration (

The high-level question in this work is: If we learn a task using a sufficiently deep NN, how can we uncover the underlying hierarchical organization of sub-functions in that task?

The robot could learn from these demonstrations to complete the tasks autonomously. For IL algorithms to be useful, however, they must be able to learn how to perform tasks from few demonstrations. A domestic robot wouldn't be very helpful if it required thirty demonstrations before it figured out that you are deliberately washing your purple cravat

Inparticular,standardmetricformulations as hierarchical k-center,k-means, andk-median received a lot of attention and the problems have been studied extensively in different models of computation.

The objective of sequential recommendation is to predict the next likely item for users based on theirhistoricalrecords[7,54].

To make the proof concise, we will assume matrixP is connected, otherwise we can perform the same operation inside each block. With the help of NDLS, Random Forest and XGBoost outperforms their base models by6.1% and 7.5% respectively. In these three networks, papers from different topics are considered asnodes, and the edges are citations among the papers. Industry is a short-form video graph, collected from a real-world mobile application from our industrial cooperativeenterprise. Wesampled 1,000,000 users and videos from the app, and treat these items as nodes.