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Found 5 papers, 3 papers with code
Non-autoregressive Generative Models for Reranking Recommendation
Hence, we propose a Non-AutoRegressive generative model for reranking Recommendation (NAR4Rec) designed to enhance efficiency and effectiveness.
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations
Transformer models bring propelling advances in various NLP tasks, thus inducing lots of interpretability research on the learned representations of the models.
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation
It has been commonly observed that a teacher model with superior performance does not necessarily result in a stronger student, highlighting a discrepancy between current teacher training practices and effective knowledge transfer.
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration
In this paper, to address the distribution shift in learning with instance-dependent label noise, a dynamic distribution-calibration strategy is adopted.
Exploring Extreme Parameter Compression for Pre-trained Language Models
A tiny version achieves $96. 7\%$ performance of BERT-base with $ {1}/{48} $ encoder parameters (i. e., less than 2M parameters excluding the embedding layer) and $2. 7 \times$ faster on inference.
