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Applications of data valuation in machine learning
At TransferLab we have extensively covered existing and developing methods for Data valuation, the task of attributing value to samples in a …
FLAME 2023: Diving into the Future of Fluid Dynamics and Machine Learning
At the Stanford FLAME AI Workshop 2023, I immersed myself in the intersection of machine learning and fluid dynamics, benefiting from …
AutoDev: Exploring Custom LLM-Based Coding Assistance Functions
We explore the potential of custom code assistant functions based on large language models (LLMs). With our open-source software package …
Our take on IJCAI 2022
The International Joint Conference on Artificial Intelligence (IJCAI) is a premier event for researchers working in all areas of AI.
The hidden assumptions and pitfalls of density based anomaly detection
Anomaly detection is a notoriously ill-defined problem. The notion of an anomaly is arguably subjective since it depends to some extent on …
Our take on ICLR 2022 - Part 2
ICLR is one of the most important and prestigious conferences in the ML community. It spans all ML topics, from embeddings to Graph neural …
Our take on ICLR 2022 - Part 1
ICLR is one of the most important and prestigious conferences in the ML community. It spans all ML topics, from embeddings to Graph neural …
Certified error rates for neural networks
Adversarial training has been shown to improve robustness of neural networks to certain classes of data perturbations. Despite constant …
Natural, Trust Region and Proximal Policy Optimization
We present an overview of the theory behind three popular and related algorithms for gradient based policy optimization: natural policy …
Cross-validation: what does it estimate?
In Cross-validation: what does it estimate and how well does it do it? Bates et al. focus on cross validation for error estimation and show …
Calibration of Classifiers
We discuss the calibration of probabilistic classifiers, i.e. the question of whether the output vectors of such classifiers accurately …
Solving PDEs With Neural Networks
We discuss some current trends in applying ML to the solution of differential equations, and the difficulties faced. We focus on a family of …