Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains.
2018
Earlier work this paper cites.
Z. Liu, R. J. Crouser, and A. Ottley, “Survey on individual differences in visualization,” in Computer Graphics Forum, 39: 693-712. doi:10.1111/cgf.14033 , 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
P. Zhang, X. Li, X. Hu, J. Yang, L. Zhang, L. Wang, Y. Choi, and J. Gao, “Vinvl: Revisiting visual representations in vision-language models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 5579–5588
2021
Earlier work this paper cites.
E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn, “BC-z: Zero-shot task generalization with robotic imitation learning,” in 5th Annual Conference on Robot Learning , 2021. [Online]. Available: https://openreview.net/forum?id=8kbp23tSGYv
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
C. Batailler, A. Fernandez, J. Swan, E. Servien, F. S. Haddad, F. Catani, and S. Lustig, “Mako ct-based robotic arm-assisted system is a reliable procedure for total knee arthroplasty: a systematic review,” Knee Surgery, Sports Traumatology, Arthroscopy , vol. 29, pp. 3585–3598, 2021
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
Z. Liu, M. He, Z. Jiang, Z. Wu, H. Dai, L. Zhang, S. Luo, T. Han, X. Li, X. Jiang et al. , “Survey on natural language processing in medical image analysis.” Zhong nan da xue xue bao. Yi xue ban= Journal of Central South University. Medical Sciences , vol. 47, no. 8, pp. 981–993, 2022
2022
Earlier work this paper cites.
D. Rothman and A. Gulli, Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 . Packt Publishing Ltd, 2022
2022
Earlier work this paper cites.
S. Rezayi, H. Dai, Z. Liu, Z. Wu, A. Hebbar, A. H. Burns, L. Zhao, D. Zhu, Q. Li, W. Liu, S. Li, T. Liu, and X. Li, “Clinicalradiobert: Knowledge-infused few shot learning for clinical notes named entity recognition,” in Machine Learning in Medical Imaging , C. Lian, X. Cao, I. Rekik, X. Xu, and Z. Cui, Eds. Cham: Springer Nature Switzerland, 2022, pp. 269–278
2022
Earlier work this paper cites.
Z. Liu, M. He, Z. Jiang, Z. Wu, H. Dai, L. Zhang, S. Luo, T. Han, X. Li, X. Jiang, D. Zhu, X. Cai, B. Ge, W. Liu, J. Liu, D. Shen, and T. Liu, “Survey on natural language processing in medical image analysis,” Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences , vol. 47, no. 8, p. 981—993, August 2022. [Online]. Available: https://doi.org/10.11817/j.issn.1672-7347.2022.220376
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
L. Zhao, Z. Wu, H. Dai, Z. Liu, T. Zhang, D. Zhu, and T. Liu, “Embedding human brain function via transformer,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer Nature Switzerland Cham, 2022, pp. 366–375
2022
Earlier work this paper cites.
H. Dai, Q. Li, L. Zhao, L. Pan, C. Shi, Z. Liu, Z. Wu, L. Zhang, S. Zhao, X. Wu et al. , “Graph representation neural architecture search for optimal spatial/temporal functional brain network decomposition,” in International Workshop on Machine Learning in Medical Imaging . Springer Nature Switzerland Cham, 2022, pp. 279–287
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Y. Ding, Z. Liu, H. Feng, J. Holmes, Y. Yang, N. Yu, T. Sio, S. Schild, B. Li, and W. Liu, “Accurate and efficient deep neural network based deformable image registration method in lung cancer,” in MEDICAL PHYSICS , vol. 49, no. 6. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2022, pp. E148–E148
2022
Earlier work this paper cites.
K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Learning to prompt for vision-language models,” International Journal of Computer Vision , vol. 130, no. 9, pp. 2337–2348, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
S. Li, X. Puig, C. Paxton, Y. Du, C. Wang, L. Fan, T. Chen, D.-A. Huang, E. Akyürek, A. Anandkumar et al. , “Pre-trained language models for interactive decision-making,” Advances in Neural Information Processing Systems , vol. 35, pp. 31 199–31 212, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan, “Vima: General robot manipulation with multimodal prompts,” arXiv , 2022
2022
Earlier work this paper cites.
A. Tam, N. Rabinowitz, A. Lampinen, N. A. Roy, S. Chan, D. Strouse, J. Wang, A. Banino, and F. Hill, “Semantic exploration from language abstractions and pretrained representations,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 377–25 389, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
T. Silver, V. Hariprasad, R. S. Shuttleworth, N. Kumar, T. Lozano-Pérez, and L. P. Kaelbling, “Pddl planning with pretrained large language models,” in NeurIPS 2022 Foundation Models for Decision Making Workshop , 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
E. Rosete-Beas, O. Mees, G. Kalweit, J. Boedecker, and W. Burgard, “Latent plans for task agnostic offline reinforcement learning,” in Proceedings of the 6th Conference on Robot Learning (CoRL) , 2022
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
Y. Liu, T. Han, S. Ma, J. Zhang, Y. Yang, J. Tian, H. He, A. Li, M. He, Z. Liu et al. , “Summary of chatgpt-related research and perspective towards the future of large language models,” Meta-Radiology , p. 100017, 2023
2023
Earlier work this paper cites.
