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Publication search results
found 79 matches
- 2024
- Thomas Hellström, Suna Bensch:
Apocalypse now: no need for artificial general intelligence. AI Soc. 39(2): 811-813 (2024) - Yusen Zhang:
A General Benchmark Framework is Dynamic Graph Neural Network Need. CoRR abs/2401.06559 (2024) - Benjamin Doerr, Andrew James Kelley:
The Runtime of Random Local Search on the Generalized Needle Problem. CoRR abs/2403.08153 (2024) - Maxime Zanella, Ismail Ben Ayed:
On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning? CoRR abs/2405.02266 (2024) - 2023
- Wen Wu, Wenya Yang, Weiyin Ma, Xiao-Diao Chen:
How Many Annotations Do We Need for Generalizing New-Coming Shadow Images? IEEE Trans. Circuits Syst. Video Technol. 33(11): 6213-6224 (2023) - Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, Pascal Vincent:
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning. Trans. Mach. Learn. Res. 2023 (2023) - Alban Petit, Caio F. Corro, François Yvon:
Structural generalization in COGS: Supertagging is (almost) all you need. EMNLP 2023: 1089-1101 - Yao Wei, Yanchao Sun, Ruijie Zheng, Sai Vemprala, Rogerio Bonatti, Shuhang Chen, Ratnesh Madaan, Zhongjie Ba, Ashish Kapoor, Shuang Ma:
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training. ICCV 2023: 16175-16185 - Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein:
Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. ICLR 2023 - Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan:
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation. ICML 2023: 27680-27708 - Arpit Bahety, Shreeya Jain, Huy Ha, Nathalie Hager, Benjamin Burchfiel, Eric Cousineau, Siyuan Feng, Shuran Song:
Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects. IROS 2023: 960-967 - Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan:
Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation. CoRR abs/2302.08923 (2023) - Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu:
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need. CoRR abs/2303.07338 (2023) - Weihua Liu, Yong Zuo:
Stone Needle: A General Multimodal Large-scale Model Framework towards Healthcare. CoRR abs/2306.16034 (2023) - Yao Wei, Yanchao Sun, Ruijie Zheng, Sai Vemprala, Rogerio Bonatti, Shuhang Chen, Ratnesh Madaan, Zhongjie Ba, Ashish Kapoor, Shuang Ma:
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training. CoRR abs/2307.07909 (2023) - Alban Petit, Caio F. Corro, François Yvon:
Structural generalization in COGS: Supertagging is (almost) all you need. CoRR abs/2310.14124 (2023) - 2022
- Leonie Westerbeek, Gert-Jan de Bruijn, Henk C. P. M. van Weert, Ameen Abu-Hanna, Stephanie Medlock, Julia C. M. van Weert:
General Practitioners' needs and wishes for clinical decision support Systems: A focus group study. Int. J. Medical Informatics 168: 104901 (2022) - Joshua Vendrow, Jamie Haddock, Deanna Needell:
A Generalized Hierarchical Nonnegative Tensor Decomposition. ICASSP 2022: 4473-4477 - Manikya Swathi Vallabhajosyula, Rajiv Ramnath:
Towards Practical, Generalizable Machine-Learning Training Pipelines to build Regression Models for Predicting Application Resource Needs on HPC Systems. PEARC 2022: 43:1-43:5 - Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney:
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. CoRR abs/2202.02842 (2022) - Wesley Hanwen Deng, Nikita Mehandru, Samantha Robertson, Niloufar Salehi:
Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust. CoRR abs/2205.06920 (2022) - Arpit Bahety, Shreeya Jain, Huy Ha, Nathalie Hager, Benjamin Burchfiel, Eric Cousineau, Siyuan Feng, Shuran Song:
Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects. CoRR abs/2210.09997 (2022) - 2021
- HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell:
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions. J. Mach. Learn. Res. 22: 185:1-185:36 (2021) - Bo Wei, Kai Li, Chengwen Luo, Weitao Xu, Jin Zhang, Kuan Zhang:
No Need of Data Pre-processing: A General Framework for Radio-based Device-free Context Awareness. ACM Trans. Internet Things 2(4): 29:1-29:26 (2021) - Samir Chowdhury, Tom Needham:
Generalized Spectral Clustering via Gromov-Wasserstein Learning. AISTATS 2021: 712-720 - Inbar Oren, Jonathan Herzig, Jonathan Berant:
Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization. EMNLP (1) 2021: 10793-10809 - Samu Kumpulainen, Vagan Y. Terziyan:
Artificial General Intelligence vs. Industry 4.0: Do They Need Each Other? ISM 2021: 140-150 - Alejandro Carderera, Mathieu Besançon, Sebastian Pokutta:
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions. NeurIPS 2021: 5390-5401 - Junbum Cha, Hancheol Cho, Kyungjae Lee, Seunghyun Park, Yunsung Lee, Sungrae Park:
Domain Generalization Needs Stochastic Weight Averaging for Robustness on Domain Shifts. CoRR abs/2102.08604 (2021) - HanQin Cai, Keaton Hamm, Longxiu Huang, Deanna Needell:
Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions. CoRR abs/2103.11037 (2021)
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