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GPU Acceleration after the End of Moore
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Classroom Contents
GPU Accelerated Computation of VR Barcodes in Evaluating Deep Learning Models
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- 1 Intro
- 2 GPU Acceleration after the End of Moore
- 3 Challenges to achieve GPU acceleration
- 4 GPUs in Deep Learning
- 5 The Simplex-wise Flag Filtration
- 6 Persistent homology: Birth and Death for of the C. elegans Dataset
- 7 Design Goals for High Performance
- 8 Efficient Persistent Pair Hashmap
- 9 Filtration Construction with Clearing is jus Filtering and Sorting Problem
- 10 Why do we need Ripser++
- 11 What is a Generative Adversarial Network
- 12 Deep learning model evaluation: using topology
- 13 MTop-Divergence Properties
- 14 Computational aspect of MTopDiv
- 15 Experiments with MTopDiv
- 16 Detecting distribution shifts
- 17 Computational considerations
- 18 Conclusion
- 19 VR barcodes of attention graphs as feature • Pretrained or finetuned BERT model with pretrained Key, Query Weight matrices. For each head compute the matrix of pairwise self attention
