Problem (1) has been comprehensively investigated in the literature [Duchi et al., 2011, Kingma and Ba, 2015, Loshchilov and Hutter, 2017], and it is well-known that the classical stochastic gradient descent (SGD) achieves a convergence rate of

Being still in its early stage, there are fundamental issues: scalability,reward design, and end-toend learning paradigm etc.

The code is available at github.com/TedLentsch/UNION. Figure 1: UNION discovers mobile objects (e.g.

The AUCsonthetrainingand are 0.855 and 0.859, respectively.

Foundation models are trained on increasingly immense and opaque datasets.

Deep learning methods have boosted the adoption of NLP systems in real-life applications.

However, if intrinsic parameters change, neural computations are altered drastically.