Andrew Fairless, Ph.D.
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Posts
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2021-01-23
What I Read: Multi-Armed Bandits and Experimentation
2021-01-23
What I Read: Traffic prediction with Graph Neural Networks
2021-01-22
What I Read: Bayesian Product Ranking
2021-01-22
What I Read: Continual Learning... and the Bridge to Active and Open World Learning
2021-01-21
What I Read: Transformer Architecture
2021-01-20
What I Read: Biden’s Covid-19 Plan Is Maddeningly Obvious
2021-01-20
What I Read: testing for machine learning
2021-01-20
What I Read: why Switch from Jupyter Notebook to Scripts
2021-01-19
What I Read: Autotuning Multi-Objective Optimization
2021-01-19
What I Read: Maintaining Machine Learning in Production
2021-01-18
What I Read: Floating-Point and Deep Learning
2021-01-18
What I Read: Improving AI Economics
2021-01-17
What I Read: Air pollution is worse than we thought
2021-01-17
What I Read: Data Scientists Should Be More End-to-End
2021-01-16
What I Read: Open-source bionic leg
2021-01-16
What I Read: Think Like an Epidemiologist
2021-01-15
What I Read: Covid-19, Most of Us Have Risk Exactly Backward
2021-01-15
What I Read: End-to-End Machine Learning Platforms
2021-01-14
What I Read: R squared Does Not Measure Predictive Capacity
2021-01-14
What I Read: ScaNN, Efficient Vector Similarity Search
2021-01-13
What I Read: Effort to Stop the Coronavirus in Nursing Homes
2021-01-13
What I Read: Snorkel Tutorial to Predict Multiple Sclerosis
2021-01-12
What I Read: Intro to Data Engineering for Data Scientists
2021-01-12
What I Read: Mitochondria, Anxiety and Mental Health
2021-01-11
What I Read: Making Netflix’s Data Infrastructure Cost-Effective
2021-01-11
What I Read: Patients aren’t being told about AI systems
2021-01-10
What I Read: A winners curse adjustment
2021-01-10
What I Read: This Algorithm Doesnt Replace Doctors
2021-01-09
What I Read: The Cost of AI Training is Improving
2021-01-09
What I Read: the Effort to Stop Maternal Deaths
2021-01-08
What I Read: Count, data notebook
2021-01-08
What I Read: ML For Python Developers
2021-01-07
What I Read: Mathematician’s Guide to Contagion
2021-01-07
What I Read: Running Machine Learning at Scale
2021-01-06
What I Read: Software engineering for Data Scientists
2021-01-06
What I Read: start deploying
2021-01-05
What I Read: making machine learning actually useful
2021-01-05
What I Read: Progress of Natural Language Processing
2021-01-04
What I Read: Symbolic Models from Deep Learning
2021-01-04
What I Read: Who Is Responsible When Autonomous Systems Fail?
2021-01-03
What I Read: Computers Roll Loaded Dice
2021-01-03
What I Read: GPT-3, a Giant Step for NLP
2021-01-02
What I Read: Five Cognitive Biases
2021-01-02
What I Read: Nitpicking ML Technical Debt
2021-01-01
What I Read: data distributions
2021-01-01
What I Read: Differentiable Reasoning over Text
2020-12-31
What I Read: Flows for simultaneous manifold learning and density estimation
2020-12-31
What I Read: Scientists Taught Mice to Smell an Odor That Doesn’t Exist
2020-12-30
What I Read: Learning Neural Causal Models
2020-12-30
What I Read: To Adapt to Tech, We’re Heading Into the Shadows
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