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Nature Machine Intelligence 2024: all accepted papers

178 papers · page 2 of 2 · the most cited first · ← Nature Machine Intelligence 2024

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  1. Leveraging ancestral sequence reconstruction for protein representation learning ↗Dana S. Matthews, Matthew A. Spence, Adam C. Mater et al.20 citations
  2. Nanobody–antigen interaction prediction with ensemble deep learning and prompt-based protein language models ↗Juntao Deng, Miao Gu, Pengyan Zhang et al.20 citations
  3. Unsupervised learning of topological non-Abelian braiding in non-Hermitian bands ↗Yang Long, Haoran Xue, Baile Zhang20 citations
  4. Zero-shot transfer of protein sequence likelihood models to thermostability prediction ↗Shawn Reeves, Subha Kalyaanamoorthy19 citations
  5. Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics ↗Milena Pavlović, Ghadi S. Al Hajj, Chakravarthi Kanduri et al.17 citations
  6. Sparse learned kernels for interpretable and efficient medical time series processing ↗Sully F. Chen, Zhicheng Guo, Cheng Ding et al.17 citations
  7. Discussions of machine versus living intelligence need more clarity ↗Nicolas Rouleau, Michael G. Levin16 citations
  8. Learning efficient backprojections across cortical hierarchies in real time ↗Kevin Max, Laura Kriener, Garibaldi Pineda García et al.16 citations
  9. Mitigating allocative tradeoffs and harms in an environmental justice data tool ↗Benjamin Q. Huynh, Elizabeth T. Chin, Allison Koenecke et al.16 citations
  10. Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks ↗Chenguang Li, Gabriel Kreiman, Sharad Ramanathan14 citations
  11. The curious case of the test set AUROC ↗Michael T. Roberts, Alon Hazan, Sören Dittmer et al.14 citations
  12. The democratization of global AI governance and the role of tech companies ↗Eva Erman, Markus Furendal14 citations
  13. Variational autoencoder for design of synthetic viral vector serotypes ↗Suyue Lyu, Shahin Sowlati‐Hashjin, Michael Garton14 citations
  14. Efficient rare event sampling with unsupervised normalizing flows ↗Solomon Asghar, Qing‐Xiang Pei, Giorgio Volpe et al.13 citations
  15. The new NeuroAI ↗13 citations
  16. Anniversary AI reflections ↗Noelia Ferruz, Marinka Žitnik, Pierre‐Yves Oudeyer et al.12 citations
  17. Connecting molecular properties with plain language ↗Glen M. Hocky12 citations
  18. The need for reproducible research in soft robotics ↗Robert L. Baines, Dylan Shah, Jeremy A. Marvel et al.12 citations
  19. Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases ↗Mark Endo, Favour Nerrise, Qingyu Zhao et al.11 citations
  20. Deep learning prediction of ribosome profiling with Translatomer reveals translational regulation and interprets disease variants ↗Jialin He, Lei Xiong, Shaohui Shi et al.11 citations
  21. Engineering flexible machine learning systems by traversing functionally invariant paths ↗Guruprasad Raghavan, Bahey Tharwat, Surya N. Hari et al.11 citations
  22. Modulating emotional states of rats through a rat-like robot with learned interaction patterns ↗Guanglu Jia, Zhe Chen, Yulai Zhang et al.11 citations
  23. Partial-convolution-implemented generative adversarial network for global oceanic data assimilation ↗Yoo‐Geun Ham, Yong-Sik Joo, Jeong-Hwan Kim et al.11 citations
  24. Reusability report: Uncovering associations in biomedical bipartite networks via a bilinear attention network with domain adaptation ↗Tao Xu, Haoyuan Shi, Wanling Gao et al.11 citations
  25. Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design ↗Ruijiang Li, Jiang Lu, Ziyi Liu et al.11 citations
  26. The synergy complement control approach for seamless limb-driven prostheses ↗Johannes Kühn, Tingli Hu, Alexander Toedtheide et al.11 citations
  27. Visual odometry with neuromorphic resonator networks ↗Alpha Renner, Lazar Supic, Andreea Danielescu et al.10 citations
  28. A multi-modal deep language model for contaminant removal from metagenome-assembled genomes ↗Bohao Zou, Jingjing Wang, Yi Ding et al.9 citations
  29. AI in biomaterials discovery: generating self-assembling peptides with resource-efficient deep learning ↗Tianang Leng, César de la Fuente‐Núñez9 citations
  30. Empathic AI can’t get under the skin ↗9 citations
  31. Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning ↗Tian Lan, Shuquan Su, Pengyao Ping et al.9 citations
  32. Is it five already? ↗9 citations
  33. Realizing full-body control of humanoid robots ↗Guangliang Li, Randy Gómez9 citations
  34. AI pioneers win 2024 Nobel prizes ↗8 citations
  35. Estimation of causal effects of genes on complex traits using a Bayesian-network-based framework applied to GWAS data ↗Liangying Yin, Yaning Feng, Yujia Shi et al.8 citations
  36. High-resolution real-space reconstruction of cryo-EM structures using a neural field network ↗Yue Hui Huang, Chengguang Zhu, Xiaokang Yang et al.8 citations
  37. Successful implementation of the EU AI Act requires interdisciplinary efforts ↗Christian Montag, Michèle Finck8 citations
  38. Will generative AI transform robotics? ↗8 citations
  39. An interpretable deep learning framework for genome-informed precision oncology ↗Shuangxia Ren, Gregory F. Cooper, Lujia Chen et al.7 citations
  40. Diving into deep learning ↗Ge Wang7 citations
  41. Foundation models in healthcare require rethinking reliability ↗Thomas Grote, Timo Freiesleben, Philipp Berens7 citations
  42. Limitations in odour recognition and generalization in a neuromorphic olfactory circuit ↗Nik Dennler, van Schaik André, Michael Schmuker7 citations
  43. What comparing deep neural networks can teach us about human vision ↗Katja Seeliger, Martin N. Hebart7 citations
  44. What is in your LLM-based framework? ↗7 citations
  45. Back to basics to open the black box ↗Diego Marcondes, Adilson Simonis, Júnior Barrera6 citations
  46. Fast and generalizable micromagnetic simulation with deep neural nets ↗Yunqi Cai, Jiangnan Li, Dong Wang6 citations
  47. Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization ↗Simone Ciceri, Lorenzo Cassani, Matteo Osella et al.6 citations
  48. Reusability report: Unpaired deep-learning approaches for holographic image reconstruction ↗Yuhe Zhang, Tobias Ritschel, Pablo Villanueva‐Perez6 citations
  49. Sequential memory improves sample and memory efficiency in episodic control ↗Ismael T. Freire, Adrián F. Amil, Paul F. M. J. Verschure6 citations
  50. Envisioning better benchmarks for machine learning PDE solvers ↗J. Brandstetter5 citations
  51. Lessons from a challenge on forecasting epileptic seizures from non-cerebral signals ↗Kenny Schlegel, Denis V. Kleyko, Benjamin H. Brinkmann et al.5 citations
  52. Self-decoupling three-axis forces in a simple sensor ↗Kuanming Yao, Qiuna Zhuang5 citations
  53. A plea for caution and guidance about using AI in genomics ↗Mohammad Hosseini, Christopher Donohue4 citations
  54. ARNLE model identifies prevalence potential of SARS-CoV-2 variants ↗Yuqi Liu, Jing Li, Peihan Li et al.4 citations
  55. Advanced AI assistants that act on our behalf may not be ethically or legally feasible ↗Silvia Milano, Sven Nyholm4 citations
  56. Artificial intelligence tackles the nature–nurture debate ↗Justin N. Wood4 citations
  57. Deep learning at the forefront of detecting tipping points ↗Smita Deb, Partha Sharathi Dutta4 citations
  58. Kernel approximation using analogue in-memory computing ↗Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos et al.4 citations
  59. Reusability report: Annotating metabolite mass spectra with domain-inspired chemical formula transformers ↗Janne Heirman, Wout Bittremieux4 citations
  60. Reusability report: Leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data ↗Yingying Cao, Tiangen Chang, Sahil Sahni et al.4 citations
  61. The rewards of reusable machine learning code ↗4 citations
  62. Dangers of speech technology for workplace diversity ↗Mike Horia Teodorescu, Mingang K. Geiger, Lily Morse3 citations
  63. Towards a personalized AI assistant to learn machine learning ↗Pascal Wallisch, Ibrahim Sheikh3 citations
  64. AI protein shake-up ↗2 citations
  65. An end-to-end recurrent compressed sensing method to denoise, detect and demix calcium imaging data ↗Kangning Zhang, Sean Tang, Vivian Zhu et al.2 citations
  66. Cognitive maps from predictive vision ↗Margaret von Ebers, Xue-Xin Wei2 citations
  67. Pick your AI poison ↗2 citations
  68. Reply to: Deeper evaluation of a single-cell foundation model ↗Fan Yang, Fang Wang, Long-Kai Huang et al.2 citations
  69. Strategies needed to counter potential AI misuse ↗2 citations
  70. A question of trust for AI research in medicine ↗1 citation
  71. A step forward in tracing and documenting dataset provenance ↗Nicholas Vincent1 citation
  72. Guidelines for study protocols describing predefined validations of prediction models in medical deep learning and beyond ↗Andreas Kleppe, Ole-Johan Skrede, Knut Liestøl et al.1 citation
  73. Reply to: Limitations in odour recognition and generalization in a neuromorphic olfactory circuit ↗Roy Moyal, Nabil Imam, Thomas A. Cleland1 citation
  74. Seeking clarity rather than strong opinions on intelligence ↗1 citation
  75. Shielding sensitive medical imaging data ↗Gaoyang Liu, Chen Wang, Tian Xia1 citation
  76. A multiscale approach for biomedical machine learning ↗
  77. Clinical large language models with misplaced focus ↗Zining Luo, Haowei Ma, Zhiwu Li et al.
  78. Memetic robots ↗Thomas Schmickl

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