Completed
Why do we need Ripser++
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
GPU Accelerated Computation of VR Barcodes in Evaluating Deep Learning Models
Automatically move to the next video in the Classroom when playback concludes
- 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
