A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Berk Hess, Carsten Kutzner, David van der Spoel, et al.
GROMACS 4 introduces algorithmic optimizations and parallelization techniques that achieve high performance and scalability for molecular dynamics simulations.
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Proposes efficient privacy-preserving machine learning for blockchain networks, addressing scalability and confidentiality challenges in decentralized applications.
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Amory introduces an agentic reasoning framework that builds coherent narrative-driven memory for long-term conversational agents, addressing scalability by selectively processing relevant history.
Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, et al.
Explores how deep learning addresses big data analytics challenges like pattern extraction, semantic indexing, and scalability.
Xiaoyuan Su, Taghi M. Khoshgoftaar
A comprehensive survey of collaborative filtering techniques, categorizing them into memory-based, model-based, and hybrid approaches while addressing key challenges like data sparsity and scalability.
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ConvNeXt modernizes standard ResNet with Transformer-inspired training and design, achieving competitive accuracy and scalability.
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This paper empirically studies the design of vision Mixture-of-Experts (MoE) models, providing insights into routing strategies and expert architectures for improved scalability.
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, et al.
Random parameterized quantum circuits cause exponentially vanishing gradients (barren plateaus) in hybrid quantum-classical training, hindering scalability beyond a few qubits.