no code implementations • ACL (splurobonlp) 2021 • Sayali Kulkarni, Shailee Jain, Mohammad Javad Hosseini, Jason Baldridge, Eugene Ie, Li Zhang
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates.
2 code implementations • 17 May 2023 • Chandan Singh, Aliyah R. Hsu, Richard Antonello, Shailee Jain, Alexander G. Huth, Bin Yu, Jianfeng Gao
Here, we ask whether we can automatically obtain natural language explanations for black box text modules.
no code implementations • 27 May 2022 • Aditya R. Vaidya, Shailee Jain, Alexander G. Huth
Overall, these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
no code implementations • NeurIPS 2020 • Shailee Jain, Vy Vo, Shivangi Mahto, Amanda LeBel, Javier S. Turek, Alexander Huth
To understand how the human brain represents this information, one approach is to build encoding models that predict fMRI responses to natural language using representations extracted from neural network language models (LMs).
1 code implementation • 21 Aug 2020 • Sayali Kulkarni, Shailee Jain, Mohammad Javad Hosseini, Jason Baldridge, Eugene Ie, Li Zhang
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations.
1 code implementation • ICML 2020 • Javier S. Turek, Shailee Jain, Vy Vo, Mihai Capota, Alexander G. Huth, Theodore L. Willke
In this work, we explore the delayed-RNN, which is a single-layer RNN that has a delay between the input and output.
no code implementations • NeurIPS 2018 • Shailee Jain, Alexander Huth
By varying the amount of context used in the models and providing the models with distorted context, we show that this improvement is due to a combination of better word embeddings learned by the LSTM language model and contextual information.