Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.
Compared to the naive voxel-level self-attention implementation, such volume-based operations help to reduce the computational complexity by approximate 98% and 99. 5% on Synapse and ACDC datasets, respectively.
Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmark datasets with multiple backbone architectures to evaluate common pitfalls and effects of different training tricks.
As a bi-product of the standard NLU benchmarks, we introduce a new downstream dataset on natural language inference (NLI) and show that BanglaBERT outperforms previous state-of-the-art results on all tasks by up to 3. 5%.
Interest has been rising lately towards methods representing data in non-Euclidean spaces, e. g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e. g. scale-free, hierarchical or cyclical.