Sahar Asadi
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View article: Identification of Lung Cancer Metabolomics Profile and Molecular Interactions Using Bioinformatic Methods
Identification of Lung Cancer Metabolomics Profile and Molecular Interactions Using Bioinformatic Methods Open
Lung cancer remains a major public health concern and a leading cause of cancer-re-lated deaths worldwide. Despite its prevalence, existing diagnostic approaches for earlydetection face significant challenges, including limited clinical re…
View article: Novel computer-aided systems for interpreting immu- nohistochemistry (IHC) results in breast cancer based on deep learning algorithms: A systematic review
Novel computer-aided systems for interpreting immu- nohistochemistry (IHC) results in breast cancer based on deep learning algorithms: A systematic review Open
Breast cancer is a prevalent disease worldwide and the accurate diagnosis and prog-nosis of breast cancer are essential for the development of effective treatment plans.Pathology remains the gold standard for diagnosis and prognosis but wi…
View article: Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review Open
Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning expressive representations. While most research has center…
View article: SCORE: Skill-Conditioned Online Reinforcement Learning
SCORE: Skill-Conditioned Online Reinforcement Learning Open
Solving complex long-horizon tasks through Reinforcement Learning (RL) from scratch presents challenges related to efficient exploration. Two common approaches to reduce complexity and enhance exploration efficiency are (i) integrating lea…
View article: Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study Open
This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile game, transforming raw event data into textual sequences and …
View article: player2vec: A Language Modeling Approach to Understand Player Behavior in Games
player2vec: A Language Modeling Approach to Understand Player Behavior in Games Open
Methods for learning latent user representations from historical behavior logs have gained traction for recommendation tasks in e-commerce, content streaming, and other settings. However, this area still remains relatively underexplored in…
View article: Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning Open
Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super Ma…
View article: Capturing Local and Global Patterns in Procedural Content Generation via\n Machine Learning
Capturing Local and Global Patterns in Procedural Content Generation via\n Machine Learning Open
Recent procedural content generation via machine learning (PCGML) methods\nallow learning from existing content to produce similar content automatically.\nWhile these approaches are able to generate content for different games (e.g.\nSuper…
View article: Simple, Scalable, and Stable Variational Deep Clustering
Simple, Scalable, and Stable Variational Deep Clustering Open
Deep clustering (DC) has become the state-of-the-art for unsupervised clustering. In principle, DC represents a variety of unsupervised methods that jointly learn the underlying clusters and the latent representation directly from unstruct…
View article: Towards Dense Air Quality Monitoring : Time-Dependent Statistical Gas Distribution Modelling and Sensor Planning
Towards Dense Air Quality Monitoring : Time-Dependent Statistical Gas Distribution Modelling and Sensor Planning Open
This thesis addresses the problem of gas distribution modelling for gas monitoring and gas detection. The presented research is particularly focused on the methods that are suitable for uncontrolled environments. In such environments, gas …