Papers

A Correlation Maximization Approach for Cross Domain Co-Embeddings. Shiebler, D. In submission to AAAI (2018)

Learning what and where to attend with humans in the loop. Linsley, D., Shiebler, D., Eberhardt, S., & Serre, T. In submission to ICLR (2018)

Global-and-local attention networks for visual recognition. Linsley, D., Shiebler, D., Eberhardt, S., & Serre, T. Computational Cognitive Neuroscience (2018)

Fighting Redundancy and Model Decay with Embeddings. Shiebler, D., Belli, L., Baxter, J., Xiong, H., & Tayal, A. KDD Workshop on Common Model Infrastructure (2018)

Making Machine Learning Easy with Embeddings. Shiebler, D., & Tayal, A. SysML (2018)

A Dichotomy of Visual Relations. Kim, J., Ricci, M., Shiebler, D., & Serre, T. Computational Cognitive Neuroscience (2017)

Beta Shift and Decision Conflict. Shiebler, D. Brown University Neuroscience Sc.B Honors Thesis (2015)

Patents

Systems and Methods for Detecting and Assessing Distracted Drivers. Cordova, B., Finegold, R., Shiebler, D., & Farrell, K. (U.S. 2017/0105098 A1)

Systems and Methods for Scoring Driver Trips Cordova, B., Finegold, R., Shiebler, D., & Cecala, S. (U.S. 2017/0349182 A1)

Systems and Methods for Sensor-Based Vehicle Crash Prediction, Detection and Reconstruction. Cordova, B., Vaisman, E., & Shiebler, D. (U.S. 2017/0210323 A1)

Systems and Methods for Detecting Airbag Deployment Resulting From a Vehicle Crash Cordova, B., Vaisman, E., & Shiebler, D. (U.S. 2018/0126938 A1)

Conference Abstracts

Capturing Lingual Shifts in Word Embeddings with CCA. Kamani, M., Shiebler, D., Tayal, A., Verma, S., & Belli, L. WeCNLP (2018)

FastTweets: Measuring Embedding Quality for Highly Variable Text Data. Rabhi, S., Green, C., Verma, S., Shiebler, D., & Belli, L. WeCNLP (2018)

A Data-Driven Approach to Learning 3D Shape. Eberhardt, S., Shiebler, D., Linsley, D., & Serre, T. Journal of Vision. (2017)

Large-scale discovery of visual features for object recognition. Linsley, D., Eberhardt, S., Shiebler, D., & Serre, T. Computational and Mathematical Models in Vision (2017)

More Feedback, Less Depth: Approximating Human Vision with Deep Networks. Eberhardt, S., Cader, J., Linsley, D., Barhomi, Y., Shiebler, D., & Serre, T. NIPS Workshop on Representation Learning in Artificial and Biological Neural Networks (2016)