In recent years, Dynamic Sparse Training (DST) has emerged as an alternative to post-training pruning for generating efficient models. In principle, DST allows for a more memory efficient training process, as it maintains sparsity throughout the entire training run. However, current DST implementations fail to capitalize on this in practice. Because sparse matrix multiplication is much less efficient than dense matrix multiplication on GPUs, most implementations simulate sparsity by masking weights.

We study thorax disease classification in this paper.

In this work, we study the relationship between these objectives.

Deputy Tokyo Gov. Manabu Miyasaka speaks during a SusHi Tech pre-event in Tokyo on Monday. Tokyo's annual startup convention, SusHi Tech Tokyo, is growing to be Asia's largest startup event with this year's conference in April set to focus on artificial intelligence, robotics, resilience and entertainment. The fourth SusHi Tech Tokyo -- which stands for Sustainable High City Tech Tokyo -- is expected to be the largest to date, with over 700 startups in participation. It will be held from April 27 to 29, with the first two days reserved for businesses and the final day open to the public. "(SusHi Tech Tokyo) has grown into Asia's largest innovation conference," Manabu Miyasaka, Tokyo's deputy governor, said on Monday.

However, the design of methods for learning credal set predictors remains a challenging problem. In this paper, we make use of conformal prediction for this purpose.

Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (Separated Models for Generalization), a novel approach that exploits image reconstruction for generalization.

This transformation naturally implements a parameterization for the relaxation of permutation matrices, allowing for gradient-based optimization of problems involving permutations.