awesome-ml-for-systems
github.com/agrawalamey/awesome-ml-for-systems ↗📖 A curated list of resources dedicated to Machine Learning for Systems research
11
GitHub Stars
55
Curated Resources
4
Categories
1 month ago
Last Refreshed
ML for Systems workshop NeurIPS 2019:ML for Systems workshop ISCA 2019ML for Systems workshop NeurIPS 2018:Work authored by Tim Kraska
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me ml for systems workshop neurips 2018: resources from awesome-ml-for-systems"
Installation instructions →What's inside
ML for Systems workshop NeurIPS 2018:
- A K-means Cluster-Driven Calibration to Improve the Accuracy of Personal Wearable UV Sensors. Thomas Pumir, Emmanuel Dumont, Peter Kaplan, and Shayak Banerjee
- Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies. Lev Faivishevsky, Ashwin K Muppalla, Ravid Shwartz-Ziv, Ronen Laperdon, Benjamin Melloul, Tahi Hollander, and Amitai Armon
- Cache Miss Rate Predictability via Neural Networks. Rishikesh Jha*, Arjun Karuvally*, Saket Tiwari*, and J. Eliot B. Moss
- Chasing the Signal: Statistically Separating Multi-Tenant I/O Workloads. Si Chen and Avani Wildani
- Dali: Lazy Compilation & Kernel Fusion in Dynamic Computation Graphs. Jonathan Raiman
- DeepConf: Automating Data Center Network Topologies Management with Machine Learning. Saim Salman, Theophilus Benson, and Asim Kadav
ML for Systems workshop ISCA 2019
- AutoRank: Automated Rank Selection for Effective Neural Network Customization. Mohammad Samragh, Mojan Javaheripi, and Farinaz Koushanfar
- Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. Charith Mendis, Alex Renda, Saman Amarasinghe, and Michael Carbin
- Learning automatic schedulers with projective reparameterization. Ajay Jain, and Saman Amarasingh
- Optimal Learning-Based Network Protocol Selection. Xiaoxi Zhang, Siqi Chen, Youngbin Im, Maria Gorlotova, Sangtae Ha, and Carlee Joe-Wong
- SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training. Ahmed T. Elthakeb, Prannoy Pilligundla, and Hadi Esmaeilzadeh
ML for Systems workshop NeurIPS 2019:
- A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units. Adi Szeskin, Lev Faivishevsky, Ashwin K. Muppalla, Amitai Armon, and Tom Hope
- CodeCaption: A dataset for captioning data science code. Ioana Baldini, Kavitha Srinivas, and Jiri Navratil
- Defeating the Curse of Dimensionality to Scale JIT Fusion. Jonathan Raiman
- Learned TPU Cost Model for XLA Tensor Programs. Samuel J. Kaufman, Phitchaya Phothilimtha, and Mike Burrows
- Learning Caching Policies with Subsampling. Haonan Wang, Hao He, Mohammad Alizadeh, and Hongzi Mao
- Learning Multi-dimensional Indexing. Vikram Nathan, Jialin Ding, Mohammad Alizadeh, and Tim Kraska
Work authored by Tim Kraska
- FITing-Tree: A Data-aware Index Structure. Alex Galakatos, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, Tim Kraska. SIGMOD 2019
- From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems (Tutorial). Stratos Idreos and Tim Kraska. SIGMOD 2019
- Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?. Christopher Ré and Divy Agrawal and Magdalena Balazinska and Michael I. Cafarella and Michael I. Jordan and Tim Kraska and Raghu Ramakrishnan. SIGMOD 2015
- Neo: A Learned Query Optimizer. Ryan Marcus and Parimarjan Negi and Hongzi Mao and Chi Zhang and Mohammad Alizadeh and Tim Kraska and Olga Papaemmanouil and Nesime Tatbul. PVLDB 2019
- Park: An Open Platform for Learning-Augmented Computer Systems. Hongzi Mao and Parimarjan Negi and Akshay Narayan and Hanrui Wang and Jiacheng Yang and Haonan Wang and Ryan Marcus and Ravichandra Addanki and Mehrdad Khani Shirkoohi and Songtao He and Vikram Nathan and Frank Cangialosi and Shaileshh Venkatakrishnan and Wei-Hung Weng and Song Han and Tim Kraska and Dr.Mohammad Alizadeh. NeurIPS 2019
- SageDB: A Learned Database System. Tim Kraska and Mohammad Alizadeh and Alex Beutel and Ed H. Chi and Ani Kristo and Guillaume Leclerc and Samuel Madden and Hongzi Mao and Vikram Nathan. CIDR 2019
Showing a sample of 55 resources. View the full list on GitHub →