Advanced numerical methods and neural networks are explored as part of a procedure to improve the performance of trained decision trees without requiring access to training, validation, or test data. By using only the information contained within a trained decision tree, a model's performance can be improved by more than 5% over the original trained tree. There are many applications of this technique such as when data may not be retained, where federated learning is employed, or where models are housed on edge devices.

Modeling the Model: Improving Trained Decision Trees Without Access to Data

Neural networks and ensemble techniques achieve state-of-the-art results but often incur a considerable resource cost. Decision trees, in contrast, require comparatively fewer resources, to the detriment of model performance. To bridge this gap, soft trees are introduced. Soft trees represent a novel variant of decision trees which incorporate additional contextual information into the predictions. Under this methodology, predictions comprise a summation of leaf values weighted by threshold distance and probability. Such predictions provide a significant improvement over the performance of standard trees while retaining trees’ reduced-resource properties. Soft tree models may then allow applications to realize the resource benefits of using a decision tree while also producing improved results within a constrained environment.

Soft Trees: Smoothing Decision Thresholds for Improved Performance

The Digital Aviation Readiness Technology Engine (DARTE) Board provides a comprehensive, up-to-date picture of future F/A-18 aircraft readiness at the squadron and buno levels across the Naval Aviation Enterprise. It acts as a visual user interface in the form of Qlik dashboards for the Mission Capable (MC) status forecast powered by machine learning in ADVANA Databricks. This tool uses models created with the DARTE framework to forecast the daily MC status of squadrons and units of a TMS across the entire fleet for the next seven days. Predictions are also interrogated with explainable AI (XAI) so that users can see the leading factors that affect the predictions. The DARTE Board can display this information in multiple functional views, providing the warfighters with a tool to derive data-driven actionable insights into future readiness.

Visualizing the Digital Aviation Readiness Technology Engine (DARTE) with the Advana Platform

Understanding and improving readiness is a critical task for Naval Aviation. The Digital Aviation Readiness Technology Engine (DARTE) is a proven paradigm for predicting readiness metrics such as mission capable (MC) status and expected quarterly flight hour (FH) execution for F/A-18 squadrons. The next generation of DARTE implements cutting-edge AI/ML techniques such as Long Short Term Memory networks with Attention and Hyper Deep Ensembles to extend MC prediction time horizons. We explore adding new capabilities to DARTE by utilizing Topological Data Analysis to yield new data insights, Natural Language Processing/Understanding to incorporate new data sources, Reinforcement Learning to recommend maintainer movements across the fleet, and expanding the MC prediction model to the MH-60. Additionally, we show how the products of these models can be delivered to the warfighter.

Daily Readiness Predictions with the Digital Aviation Readiness Technology Engine

Please click on the poster to see the abstract or leave your comment to the author.

Poster Session Day 1

poster

### Welcome to the 7th annual workshop on Naval Applications of Machine Learning!

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific<br><br>

<button name="button" onclick="window.open('https://sites.google.com/go.spawar.navy.mil/naml/','blank')">**NAML 2023 Website**</button>

<br><br><br>

To access the site please register [**here**](https://events.r20.constantcontact.com/register/eventReg?oeidk=a07ejmzkc5g71584a82&oseq=&c=&ch=). 

NAML 2023

The 7th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


Poster Session 1B

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


<figure class="video_container">
  <iframe src="https://s3.amazonaws.com/pf-user-files-01/u-59356/uploads/2022-03-22/hk13mxo/NAML_ReceptionWithtitle.mp4" width=826  height=526  frameborder="0" allowfullscreen="true"> </iframe>
</figure>

NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community




**Title:** Platform Readiness

**Moderator:** Kirk Horton, Naval Safety Command

               
        


Benjamin Michlin

4

SHORT BIO

Presentations

Modeling the Model: Improving Trained Decision Trees Without Access to Data

Soft Trees: Smoothing Decision Thresholds for Improved Performance

Visualizing the Digital Aviation Readiness Technology Engine (DARTE) with the Advana Platform

Daily Readiness Predictions with the Digital Aviation Readiness Technology Engine

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