Standout Papers

Understanding the difficulty of training deep feedforward neural networks 2010 2026 2015 2020 5.9k
  1. Understanding the difficulty of training deep feedforward neural networks (2010)
    Xavier Glorot, Yoshua Bengio Journal of Machine Learning Research
  2. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework (2017)
    Irina Higgins, Löıc Matthey et al. International Conference on Learning Representations
  3. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction (2011)
    Salah Rifai, Pascal Vincent et al. International Conference on Machine Learning
  4. A semantic matching energy function for learning with multi-relational data (2013)
    Antoine Bordes, Xavier Glorot et al. Machine Learning

Immediate Impact

16 by Nobel laureates 26 from Science/Nature 92 standout
Sub-graph 1 of 23

Citing Papers

Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models
2023 StandoutNobel
A compute-in-memory chip based on resistive random-access memory
2022 StandoutNature
2 intermediate papers

Works of Xavier Glorot being referenced

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
2017 Standout
Understanding the difficulty of training deep feedforward neural networks
2010 Standout

Author Peers

Author Last Decade Papers Cites
Xavier Glorot 3218 2638 439 651 6 7.7k
Mark E. Shields 3729 2701 392 689 4 9.5k
Yee‐Whye Teh 3341 2562 238 952 4 8.5k
Diederik P. Kingma 3975 3260 383 890 15 8.2k
Sherjil Ozair 2061 2501 146 439 5 6.5k
D. Henderson 2656 2906 142 478 14 7.8k
Vinod Nair 2129 2594 164 626 16 7.3k
Mehdi Mirza 2148 2645 127 454 4 6.7k
Sheelagh Lloyd 2069 1980 322 833 6 7.5k
Vincent Vanhoucke 3511 2022 183 1569 13 7.4k
Jean Pouget-Abadie 1871 2418 123 404 4 6.2k

All Works

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Rankless by CCL
2026