Stanford MLSys Seminar

Apr 27 2022 6

Stanford MLSys Seminar Podcast artwork

Machine learning is driving exciting changes and progress in computing. What does the ubiquity of machine learning mean for how people build and deploy systems and applications? What challenges does industry face when deploying machine learning systems in the real world, and how can academia rise to meet those challenges? Updates every Monday and Friday - old episodes on Mondays, new episodes on Fridays! Check out our website and your YouTube channel for full videos! https://mlsys.stanford.edu/ https://www.youtube.com/channel/UCzz6ructab1U44QPI3HpZEQ

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#62 Dan Fu - Improving Transfer and Robustness of Supervised Contrastive Learning

Apr 27 2022 56 mins

Dan Fu - An ideal learned representation should display transferability and robustness. Supervised contrastive learning is a promising method for training accurate models, but produces representations that do not capture these properties due to class collapse -- when all points in a class map to the same representation. In this talk, we discuss how to alleviate these problems to im

#61 Kexin Rong - Big Data Analytics

Apr 22 2022 59 mins

Kexin Rong - Learned Indexing and Sampling for Improving Query Performance in Big-Data Analytics Traditional data analytics systems improve query efficiency via fine-grained, row-level indexing and sampling techniques. However, to keep up with the data volumes, increasingly many systems store and process datasets in large partitions containing hundreds of thousands of rows. Therefo

#60 Igor Markov - Looper: An End-to-End ML Platform for Product Decisions

Apr 11 2022 60 mins

Igor Markov - Looper: an end-to-end ML platform for product decisions Episode 60 of the Stanford MLSys Seminar Series! Looper: an end-to-end ML platform for product decisions Speaker: Igor Markov Abstract: Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and o

#59 Zhuohan Li - Alpa: Automated Model-Parallel Deep Learning

Apr 04 2022 55 mins

Zhuohan Li - Alpa: Automated Model-Parallel Deep Learning Alpa (https://github.com/alpa-projects/alpa) automates model-parallel training of large deep learning models by generating execution plans that unify data, operator, and pipeline parallelism. Alpa distributes the training of large deep learning models by viewing parallelisms as two hierarchical levels: inter-operator and int

3/10/22 #58 Shruti Bhosale - Multilingual Machine Translation

Mar 18 2022 57 mins

Shruti Bhosale - Scaling Multilingual Machine Translation to Thousands of Language Directions Existing work in translation has demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. However, much of this work is English-Centric by training only on data which was translated from or to Engli

3/3/22 #57 Vijay Janapa Reddi - TinyML, Harvard Style

Mar 04 2022 57 mins

Vijay Janapa Reddi - Tiny Machine Learning Tiny machine learning (TinyML) is a fast-growing field at the intersection of ML algorithms and low-cost embedded systems. TinyML enables on-device analysis of sensor data (vision, audio, IMU, etc.) at ultra-low-power consumption (less than 1mW). Processing data close to the sensor allows for an expansive new variety of always-on ML use-ca

2/24/22 #56 Fait Poms - Interactive Model Development

Feb 28 2022 55 mins

Fait Poms - A vision for interactive model development: efficient machine learning by bringing domain experts in the loop Building computer vision models today is an exercise in patience--days to weeks for human annotators to label data, hours to days to train and evaluate models, weeks to months of iteration to reach a production model. Without tolerance for this timeline or acces

1/28/21 #10 Travis Addair - Deep Learning at Scale with Horovod

Feb 23 2022 59 mins

Travis Addair - Horovod and the Evolution of Deep Learning at Scale Deep neural networks are pushing the state of the art in numerous machine learning research domains; from computer vision, to natural language processing, and even tabular business data. However, scaling such models to train efficiently on large datasets imposes a unique set of challenges that traditional bat

2/17/22 #55 Doris Lee - Visualization for Data Science

Feb 19 2022 58 mins

Doris Lee - Always-on Dataframe Visualizations with Lux Visualizations help data scientists discover trends, patterns, identify outliers, and derive insights from their data. However, existing visualization libraries in Python require users to write a substantial amount of code for plotting even a single visualization, often hindering the flow of data exploration. In this talk, you

1/21/21 #9 Song Han - Reducing AI's Carbon Footprint

Feb 15 2022 56 mins

Song Han - TinyML: Reducing the Carbon Footprint of Artificial Intelligence in the Internet of Things (IoT) Deep learning is computation-hungry and data-hungry. We aim to improve the computation efficiency and data efficiency of deep learning. I will first talk about MCUNet[1] that brings deep learning to IoT devices. The technique is tiny neural architecture search (TinyNAS) co-de