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Found 7 papers, 1 papers with code
Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification
Using this free annotation, we introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches.
Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attention
Electronic Health Record (EHR) provides abundant information through various modalities.
Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution
Accurate time prediction of patients' critical events is crucial in urgent scenarios where timely decision-making is important.
Self-Knowledge Distillation in Natural Language Processing
Since deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks, and in some cases, they are even outperforming humans.
Disentangling Latent Factors of Variational Auto-Encoder with Whitening
The proposed method is applied to the latent variables of variational auto-encoder (VAE), although it can be applied to any generative models with latent variables.
Gradient Acceleration in Activation Functions
Based on this explanation, we propose a new technique for activation functions, {\em gradient acceleration in activation function (GAAF)}, that accelerates gradients to flow even in the saturation area.
Understanding Dropout as an Optimization Trick
Based on this explanation, we propose a new technique for activation functions, {\em gradient acceleration in activation function (GAAF)}, that accelerates gradients to flow even in the saturation area.
