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Found 11 papers, 1 papers with code
NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
Existing approaches either fine-tune the pre-trained model itself or, more efficiently, but at the cost of accuracy, train adaptor models to transform the output of the pre-trained model.
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations.
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows
Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning.
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
It is well-known that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model.
Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved.
Helix: Accelerating Human-in-the-loop Machine Learning
Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance.
How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature
A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems.
The Importance of Norm Regularization in Linear Graph Embedding: Theoretical Analysis and Empirical Demonstration
Both theoretical and empirical evidence are provided to support this argument: (a) we prove that the generalization error of these methods can be bounded by limiting the norm of vectors, regardless of the embedding dimension; (b) we show that the generalization performance of linear graph embedding methods is correlated with the norm of embedding vectors, which is small due to the early stopping of SGD and the vanishing gradients.
On the Interpretability of Conditional Probability Estimates in the Agnostic Setting
We study the interpretability of conditional probability estimates for binary classification under the agnostic setting or scenario.
Indexing Cost Sensitive Prediction
We develop algorithms and indexes to support cost-sensitive prediction, i. e., making decisions using machine learning models taking feature evaluation cost into account.
Minimizing Uncertainty in Pipelines
In this paper, we consider the problem of asking the optimal set of queries to minimize the resulting output uncertainty.
