Spain

ECAI 2020

Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem

learning heuristics

vehicle routing

deep reinforcement learning

optimization

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

**Watch this video to hear about the AFCEA purpose.**

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Registration for this workshop has now closed.

NAML 2021

The 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.


technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Improving Document Classification with Multi-Sense Embeddings

Reasoning with a Bounded Number of Resources in ATL+

Rewrite or not Rewrite? ML-based Algorithm Selection for Datalog Query Answering on Knowledge Graphs

Unsupervised Adversarial Learning of Anomaly Detection in the Wild

Ensemble Methods for Object Detection

Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking

Learning to Predict Charges for Legal Judgment via Self-Attentive Capsule Network

Bidirectional Scene Text Recognition with a Single Decoder

Humans are capable of abstracting away irrelevant details or distinguish the common properties when studying problems. This is especially noticeable for planning problems, as humans are able to disregard certain parts of the domain and focus on the key elements important for finding a plan. Recently, the notion of abstraction has been introduced for Answer Set Programming (ASP), a knowledge representation and reasoning paradigm widely used in problem solving, with the potential to understand the key elements of a program that play a role in finding a solution. This paper highlights the potential use of such an abstraction in getting to the essence of planning problems, expressed in ASP, by disregarding irrelevant details and computing abstract plans.

Abstraction for ASP Planning

In some abstract argumentation framework (AAF), arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real life situations the exact probabilities values are unknown and sometimes there is a need for aggregating the probability values of different sources. In this paper, we tackle the problem of calculating the degree of uncertainty of the extensions considering that the probability values of the arguments are imprecise.

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Improving Document Classification with Multi-Sense Embeddings

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Improving Document Classification with Multi-Sense Embeddings

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Algorithms for drone-assisted mission planning problems

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