Fetching the paper…
Reading the bibliography…
Traditional human-in-the-loop-based annotation for time-series data like inertial data often requires access to alternate modalities like video or audio from the environment.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
PAMAP2 Physical Activity Monitoring
Attila Reiss. 2012 · 2012
Earlier work this paper cites.
Human Activity Recognition Using Smartphones
Reyes-Ortiz Jorge, Anguita Davide, Ghio Alessandro, Oneto Luca, Parra Xavier. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Heterogeneity Activity Recognition
Blunck Henrik, Bhattacharya Sourav, Prentow Thor, Kjrgaard Mikkel, and Dey Anind. 2015 · 2015
Earlier work this paper cites.
Actively avoiding nonsense in generative models. In ACM COLT . PMLR, 209–227
Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, and Christos Tzamos. 2018 · 2018
Earlier work this paper cites.
Active deep learning for activity recognition with context aware annotator selection. In ACM SIGKDD . 1862–1870
HM Sajjad Hossain and Nirmalya Roy. 2019 · 2019
Earlier work this paper cites.
Handling annotation uncertainty in human activity recognition. In ACM ISWC . 109–117
Hyeokhyen Kwon, Gregory D Abowd, and Thomas Plötz. 2019 · 2019
Earlier work this paper cites.
Sensing fine-grained hand activity with smartwatches. In ACM CHI . 1–13
Gierad Laput and Chris Harrison. 2019 · 2019
Earlier work this paper cites.
Mobile sensor data anonymization. In IoTDI . 49–58
Mohammad Malekzadeh, Richard G Clegg, Andrea Cavallaro, and Hamed Haddadi. 2019 · 2019
Earlier work this paper cites.
Fixing mislabeling by human annotators leveraging conflict resolution and prior knowledge
Mattia Zeni, Wanyi Zhang, Enrico Bignotti, Andrea Passerini, and Fausto Giunchiglia. 2019 · 2019
Earlier work this paper cites.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020 · 2020
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations. In ICML . PMLR, 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning. In IEEE CVPR . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
Earlier work this paper cites.
A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon. 2020 · 2020
Earlier work this paper cites.
Exploring contrastive learning in human activity recognition for healthcare
Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, and Cecilia Mascolo. 2020 · 2020
Earlier work this paper cites.
SyncWISE: Window induced shift estimation for synchronization of video and accelerometry from wearable sensors
Yun C Zhang, Shibo Zhang, Miao Liu, Elyse Daly, Samuel Battalio, Santosh Kumar, Bonnie Spring, James M Rehg, and Nabil Alshurafa. 2020 · 2020
Earlier work this paper cites.
Time series change point detection with self-supervised contrastive predictive coding. In ACM WWW . 3124–3135
Shohreh Deldari, Daniel V Smith, Hao Xue, and Flora D Salim. 2021 · 2021
Earlier work this paper cites.
Cocoa: Cross modality contrastive learning for sensor data
Shohreh Deldari, Hao Xue, Aaqib Saeed, Daniel V Smith, and Flora D Salim. 2022 · 2022
Cited alongside, same era.
Jury learning: Integrating dissenting voices into machine learning models. In ACM CHI . 1–19
Mitchell L Gordon, Michelle S Lam, Joon Sung Park, Kayur Patel, Jeff Hancock, Tatsunori Hashimoto, and Michael S Bernstein. 2022 · 2022
Cited alongside, same era.
Assessing the state of self-supervised human activity recognition using wearables
Harish Haresamudram, Irfan Essa, and Thomas Plötz. 2022 · 2022
Cited alongside, same era.
Collossl: Collaborative self-supervised learning for human activity recognition
Yash Jain, Chi Ian Tang, Chulhong Min, Fahim Kawsar, and Akhil Mathur. 2022 · 2022
Cited alongside, same era.
Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al · 2022
Cited alongside, same era.
Large Language Models are Few-Shot Health Learners
Xin Liu, Daniel McDuff, Geza Kovacs, Isaac Galatzer-Levy, Jacob Sunshine, Jiening Zhan, Ming-Zher Poh, Shun Liao, Paolo Di Achille, and Shwetak Patel. 2023 · 2023
Later among the works it cites.
Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation. In ACM MM . 643–654
Leigang Qu, Shengqiong Wu, Hao Fei, Liqiang Nie, and Tat-Seng Chua. 2023 · 2023
Later among the works it cites.
Penetrative ai: Making llms comprehend the physical world
Huatao Xu, Liying Han, Mo Li, and Mani Srivastava. 2023a · 2023
Later among the works it cites.
Shawn Xu, Lin Yang, Christopher Kelly, Marcin Sieniek, Timo Kohlberger, Martin Ma, Wei-Hung Weng, Attila Kiraly, Sahar Kazemzadeh, Zakkai Melamed, et al · 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…
What makes good contrastive learning on small-scale wearable-based tasks?. In ACM SIGKDD . 3761–3771
Hangwei Qian, Tian Tian, and Chunyan Miao. 2022 · 2022
Cited alongside, same era.
Cogax: Early assessment of cognitive and functional impairment from accelerometry. In PerCom . IEEE, 66–76
Sreenivasan Ramasamy Ramamurthy, Soumyajit Chatterjee, Elizabeth Galik, Aryya Gangopadhyay, Nirmalya Roy, Bivas Mitra, and Sandip Chakraborty. 2022 · 2022
Cited alongside, same era.
Breaking away from labels: The promise of self-supervised machine learning in intelligent health
Dimitris Spathis, Ignacio Perez-Pozuelo, Laia Marques-Fernandez, and Cecilia Mascolo. 2022 · 2022
Cited alongside, same era.
A survey of human-in-the-loop for machine learning
Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, and Liang He. 2022 · 2022
Cited alongside, same era.
Leveraging language foundation models for human mobility forecasting. In ACM SIGSPATIAL . 1–9
Hao Xue, Bhanu Prakash Voutharoja, and Flora D Salim. 2022 · 2022
Cited alongside, same era.
React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022
Cited alongside, same era.
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data
Hang Yuan, Shing Chan, Andrew P Creagh, Catherine Tong, David A Clifton, and Aiden Doherty. 2022 · 2022
Cited alongside, same era.
Promptcast: A new prompt-based learning paradigm for time series forecasting
Hao Xue and Flora D Salim. 2023 · 2023
Later among the works it cites.
Video-llama: An instruction-tuned audio-visual language model for video understanding
Hang Zhang, Xin Li, and Lidong Bing. 2023a · 2023
Later among the works it cites.
Balancing specialized and general skills in llms: The impact of modern tuning and data strategy
Zheng Zhang, Chen Zheng, Da Tang, Ke Sun, Yukun Ma, Yingtong Bu, Xun Zhou, and Liang Zhao. 2023b · 2023
Later among the works it cites.
Explainability for large language models: A survey
Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, and Mengnan Du. 2023 · 2023
Later among the works it cites.
LLM-based NLG Evaluation: Current Status and Challenges
Mingqi Gao, Xinyu Hu, Jie Ruan, Xiao Pu, and Xiaojun Wan. 2024 · 2024
Closest in time.
Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, and Qingsong Wen. 2024 · 2024
Closest in time.
Health-llm: Large language models for health prediction via wearable sensor data
Yubin Kim, Xuhai Xu, Daniel McDuff, Cynthia Breazeal, and Hae Won Park. 2024 · 2024
Closest in time.
Towards generalist biomedical ai
Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Charles Lau, Ryutaro Tanno, Ira Ktena, et al · 2024
Closest in time.
Large Language Models for Time Series: A Survey
Xiyuan Zhang, Ranak Roy Chowdhury, Rajesh K. Gupta, and Jingbo Shang. 2024 · 2024
Closest in time.
What is a Vector Database?
Microsoft. 2023 · 2026
Closest in time.
Rate limits
OpenAI. 2023 · 2026
Closest in time.
How much does GPT-4 cost?
OpenAI. 2024 · 2026
Closest in time.
How does in-context learning work? A framework for understanding the differences from traditional supervised learning
Sang Michael Xie and Sewon Min. 2022 · 2026
Closest in time.