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- Explainability (3)
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- Recommender systems (3)
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- User cold start problem (2)
- AI Safety (1)
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- Hybrid recommender system (1)
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- Music recommender system (1)
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- Value Alignment Problem (1)
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Articles 1 - 7 of 7
Full-Text Articles in Computer Engineering
Seer: An Explainable Deep Learning Midi-Based Hybrid Song Recommender System, Khalil Damak, Olfa Nasraoui
Seer: An Explainable Deep Learning Midi-Based Hybrid Song Recommender System, Khalil Damak, Olfa Nasraoui
Faculty Scholarship
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning …
Formally Designing And Implementing Cyber Security Mechanisms In Industrial Control Networks., Mehdi Sabraoui
Formally Designing And Implementing Cyber Security Mechanisms In Industrial Control Networks., Mehdi Sabraoui
Electronic Theses and Dissertations
This dissertation describes progress in the state-of-the-art for developing and deploying formally verified cyber security devices in industrial control networks. It begins by detailing the unique struggles that are faced in industrial control networks and why concepts and technologies developed for securing traditional networks might not be appropriate. It uses these unique struggles and examples of contemporary cyber-attacks targeting control systems to argue that progress in securing control systems is best met with formal verification of systems, their specifications, and their security properties. This dissertation then presents a development process and identifies two technologies, TLA+ and seL4, that can be …
Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders
Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders
Faculty Scholarship
Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of …
An Explainable Sequence-Based Deep Learning Predictor With Applications To Song Recommendation And Text Classification., Khalil Damak
An Explainable Sequence-Based Deep Learning Predictor With Applications To Song Recommendation And Text Classification., Khalil Damak
Electronic Theses and Dissertations
Streaming applications are now the predominant tools for listening to music. What makes the success of such software is the availability of songs and especially their ability to provide users with relevant personalized recommendations. State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction (listening to a song) using a memory-based deep learning structure that learns from temporal sequences of user actions. Despite advances in deep learning models for song recommendation systems, none has taken …
Debiasing The Human-Recommender System Feedback Loop In Collaborative Filtering, Wenlong Sun, Sami Khenissi, Olfa Nasraoui, Patrick Shafto
Debiasing The Human-Recommender System Feedback Loop In Collaborative Filtering, Wenlong Sun, Sami Khenissi, Olfa Nasraoui, Patrick Shafto
Faculty Scholarship
Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants,etc. Because a traditional recommendation strategy always shows the most relevant items (thus with highest predicted rating), traditional RS’s are expected to make popular items become even more popular and non-popular items become even less popular which in turn further divides the haves (popular) from the have-nots (un-popular). Therefore, a major problem with RSs is that they may introduce biases affecting the exposure of items, thus creating a popularity divide of items during the feedback loop that occurs with users, and this may …
An Explainable Autoencoder For Collaborative Filtering Recommendation, Pegah Sagheb Haghighi, Olurotimi Seton, Olfa Nasraoui
An Explainable Autoencoder For Collaborative Filtering Recommendation, Pegah Sagheb Haghighi, Olurotimi Seton, Olfa Nasraoui
Faculty Scholarship
Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately, like all black box machine learning models, they are unable to explain their outputs. Hence, while predictions from an Autoencoderbased recommender system might be accurate, it might not be clear to the user why a recommendation was generated. In this work, we design an explainable recommendation system using an Autoencoder model whose predictions can be explained using the neighborhood based explanation style. Our preliminary work …
Personal Universes: A Solution To The Multi-Agent Value Alignment Problem, Roman V. Yampolskiy
Personal Universes: A Solution To The Multi-Agent Value Alignment Problem, Roman V. Yampolskiy
Faculty Scholarship
AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of each user. We conclude by analyzing benefits and limitations of the proposed approach.