Computability of Classification and Deep Learning: From Theoretical
Limits to Practical Feasibility through Quantization

Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization

12 August 2024

Papers citing "Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization"

15 / 15 papers shown

Title
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement Holger Boche Adalbert Fono Gitta Kutyniok FaML 72 4 0 18 Jan 2024
GPT-4 Technical Report OpenAI OpenAI OpenAI Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad ... Shengjia Zhao Tianhao Zheng Juntang Zhuang William Zhuk Barret Zoph LLMAG MLLM 1.4K 14,313 0 15 Mar 2023
Limitations of Deep Learning for Inverse Problems on Digital Hardware Holger Boche Adalbert Fono Gitta Kutyniok 51 25 0 28 Feb 2022
The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks Alexander Bastounis A. Hansen Verner Vlacic AAML OOD 64 28 0 13 Sep 2021
Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem Matthew J. Colbrook Vegard Antun A. Hansen 109 135 0 20 Jan 2021
Computing Systems for Autonomous Driving: State-of-the-Art and Challenges Liangkai Liu Sidi Lu Ren Zhong Baofu Wu Yongtao Yao Qingyan Zhang Weisong Shi 66 275 0 30 Sep 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 95 377 0 30 Apr 2020
The gap between theory and practice in function approximation with deep neural networks Ben Adcock N. Dexter 44 93 0 16 Jan 2020
Quantization Networks Jiwei Yang Xu Shen Jun Xing Xinmei Tian Houqiang Li Bing Deng Jianqiang Huang Xiansheng Hua MQ 72 346 0 21 Nov 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang Hongge Chen Chaowei Xiao Sven Gowal Robert Stanforth Yue Liu Duane S. Boning Cho-Jui Hsieh AAML 67 347 0 14 Jun 2019
On instabilities of deep learning in image reconstruction - Does AI come at a cost? Vegard Antun F. Renna C. Poon Ben Adcock A. Hansen 48 603 0 14 Feb 2019
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 99 1,778 0 30 May 2018
Optimal Approximation with Sparsely Connected Deep Neural Networks Helmut Bölcskei Philipp Grohs Gitta Kutyniok P. Petersen 189 255 0 04 May 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 315 1,867 0 03 Feb 2017
WaveNet: A Generative Model for Raw Audio Aaron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner A. Senior Koray Kavukcuoglu DiffM 401 7,391 0 12 Sep 2016