Papers with multilayer perceptron (

2 papers
EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation (2021.findings-emnlp)

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Challenge: Pre-trained language models have shown remarkable results on various NLP tasks.
Approach: They propose to improve the feed-forward network (FFN) in BERT with a higher computational cost than improving the multi-head attention (MHA).
Outcome: The proposed model is 6.9 smaller and 4.4 faster than BERTBASE and has competitive performances on GLUE and SQuAD Benchmarks.
LETS-C: Leveraging Text Embedding for Time Series Classification (2025.acl-long)

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Challenge: Recent advances in language modeling have shown promising results when applied to time series data.
Approach: They propose a method to fine-tune large language models for time series classification tasks using text embedding models and a simple classification head.
Outcome: The proposed model outperforms the current SOTA model on a time series classification benchmark and uses only 14.5% of the trainable parameters.

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