# Talks

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### How Can We Be So Slow? Winning the Hardware Lottery by Accelerating Sparse Networks

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### Estimation Rates for Sparse Linear Cyclic Causal Models

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### Fast Pose Graph Optimization via Krylov-Schur and Cholesky Factorization

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### Portable performance on multi-threaded Sparse BLAS operations with PyRSB

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### Hands On Tutorials: Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS

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### Learning Sparse Matrix Row Permutations for Efficient SpMM on GPU Architectures

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### GNNMark: A Benchmark Suite to Characterize Graph Neural Network Training on GPUs

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### CUTIE: Multi-PetaOP/s/W Ternary DNN inference Engine for TinyML

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### Gradient Boosting Machines (GBM): from Zero to Hero (with R and Python code)

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### Aaron Lun, Making the infrastructure sausage tales of Bioconductor package development

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### Peter Hickey, Workshop 500: The DelayedArray Framework to Support the Analysis of Large Datasets

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### useR! 2020: Prediction with text using tidy data principles

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### High Performance Linear System Solvers with Focus on Graph Laplacians

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### Small Big Data: using NumPy and Pandas when your data doesn't fit ...

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### tinyML Summit 2020 - Frans Sijstermans Mitch Harwell Robin Paul Prakash: NVIDIA Deep Learning...

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### CuPy: A NumPy Compatible Library for High Performance Computing with GPU | SciPy 2019 | SciPy 2019 |

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### Intermediate Methods for Geospatial Data Analysis | SciPy 2019 Tutorial | Serge Rey, Levi Wolf

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### To a Billion & Beyond: How to Visually Explore, Compare & Share Large Quant. Datasets w/ HiGlass

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### Emily Dodwell - From Tangled Lassos to Boosted Trees: Iterative Research in Practice

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### Automated Code Generation for Future-Compatible High-Performance Graph Libraries

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### RubyConf 2019 - Speeding up NMatrix by 100x by Udit Gulati 1

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