Resource of free step by step video how to guides to get you started with machine learning.
Sunday, May 31, 2020
Synthesizer: Rethinking Self-Attention in Transformer Models (Paper Explained)
Do we really need dot-product attention? The attention mechanism is a central part of modern Transformers, mainly due to the dot-product attention mechanism. This paper changes the mechanism to remove the quadratic interaction terms and comes up with a new model, the Synthesizer. As it turns out, you can do pretty well like that! OUTLINE: 0:00 - Intro & High Level Overview 1:00 - Abstract 2:30 - Attention Mechanism as Information Routing 5:45 - Dot Product Attention 8:05 - Dense Synthetic Attention 15:00 - Random Synthetic Attention 17:15 - Comparison to Feed-Forward Layers 22:00 - Factorization & Mixtures 23:10 - Number of Parameters 25:35 - Machine Translation & Language Modeling Experiments 36:15 - Summarization & Dialogue Generation Experiments 37:15 - GLUE & SuperGLUE Experiments 42:00 - Weight Sizes & Number of Head Ablations 47:05 - Conclusion Paper: https://ift.tt/3cldH5V My Video on Transformers (Attention Is All You Need): https://youtu.be/iDulhoQ2pro My Video on BERT: https://youtu.be/-9evrZnBorM Abstract: The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required? This paper investigates the true importance and contribution of the dot product-based self-attention mechanism on the performance of Transformer models. Via extensive experiments, we find that (1) random alignment matrices surprisingly perform quite competitively and (2) learning attention weights from token-token (query-key) interactions is not that important after all. To this end, we propose \textsc{Synthesizer}, a model that learns synthetic attention weights without token-token interactions. Our experimental results show that \textsc{Synthesizer} is competitive against vanilla Transformer models across a range of tasks, including MT (EnDe, EnFr), language modeling (LM1B), abstractive summarization (CNN/Dailymail), dialogue generation (PersonaChat) and Multi-task language understanding (GLUE, SuperGLUE). Authors: Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao, Che Zheng Links: YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB
Subscribe to:
Post Comments (Atom)
-
JavaやC++で作成された具体的なルールに従って動く従来のプログラムと違い、機械学習はデータからルール自体を推測するシステムです。機械学習は具体的にどのようなコードで構成されているでしょうか? 機械学習ゼロからヒーローへの第一部ではそのような疑問に応えるため、ガイドのチャー...
-
#deeplearning #noether #symmetries This video includes an interview with first author Ferran Alet! Encoding inductive biases has been a lo...
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
How to Do PS2 Filter (Tiktok PS2 Filter Tutorial), AI tiktok filter Create your own PS2 Filter photos with this simple guide! 🎮📸 Please...
-
Challenge scenario You were recently hired as a Machine Learning Engineer at a startup movie review website. Your manager has tasked you wit...
-
#ai #attention #transformer #deeplearning Transformers are famous for two things: Their superior performance and their insane requirements...
-
Visual scenes are often comprised of sets of independent objects. Yet, current vision models make no assumptions about the nature of the p...
-
Why are humans so good at video games? Maybe it's because a lot of games are designed with humans in mind. What happens if we change t...
-
#alibi #transformers #attention Transformers are essentially set models that need additional inputs to make sense of sequence data. The mo...
-
Future skill Machine Learning Free Course -https://futureskillsprime.in/course/machine-learning-linear-regressionfree ai and machine learnin...
No comments:
Post a Comment