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Found 10 papers, 3 papers with code
Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data
In this paper, we introduce a weighted spectral filter approach to reduce the condition number of the kernel matrix and then stabilize kernel interpolation.
Data Drift Monitoring for Log Anomaly Detection Pipelines
Logs enable the monitoring of infrastructure status and the performance of associated applications.
Towards More Efficient Depression Risk Recognition via Gait
Gait is a complex form of motion, and hand-crafted gait features often only capture a fraction of the intricate associations between gait and depression risk.
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for the same purpose.
Hard negative examples are hard, but useful
We offer a simple fix to the loss function and show that, with this fix, optimizing with hard negative examples becomes feasible.
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An Empirical Study of Factors Affecting Language-Independent Models
Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches.
Policy Continuation with Hindsight Inverse Dynamics
This approach learns from Hindsight Inverse Dynamics based on Hindsight Experience Replay, enabling the learning process in a self-imitated manner and thus can be trained with supervised learning.
Visualizing How Embeddings Generalize
Deep metric learning is often used to learn an embedding function that captures the semantic differences within a dataset.
Characterizing machine learning process: A maturity framework
For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to convert business requirements from offering managers into data requirements for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, and how to customize general purpose machine learning models with industry, domain, and use case specific data to make them more accurate for specific situations etc.
Challenge AI Mind: A Crowd System for Proactive AI Testing
The evaluation shows that the crowd workflow is more effective with the help of machine learning techniques.
