Media Summary: SpanBERT: Improving Pre-training by Representing and Predicting Spans Course Materials: ... ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators Course Materials: ... Rethinking Attention with Performers Course Materials:

Bart Lecture 56 Part 4 Applied Deep Learning Supplementary - Detailed Analysis & Overview

SpanBERT: Improving Pre-training by Representing and Predicting Spans Course Materials: ... ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators Course Materials: ... Rethinking Attention with Performers Course Materials: Reformer: The Efficient Transformer Course Materials: Don't Stop Pretraining: Adapt Language Models to Domains and Tasks Course Materials: ... Improving Language Understanding by Generative Pre-Training Course Materials: ...

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention Course Materials: ... Attention Is All You Need Course Materials:

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BART | Lecture 56 (Part 4) | Applied Deep Learning (Supplementary)
SpanBERT | Lecture 56 (Part 3) | Applied Deep Learning (Supplementary)
ELECTRA | Lecture 57 (Part 4) | Applied Deep Learning (Supplementary)
Performers | Lecture 51 (Part 4) | Applied Deep Learning (Supplementary)
BART (Natural Language Processing at UT Austin)
Reformer | Lecture 56 (Part 2) | Applied Deep Learning
BART And Other Pre-Training (Natural Language Processing at UT Austin)
Don’t Stop Pretraining | Lecture 55 (Part 3) | Applied Deep Learning (Supplementary)
GPT-1 (Q&A) | Lecture 52 (Part 4) | Applied Deep Learning (Supplementary)
Transformers are RNNs | Lecture 51 (Part 3) | Applied Deep Learning (Supplementary)
Transformer | Lecture 56 (Part 1) | Applied Deep Learning
L19.5.2.6 BART:  Combining Bidirectional and Auto-Regressive Transformers
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