Harpreet Singh's Journal Club session
Tag: machine learning
miRe2e: A Full End-to-End Deep Model Based on Transformers for Prediction of Pre-miRNAs
Manal Helal's Journal Club session where she will talk about a paper "miRe2e: A Full End-to-End Deep Model Based on Transformers for Prediction of Pre-miRNAs"
Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review
Mohammad Tayarani-Najaran's Journal Club session where he will talk about a paper "Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review"
Explanation in Human-AI Systems
Epaminondas Kapetanios's Journal Club session where he will talk about his editorial work in the "Explanation in Human-AI Systems" journal.
Uncovering the Topology of Time-Varying fMRI Data Using Cubical Persistence
Emil Dmitruk's Journal Club session where he will talk about a paper "Uncovering the Topology of Time-Varying fMRI Data Using Cubical Persistence"
Evolving the Olfactory System with Machine Learning
Nik Dennler's Journal Club session where he will talk about a paper "Evolving the Olfactory System with Machine Learning"
Towards Explainable Artificial Intelligence
Muhammad Yaqoob's journal club session where he will talk about the paper "Towards Explainable Artificial Intelligence".
OPTICS: Ordering Points To Identify the Clustering Structure
Na Helian's journal club session, where she will present the paper "OPTICS Ordering Points To Identify the Clustering Structure (Mihael Ankerst et al, 1999)".
Tagged as : machine learningThe Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes using Learning Analytics Techniques
Edward Wakelam's journal club session, where he will present his work.
Tagged as : machine learningThe power of deep networks and learning
Shabnam Kadir's journal club session, where she will present the papers "The power of deeper networks for expressing natural functions (David Rolnick and Max Tegmark, 2018)" and "Why does deep and cheap learning work so well? (Henry W. Lin, Max Tegmark and David Rolnick, 2017)".
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