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Found 8 papers, 3 papers with code
Neuron-level Structured Pruning using Polarization Regularizer
Experimentally, we show that structured pruning using polarization regularizer achieves much better results than using L1 regularizer.
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking
However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated.
Adaptive Noise Injection: A Structure-Expanding Regularization for RNN
Due to the adaptive noises can be improved as the training processes, its negative effects can be weakened and even transformed into a positive effect to further improve the expressiveness of the main-branch RNN.
Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering
To solve the issue for the intermediate layers, we propose an efficient Quaternion Block Network (QBN) to learn interaction not only for the last layer but also for all intermediate layers simultaneously.
Contrastive Visual-Linguistic Pretraining
We evaluate CVLP on several down-stream tasks, including VQA, GQA and NLVR2 to validate the superiority of contrastive learning on multi-modality representation learning.
A Hierarchical User Intention-Habit Extract Network for Credit Loan Overdue Risk Detection
In this paper, we propose a model named HUIHEN (Hierarchical User Intention-Habit Extract Network) that leverages the users' behavior information in mobile banking APP.
Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking
However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn.
Dense Contrastive Visual-Linguistic Pretraining
To overcome these issues, we propose unbiased Dense Contrastive Visual-Linguistic Pretraining (DCVLP), which replaces the region regression and classification with cross-modality region contrastive learning that requires no annotations.