C. Zhou, Q. Li, C. Li, J. Yu, Y. Liu, G. Wang, K. Zhang, C. Ji, Q. Yan, L. He, H. Peng, J. Li, J. Wu, Z. Liu, P. Xie, C. Xiong, J. Pei, P. S. Yu, and L. Sun, “A comprehensive survey on pretrained foundation models: A history from bert to chatgpt,” 2023
2023
Earlier work this paper cites.
L. Zhao, L. Zhang, Z. Wu, Y. Chen, H. Dai, X. Yu, Z. Liu, T. Zhang, X. Hu, X. Jiang et al. , “When brain-inspired ai meets agi,” Meta-Radiology , p. 100005, 2023
2023
Earlier work this paper cites.
M. S. Rahaman, M. T. Ahsan, N. Anjum, H. J. R. Terano, and M. M. Rahman, “From chatgpt-3 to gpt-4: a significant advancement in ai-driven nlp tools,” Journal of Engineering and Emerging Technologies , vol. 2, no. 1, pp. 1–11, 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
W. Liao, Z. Liu, H. Dai, Z. Wu, Y. Zhang, X. Huang, Y. Chen, X. Jiang, W. Liu, D. Zhu, T. Liu, S. Li, X. Li, and H. Cai, “Mask-guided bert for few shot text classification,” 2023
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Liu, X. Yu, L. Zhang, Z. Wu, C. Cao, H. Dai, L. Zhao, W. Liu, D. Shen, Q. Li, T. Liu, D. Zhu, and X. Li, “Deid-gpt: Zero-shot medical text de-identification by gpt-4,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. A. Abdulsaheb and D. J. Kadhim, “Classical and heuristic approaches for mobile robot path planning: A survey,” Robotics , vol. 12, no. 4, p. 93, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Dai, Y. Li, Z. Liu, L. Zhao, Z. Wu, S. Song, Y. Shen, D. Zhu, X. Li, S. Li, X. Yao, L. Shi, Q. Li, Z. Chen, D. Zhang, G. Mai, and T. Liu, “Ad-autogpt: An autonomous gpt for alzheimer’s disease infodemiology,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
OpenAI, “Introducing ChatGPT — openai.com,” https://openai.com/blog/chatgpt , [Accessed 28-08-2023]
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2998–3009
2023
Later among the works it cites.
K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf, “Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning,” in Conference on Robot Learning . PMLR, 2023, pp. 23–72
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
N. Rane, “Transformers in industry 4.0, industry 5.0, and society 5.0: Roles and challenges,” 2023
2023
Later among the works it cites.
H. Qiao, Y.-X. Wu, S.-L. Zhong, P.-J. Yin, and J.-H. Chen, “Brain-inspired intelligent robotics: Theoretical analysis and systematic application,” Machine Intelligence Research , vol. 20, no. 1, pp. 1–18, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
N. Di Palo, A. Byravan, L. Hasenclever, M. Wulfmeier, N. Heess, and M. Riedmiller, “Towards a unified agent with foundation models,” in Workshop on Reincarnating Reinforcement Learning at ICLR 2023 , 2023
2023
Later among the works it cites.
A. Xie, Y. Lee, P. Abbeel, and S. James, “Language-conditioned path planning,” in Conference on Robot Learning . PMLR, 2023, pp. 3384–3396
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Bucker, L. Figueredo, S. Haddadin, A. Kapoor, S. Ma, S. Vemprala, and R. Bonatti, “Latte: Language trajectory transformer,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 7287–7294
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Z. Ren, B. Govil, T.-Y. Yang, K. R. Narasimhan, and A. Majumdar, “Leveraging language for accelerated learning of tool manipulation,” in Conference on Robot Learning . PMLR, 2023, pp. 1531–1541
2023
Later among the works it cites.
B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler, “Open-vocabulary queryable scene representations for real world planning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 509–11 522
2023
Later among the works it cites.
2023
Later among the works it cites.
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “Progprompt: Generating situated robot task plans using large language models,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 523–11 530
2023
Later among the works it cites.
L.-H. Lin, Y. Cui, Y. Hao, F. Xia, and D. Sadigh, “Gesture-informed robot assistance via foundation models,” in Conference on Robot Learning . PMLR, 2023, pp. 3061–3082
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Walke, K. Black, A. Lee, M. J. Kim, M. Du, C. Zheng, T. Zhao, P. Hansen-Estruch, Q. Vuong, A. He, V. Myers, K. Fang, C. Finn, and S. Levine, “Bridgedata v2: A dataset for robot learning at scale,” in Conference on Robot Learning (CoRL) , 2023
2023
Later among the works it cites.
G. Zhou, V. Dean, M. K. Srirama, A. Rajeswaran, J. Pari, K. Hatch, A. Jain, T. Yu, P. Abbeel, L. Pinto, C. Finn, and A. Gupta, “Train offline, test online: A real robot learning benchmark,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023
2023
Later among the works it cites.
S. Dass, J. Yapeter, J. Zhang, J. Zhang, K. Pertsch, S. Nikolaidis, and J. J. Lim, “Clvr jaco play dataset,” 2023. [Online]. Available: https://github.com/clvrai/clvr_jaco_play_dataset
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Liu, J. Hou, C. Li, and X. Wang, “Intelligent soft robotic grippers for agricultural and food product handling: A brief review with a focus on design and control,” Advanced Intelligent Systems , p. 2300233, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.