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computational expressivity of transformers
0:45:13
The Parallelism Tradeoff: Understanding Transformer Expressivity Through Circuit Complexity
0:50:52
Computational Benefits and Limitations of Transformers and State-Space Models
0:27:14
Transformers, the tech behind LLMs | Deep Learning Chapter 5
0:54:39
Rethinking Attention with Performers (Paper Explained)
0:04:53
FLatten Transformer: Vision Transformer using Focused Linear Attention
0:18:42
SakanaAI Unveils 'Transformer Squared' - Test Time LEARNING
0:19:15
How do Vision Transformers work? – Paper explained | multi-head self-attention & convolutions
1:09:56
Cyril Zhang | How do Transformers reason? First principles via automata, semigroups, and circuits
1:13:04
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding (Paper Explained)
1:00:27
Kaggle Reading Group: Generating Long Sequences with Sparse Transformers | Kaggle
0:20:33
Gradient descent, how neural networks learn | Deep Learning Chapter 2
0:37:43
Theoretical Limitations of Multi layer Transformers
0:57:30
It Ain't Broke So D̶o̶n̶'t̶ F̶i̶x̶ Let's Break It
0:51:28
Clayton Sanford: Representational Strengths and Limitations of Transformers
0:41:35
Re-thinking Transformers: Searching for Efficient Linear Layers over a Continuous Space of...
0:31:35
NEW AI Models: Hierarchical Reasoning Models (HRM)
0:28:19
[ICFP'23] Modular Models of Monoids with Operations
1:01:57
Kaggle Reading Group: Generating Long Sequences with Sparse Transformers (Part 2) | Kaggle
0:48:07
OpenAI CLIP: ConnectingText and Images (Paper Explained)
0:34:23
FNet: Mixing Tokens with Fourier Transforms (Machine Learning Research Paper Explained)
0:38:35
Can Wikipedia Help Offline Reinforcement Learning? (Paper Explained)
0:36:15
Byte Latent Transformer: Patches Scale Better Than Tokens (Paper Explained)
0:24:26
CARTA: Computational Neuroscience and Anthropogeny with Terry Sejnowski
0:45:32
'Blueprints for a Universal Reasoning Machine' by Zenna Tavares (Strange Loop 2022)
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