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Pretraining-Based Natural Language Generation for Text Summarization
Paper • 1902.09243 • Published • 2 -
Unified Vision-Language Pre-Training for Image Captioning and VQA
Paper • 1909.11059 • Published • 2 -
When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes
Paper • 2404.12365 • Published • 2
Collections
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 32 -
Transformers Can Achieve Length Generalization But Not Robustly
Paper • 2402.09371 • Published • 14 -
Triple-Encoders: Representations That Fire Together, Wire Together
Paper • 2402.12332 • Published • 2 -
BERTs are Generative In-Context Learners
Paper • 2406.04823 • Published • 1
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Paper • 1901.08746 • Published • 6 -
Pretraining-Based Natural Language Generation for Text Summarization
Paper • 1902.09243 • Published • 2 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Paper • 2006.03654 • Published • 4
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 32 -
Transformers Can Achieve Length Generalization But Not Robustly
Paper • 2402.09371 • Published • 14 -
A Thorough Examination of Decoding Methods in the Era of LLMs
Paper • 2402.06925 • Published • 1 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 107
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Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes
Paper • 2110.05909 • Published • 2 -
Deep Residual Learning for Image Recognition
Paper • 1512.03385 • Published • 18 -
Wide Residual Networks
Paper • 1605.07146 • Published • 2 -
Comprehensive Survey of Model Compression and Speed up for Vision Transformers
Paper • 2404.10407 • Published • 1
-
Pretraining-Based Natural Language Generation for Text Summarization
Paper • 1902.09243 • Published • 2 -
Unified Vision-Language Pre-Training for Image Captioning and VQA
Paper • 1909.11059 • Published • 2 -
When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes
Paper • 2404.12365 • Published • 2
-
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Paper • 1901.08746 • Published • 6 -
Pretraining-Based Natural Language Generation for Text Summarization
Paper • 1902.09243 • Published • 2 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10 -
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Paper • 2006.03654 • Published • 4
-
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 32 -
Transformers Can Achieve Length Generalization But Not Robustly
Paper • 2402.09371 • Published • 14 -
A Thorough Examination of Decoding Methods in the Era of LLMs
Paper • 2402.06925 • Published • 1 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 107
-
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 32 -
Transformers Can Achieve Length Generalization But Not Robustly
Paper • 2402.09371 • Published • 14 -
Triple-Encoders: Representations That Fire Together, Wire Together
Paper • 2402.12332 • Published • 2 -
BERTs are Generative In-Context Learners
Paper • 2406.04823 • Published • 1
-
Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes
Paper • 2110.05909 • Published • 2 -
Deep Residual Learning for Image Recognition
Paper • 1512.03385 • Published • 18 -
Wide Residual Networks
Paper • 1605.07146 • Published • 2 -
Comprehensive Survey of Model Compression and Speed up for Vision Transformers
Paper • 2404.10407 • Published • 1