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Embodied Multimodal Multitask Learning

2 minute read



  • Deep RL has been successful in training visual navigational agents conditioned on language for multimodal tasks, e.g. semantic goal navigation and embodied question answering
  • Propose a multitask model that can jointly learn these tasks
  • Novel Dual-Attention unit to disentangle words and visual concepts
  • Paper link

Toddler-Inspired Visual Object Learning

3 minute read



  • Real-world learning systems are limited in the quality and quantity of training datasets they can collect
  • Use head-mounted cameras and gaze trackers to collect egocentric images from human child in naturalistic learning contexts
  • Child data produces better object models than egocentric adult data
  • Child data exhibits unique combination of quality and diversity
  • Paper link

Automated Curriculum Learning for Neural Networks

3 minute read



  • Maximize learning efficiency by following a curriculum
  • Measure of the amount that the network learns from each data sample used as reward
  • Nonstationary multi-armed bandit algorithm
  • Consider a variety of signals based on rate of increase in prediction accuracy and network complexity
  • Experimental results with LSTMs on three curricula
  • Paper link


Artistic Style Transfer

December 2016

Wang, E., Tan, N. Artistic Style Transfer. Stanford Digital Image Processing (EE 368) Project, 2016

Download: [pdf]


Flexible Neural Representation for Physics Prediction

Published in NeurIPS, 2018

Mrowca, D.*, Zhuang, C.*, Wang, E.*, Haber, N., Fei-Fei, L., Tenenbaum, J.B., Yamins, D. (2018). Flexible Neural Representation for Physics Prediction. In Advances in Neural Information Processing Systems (NeurIPS) 31

Download: [pdf]



Teaching Assistant

Undergraduate course, Cornell University, 2015

Undergraduate TA for PHYS 2214 Physics III: Oscillations, Waves and Quantum Physics