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An end-to-end recurrent compressed sensing method to denoise, detect and demix calcium imaging data Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-09-19 Kangning Zhang, Sean Tang, Vivian Zhu, Majd Barchini, Weijian Yang
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Sparse learned kernels for interpretable and efficient medical time series processing Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-09-18 Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin
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Pre-training with fractional denoising to enhance molecular property prediction Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-09-18 Yuyan Ni, Shikun Feng, Xin Hong, Yuancheng Sun, Wei-Ying Ma, Zhi-Ming Ma, Qiwei Ye, Yanyan Lan
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Realizing full-body control of humanoid robots Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-09-11 Guangliang Li, Randy Gomez
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Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-09-09 Pushpak Pati, Sofia Karkampouna, Francesco Bonollo, Eva Compérat, Martina Radić, Martin Spahn, Adriano Martinelli, Martin Wartenberg, Marianna Kruithof-de Julio, Marianna Rapsomaniki
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Efficient and scalable reinforcement learning for large-scale network control Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-09-03 Chengdong Ma, Aming Li, Yali Du, Hao Dong, Yaodong Yang
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What is in your LLM-based framework? Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-30
To maintain high standards in clarity and reproducibility, authors need to clearly mention and describe the use of GPT-4 and other large language models in their work.
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A step forward in tracing and documenting dataset provenance Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-30 Nicholas Vincent
Training data are crucial for advancements in artificial intelligence, but many questions remain regarding the provenance of training datasets, license enforcement and creator consent. Mahari et al. provide a set of tools for tracing, documenting and sharing AI training data and highlight the importance for developers to engage with metadata of datasets.
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A large-scale audit of dataset licensing and attribution in AI Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-30 Shayne Longpre, Robert Mahari, Anthony Chen, Naana Obeng-Marnu, Damien Sileo, William Brannon, Niklas Muennighoff, Nathan Khazam, Jad Kabbara, Kartik Perisetla, Xinyi (Alexis) Wu, Enrico Shippole, Kurt Bollacker, Tongshuang Wu, Luis Villa, Sandy Pentland, Sara Hooker
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Learning integral operators via neural integral equations Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-29 Emanuele Zappala, Antonio Henrique de Oliveira Fonseca, Josue Ortega Caro, Andrew Henry Moberly, Michael James Higley, Jessica Cardin, David van Dijk
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A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-27 Davide Carnevali, Limei Zhong, Esther González-Almela, Carlotta Viana, Mikhail Rotkevich, Aiping Wang, Daniel Franco-Barranco, Aitor Gonzalez-Marfil, Maria Victoria Neguembor, Alvaro Castells-Garcia, Ignacio Arganda-Carreras, Maria Pia Cosma
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Learning motif-based graphs for drug–drug interaction prediction via local–global self-attention Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-27 Yi Zhong, Gaozheng Li, Ji Yang, Houbing Zheng, Yongqiang Yu, Jiheng Zhang, Heng Luo, Biao Wang, Zuquan Weng
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Factuality challenges in the era of large language models and opportunities for fact-checking Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-22 Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, Eduard Hovy, Heng Ji, Filippo Menczer, Ruben Miguez, Preslav Nakov, Dietram Scheufele, Shivam Sharma, Giovanni Zagni
The emergence of tools based on large language models (LLMs), such as OpenAI’s ChatGPT and Google’s Gemini, has garnered immense public attention owing to their advanced natural language generation capabilities. These remarkably natural-sounding tools have the potential to be highly useful for various tasks. However, they also tend to produce false, erroneous or misleading content—commonly referred
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A bioactivity foundation model using pairwise meta-learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-14 Bin Feng, Zequn Liu, Nanlan Huang, Zhiping Xiao, Haomiao Zhang, Srbuhi Mirzoyan, Hanwen Xu, Jiaran Hao, Yinghui Xu, Ming Zhang, Sheng Wang
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On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-12 Surbhi Mittal, Kartik Thakral, Richa Singh, Mayank Vatsa, Tamar Glaser, Cristian Canton Ferrer, Tal Hassner
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Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-09 Mark Endo, Favour Nerrise, Qingyu Zhao, Edith V. Sullivan, Li Fei-Fei, Victor W. Henderson, Kilian M. Pohl, Kathleen L. Poston, Ehsan Adeli
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Cognitive maps from predictive vision Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-08 Margaret C. von Ebers, Xue-Xin Wei
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Integrated structure prediction of protein–protein docking with experimental restraints using ColabDock Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-08-05 Shihao Feng, Zhenyu Chen, Chengwei Zhang, Yuhao Xie, Sergey Ovchinnikov, Yi Qin Gao, Sirui Liu
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Deep learning prediction of glycopeptide tandem mass spectra powers glycoproteomics Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-30 Yu Zong, Yuxin Wang, Xipeng Qiu, Xuanjing Huang, Liang Qiao
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Advanced AI assistants that act on our behalf may not be ethically or legally feasible Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-29 Silvia Milano, Sven Nyholm
Google and OpenAI have recently announced major product launches involving artificial intelligence (AI) agents based on large language models (LLMs) and other generative models. Notably, these are envisioned to function as personalized ‘advanced assistants’. With other companies following suit, such AI agents seem poised to be the next big thing in consumer technology, with the potential to disrupt
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A question of trust for AI research in medicine Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-24
Medical research is one of the most impactful areas for machine learning applications, but access to large and diverse health datasets is needed for models to be useful. Winning trust from patients by demonstrating that data are handled securely and effectively is key.
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DNA language model GROVER learns sequence context in the human genome Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-23 Melissa Sanabria, Jonas Hirsch, Pierre M. Joubert, Anna R. Poetsch
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Partial-convolution-implemented generative adversarial network for global oceanic data assimilation Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-22 Yoo-Geun Ham, Yong-Sik Joo, Jeong-Hwan Kim, Jeong-Gil Lee
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Automated construction of cognitive maps with visual predictive coding Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-18 James Gornet, Matt Thomson
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A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-18 Rui Jiang, Xiaoxu Yin, Pengshuai Yang, Lingchao Cheng, Juan Hu, Jiao Yang, Ying Wang, Xiaodan Fu, Li Shang, Liling Li, Wei Lin, Huan Zhou, Fufeng Chen, Xuegong Zhang, Zhongliang Hu, Hairong Lv
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The need for reproducible research in soft robotics Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-17 Robert Baines, Dylan Shah, Jeremy Marvel, Jennifer Case, Andrew Spielberg
Recent years have witnessed the rise of commercialization efforts for soft robotics technology, which includes soft grippers, stretchable sensors and platforms for human–robot interactions. However, this commercialization lags behind the trends seen with other robotics technologies at equivalent points in their respective lifecycles. For example, the first patent for an industrial robotic manipulator
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Realistic morphology-preserving generative modelling of the brain Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-15 Petru-Daniel Tudosiu, Walter H. L. Pinaya, Pedro Ferreira Da Costa, Jessica Dafflon, Ashay Patel, Pedro Borges, Virginia Fernandez, Mark S. Graham, Robert J. Gray, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
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High-resolution real-space reconstruction of cryo-EM structures using a neural field network Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-12 Yue Huang, Chengguang Zhu, Xiaokang Yang, Manhua Liu
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Unsupervised learning of topological non-Abelian braiding in non-Hermitian bands Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-12 Yang Long, Haoran Xue, Baile Zhang
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Shielding sensitive medical imaging data Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-11 Gaoyang Liu, Chen Wang, Tian Xia
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An interpretable deep learning framework for genome-informed precision oncology Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-11 Shuangxia Ren, Gregory F. Cooper, Lujia Chen, Xinghua Lu
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Lifelike agility and play in quadrupedal robots using reinforcement learning and generative pre-trained models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-05 Lei Han, Qingxu Zhu, Jiapeng Sheng, Chong Zhang, Tingguang Li, Yizheng Zhang, He Zhang, Yuzhen Liu, Cheng Zhou, Rui Zhao, Jie Li, Yufeng Zhang, Rui Wang, Wanchao Chi, Xiong Li, Yonghui Zhu, Lingzhu Xiang, Xiao Teng, Zhengyou Zhang
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Molecular set representation learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-07-05 Maria Boulougouri, Pierre Vandergheynst, Daniel Probst
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Will generative AI transform robotics? Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27
In the current wave of excitement about applying large vision–language models and generative AI to robotics, expectations are running high, but conquering real-world complexities remains challenging for robots.
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Machine learning for micro- and nanorobots Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Lidong Yang, Jialin Jiang, Fengtong Ji, Yangmin Li, Kai-Leung Yung, Antoine Ferreira, Li Zhang
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Neuromorphic visual scene understanding with resonator networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Bruno A. Olshausen, Yulia Sandamirskaya, Friedrich T. Sommer, E. Paxon Frady
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Visual odometry with neuromorphic resonator networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, E. Paxon Frady, Friedrich T. Sommer, Yulia Sandamirskaya
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Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-27 Osama Abdin, Philip M. Kim
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Laplace neural operator for solving differential equations Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Qianying Cao, Somdatta Goswami, George Em Karniadakis
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Challenges, evaluation and opportunities for open-world learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Mayank Kejriwal, Eric Kildebeck, Robert Steininger, Abhinav Shrivastava
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Coordinate-based neural representations for computational adaptive optics in widefield microscopy Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-24 Iksung Kang, Qinrong Zhang, Stella X. Yu, Na Ji
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Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Evan E. Seitz, David M. McCandlish, Justin B. Kinney, Peter K. Koo
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Systematic analysis of 32,111 AI model cards characterizes documentation practice in AI Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Weixin Liang, Nazneen Rajani, Xinyu Yang, Ezinwanne Ozoani, Eric Wu, Yiqun Chen, Daniel Scott Smith, James Zou
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Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Dong Chen, Jian Liu, Guo-Wei Wei
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Reconciling privacy and accuracy in AI for medical imaging Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-21 Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard F. Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis
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Machine learning-aided generative molecular design Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-18 Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell
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Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-17 Huan Yee Koh, Anh T. N. Nguyen, Shirui Pan, Lauren T. May, Geoffrey I. Webb
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Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-14 Chenguang Li, Gabriel Kreiman, Sharad Ramanathan
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Generic protein–ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-06 Duanhua Cao, Geng Chen, Jiaxin Jiang, Jie Yu, Runze Zhang, Mingan Chen, Wei Zhang, Lifan Chen, Feisheng Zhong, Yingying Zhang, Chenghao Lu, Xutong Li, Xiaomin Luo, Sulin Zhang, Mingyue Zheng
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Learning efficient backprojections across cortical hierarchies in real time Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-06-06 Kevin Max, Laura Kriener, Garibaldi Pineda García, Thomas Nowotny, Ismael Jaras, Walter Senn, Mihai A. Petrovici
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Distributed constrained combinatorial optimization leveraging hypergraph neural networks Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-30 Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad, Farinaz Koushanfar
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Empathic AI can’t get under the skin Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-24
Personalized LLMs built with the capacity for emulating empathy are right around the corner. The effects on individual users needs careful consideration.
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Accurate and robust protein sequence design with CarbonDesign Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-23 Milong Ren, Chungong Yu, Dongbo Bu, Haicang Zhang
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Quantum circuit synthesis with diffusion models Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-20 Florian Fürrutter, Gorka Muñoz-Gil, Hans J. Briegel
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Back to basics to open the black box Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-17 Diego Marcondes, Adilson Simonis, Junior Barrera
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Efficient learning of accurate surrogates for simulations of complex systems Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-17 A. Diaw, M. McKerns, I. Sagert, L. G. Stanton, M. S. Murillo
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Does it matter if empathic AI has no empathy? Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-15 Garriy Shteynberg, Jodi Halpern, Amir Sadovnik, Jon Garthoff, Anat Perry, Jessica Hay, Carlos Montemayor, Michael A. Olson, Tim L. Hulsey, Abrol Fairweather
Imagine a machine that provides a simulation of any experience a person might want, but once the machine is activated, the person is unable to tell that the experience isn’t real. When Robert Nozick formulated this thought experiment in 1974 (ref. 1), it was meant to be obvious that people in otherwise ordinary circumstances would be making a horrible mistake if they hooked themselves up to such a
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Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-13 Ning Wang, Jiang Bian, Yuchen Li, Xuhong Li, Shahid Mumtaz, Linghe Kong, Haoyi Xiong
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Diving into deep learning Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-10 Ge Wang
Understanding Deep Learning Simon J. D. PrinceThe MIT Press: 2023. 544 pp. $90.00 The field of artificial intelligence (AI) has experienced a surge in developments over the past years, propelled by breakthroughs in deep learning with neural networks. This has revolutionized many aspects of society. However, the speed at which AI is advancing highlights the need for textbooks that provide essential
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Augmenting large language models with chemistry tools Nat. Mach. Intell. (IF 18.8) Pub Date : 2024-05-08 Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D. White, Philippe Schwaller