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

Gal Dalal, Assaf Hallak, Gugan Thoppe, Shie Mannor, Gal Chechik

Introducing SoftTreeMax: a generalization of softmax that takes planning into account to reduce large variance & high sample complexity of policy gradient methods. On Atari, it shows up to 5x better performance & faster run time than PPO! #AI #ML #RL

Full paper at 👉 SoftTreeMax: Exponential Variance Reduction in Policy Gradient via Tree Search

Christian Raymond, Qi Chen, Bing Xue, Mengjie Zhang

Loss function learning is a new meta-learning paradigm to automate ML model design. Existing techniques have promising results, but our new technique adaptively updates the loss function online after each update, outperforming existing techniques on a variety of NN architectures and datasets. #ML #DataScience

Full paper at 👉 Online Loss Function Learning

Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, Dhruv Batra

Surprise! AI agents build "mental" maps to navigate PointGoal tasks, even when their perceptual system is limited and no inductive bias exists. Our research shows they can be ~95% successful, access ~1000 steps of memory, and build task-dependent maps #AI #Navigation #Research

Full paper at 👉 Emergence of Maps in the Memories of Blind Navigation Agents

Karthik Reddy Kanjula, Sai Meghana Kolla

Develop a distributed application to facilitate understanding & application of swarm intelligence in solving optimization problems. Leverage Ray distributed computing to support multiple users & offer a flexible & scalable solution. #swarmintelligence #optimization #distributedcomputing

Full paper at 👉 Distributed Swarm Intelligence

Batuhan Altundas, Zheyuan Wang, Joshua Bishop, Matthew Gombolay

Introducing HybridNet: a deep learning-based framework combining heterogeneous graph-based encoders & recurrent schedule propagators for efficient & intuitive human-robot team coordination. Outperforms existing solutions for deterministic & stochastic human performance! #AI #Robotics #Scheduling

Full paper at 👉 Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation

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