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In this video I cover:
* Perceiver (Perceiver: General Perception with Iterative Attention)
* Perceiver IO (Perceiver IO: A General Architecture for Structured Inputs & Outputs)
The goal was to create a modality-agnostic, general perception architecture that could work on images, videos, audio, text, etc. alike.
The main idea is to use the cross-attention module as a bottleneck layer that will map the input modality data into the latent space - this way we avoid the quadratic curse of transformers. After that powerful latent transformers are used to refine the representation - rinse and repeat.
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✅ Perceiver: https://arxiv.org/abs/2103.03206
✅ Perceiver IO: https://arxiv.org/abs/2107.14795
✅ Code: https://github.com/deepmind/deepmind-research/tree/master/perceiver
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⌚️ Timetable:
00:00 Intro
02:00 Perceiver architecture explained
05:40 Comparison with Facebook DETR model
07:05 Comparison to RNNs
08:35 Algorithmic complexity of Perceiver
10:35 Positional encodings and permutation equivariance
12:00 Results - ImageNet
14:35 Pixel permutation robustness
17:40 Attention visualized
20:20 Results - AudioSet
23:30 Results - Point Cloud
25:00 Perceiver IO
26:15 Decoder explained in depth (main contribution)
28:45 GLUE results (BERT baseline)
29:50 Outro
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💡 The AI Epiphany is a channel dedicated to simplifying the field of AI using creative visualizations and in general, a stronger focus on geometrical and visual intuition, rather than the algebraic and numerical "intuition".
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#perceiver #perceiverio #deepmind