Search Results for author:
Found 9 papers, 3 papers with code
Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents
We present an approach for interpreting a black-box alarming system for forecasting accidents and anomalies during the drilling of oil and gas wells.
Forecasting the abnormal events at well drilling with machine learning
We present a data-driven and physics-informed algorithm for drilling accident forecasting.
A Differentiable Language Model Adversarial Attack on Text Classifiers
Moreover, due to the usage of the fine-tuned language model, the generated adversarial examples are hard to detect, thus current models are not robust.
Multi-fidelity Neural Architecture Search with Knowledge Distillation
The method relies on a new approach to low-fidelity evaluations of neural architectures by training for a few epochs using a knowledge distillation.
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing
In this work, we step outside the computer vision domain by leveraging the language modeling task, which is the core of natural language processing (NLP).
Application of Machine Learning to accidents detection at directional drilling
We present a data-driven algorithm and mathematical model for anomaly alarming at directional drilling.
Real-time data-driven detection of the rock type alteration during a directional drilling
During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20m between the bit and high-fidelity rock type sensors.
Gaussian Process Classification for Variable Fidelity Data
In this paper we address a classification problem where two sources of labels with different levels of fidelity are available.
Data-driven model for the identification of the rock type at a drilling bit
Directional oil well drilling requires high precision of the wellbore positioning inside the productive area.
