ScholarStream.ai

Welcome to the world of academic insights!

Are you tired of the shallow and attention-seeking content on social media? Are you looking for a deeper understanding of the world around you? Look no further!

ScholarStream.ai brings you the latest research from the world of academia, all in a convenient and engaging Twitter-like feed.

With ScholarStream.ai, you can dive into the latest findings from top researchers and scholars, all at the touch of a button. No more sifting through tedious journals or abstracts – our AI does the work for you, presenting you with the latest information in an easily digestible format.

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PS: On ScholarStream.ai, the content you see is generated by AI, but the academic papers and researchers it is based on are real and sourced from the well-respected preprint server arXiv.

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Astrophysics Condensed matter General Relativity and Quantum Cosmology Mathematical Physics Nuclear Experiment Nuclear Theory Physics Quantum Physics Nonlinear Sciences Mathematics Computer Science Quantitative Biology Quantitative Finance Statistics Electrical Engineering and Systems Science Economics

Latest cs.AI papers

Johannes Schneider, Michalis Vlachos

#DeepLearning has made huge strides in the last decade! From attention & normalization to skip connection & self-supervised learning, we provide an overview of the most influential & recent works. #ConnectingTheDots #DataScience #AI

Full paper at 👉 A Survey of Deep Learning: From Activations to Transformers

Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili

Discover how to derive sample complexities for kernel based #Qlearning when a generative model exists. We propose a nonparametric Q-learning algorithm for an arbitrarily large scale discounted MDP with order optimal sample complexity! #ReinforcementLearning #MachineLearning #AI

Full paper at 👉 Sample Complexity of Kernel-Based Q-Learning

Nicholas Matsumoto, Anil Kumar Saini, Pedro Ribeiro, Hyunjun Choi, Alena Orlenko, Leo-Pekka Lyytikäinen, Jari O Laurikka, Terho Lehtimäki, Sandra Batista, Jason H. Moore

#Lexicase selection leads to faster convergence in TPOT automated ML system, compared to NSGA-II. We explore search space using trie data structure to compare these methods! #ML #DataScience #Automation

Full paper at 👉 Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning

Theodor Westny, Joel Oskarsson, Björn Olofsson, Erik Frisk

Introducing MTP-GO: a model that uses temporal graph neural networks & neural ODEs to generate robust predictions of road users' future behavior. Results show it outperforms existing methods & provides multi-modal probabilistic predictions. #MotionPlanning #AutonomousMotion

Full paper at 👉 MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

Maryam Taeb

Train future software developers to write secure source code with our proposed learning modules & hands on labs! Leverage ML & NLP to predict & identify vulnerabilities & improve student skills & awareness on source code vulnerabilities detection & mitigation. #SecureCodingEducation

Full paper at 👉 Developing Hands-on Labs for Source Code Vulnerability Detection with AI

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