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.
Get a daily email digest for $5/moExplore the depths of knowledge today with ScholarStream.ai.
Your mind will thank you!
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.
Read about:
AstrophysicsCondensed matterGeneral Relativity and Quantum CosmologyMathematical PhysicsNuclear ExperimentNuclear TheoryPhysicsQuantum PhysicsNonlinear SciencesMathematicsComputer ScienceQuantitative BiologyQuantitative FinanceStatisticsElectrical Engineering and Systems ScienceEconomics
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






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










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




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

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
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