Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data.

Neural Information Processing Systems http://nips.cc/

Our method incorporates three novel mechanisms to leverage the unique characteristics of MedTS: cross-channel patching to leverage inter-channel correlations, multi-granularity embedding for capturing features at different scales, and two-stage (intra-and inter-granularity) multi-granularity self-attention for learning features and correlations within and among granularities. We conduct extensive experiments on five public datasets under both subject-dependent and challenging subject-independent setups. Results demonstrate Medformer's superiority over 10 baselines, achieving top averaged ranking across five datasets on all six evaluation metrics. These findings underscore the significant impact of our method on healthcare applications, such as diagnosing Myocardial Infarction, Alzheimer's, and Parkinson's disease.

Inthis work, we propose analgorithm-software co-designed sparse convolution based onanovelout-vector-wise (OVW) sparse pattern.

There exists an online convex optimization problem where Adam (and RMSprop) has non-zero average regret, and one of the problem is in the form ft(x)= ( Px, if t mod P =1 x, Otherwise x [ 1,1], P N,P 3 (1) Proof. See [1] Thm.1 for proof. For the problem defined above, there's a threshold of β2 above which RMSprop converge. For the problem defined by Eq. (1), ACProp algorithm converges β1,β2 (0,1), P N,P 3. Proof. We analyze the limit behavior of ACProp algorithm.