no code implementations • 19 Sep 2022 • Alicia Y. Tsai, Juliette Decugis, Laurent El Ghaoui, Alper Atamtürk
Implicit models are a general class of learning models that forgo the hierarchical layer structure typical in neural networks and instead define the internal states based on an ``equilibrium'' equation, offering competitive performance and reduced memory consumption.
no code implementations • EMNLP (sustainlp) 2020 • Alicia Y. Tsai, Laurent El Ghaoui
To generate a summary with $k$ sentences, the algorithm only needs to execute $\approx k$ iterations, making it very efficient.
no code implementations • EMNLP (NLP4ConvAI) 2021 • Alicia Y. Tsai, Shereen Oraby, Vittorio Perera, Jiun-Yu Kao, Yuheng Du, Anjali Narayan-Chen, Tagyoung Chung, Dilek Hakkani-Tur
Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods.
no code implementations • 26 Nov 2020 • Alicia Y. Tsai, Selim Gunay, Minjune Hwang, Pengyuan Zhai, Chenglong Li, Laurent El Ghaoui, Khalid M. Mosalam
Post-hazard reconnaissance for natural disasters (e. g., earthquakes) is important for understanding the performance of the built environment, speeding up the recovery, enhancing resilience and making informed decisions related to current and future hazards.
no code implementations • 17 Aug 2019 • Laurent El Ghaoui, Fangda Gu, Bertrand Travacca, Armin Askari, Alicia Y. Tsai
Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks.