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Found 6 papers, 1 papers with code
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework
Variational Autoencoder (VAE) is an effective framework to model the interdependency for non-autoregressive neural machine translation (NAT).
A Novel ICD Coding Framework Based on Associated and Hierarchical Code Description Distillation
To address these problems, we propose a novel framework based on associated and hierarchical code description distillation (AHDD) for better code representation learning and avoidance of improper code assignment. we utilize the code description and the hierarchical structure inherent to the ICD codes.
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
In our experiments, the proposed method achieves a sensitivity of 85. 0% and specificity of 92. 6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal.
Unified Multimodal Model with Unlikelihood Training for Visual Dialog
Prior work performs the standard likelihood training for answer generation on the positive instances (involving correct answers).
A Speaker-aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-turn Dialogue Generation
Differing from prior work that solely relies on the content of conversation history to generate a response, we argue that capturing relative social relations among utterances (i. e., generated by either the same speaker or different persons) benefits the machine capturing fine-grained context information from a conversation history to improve context coherence in the generated response.
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data
Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands.
