Idea Transcript
Scott Reed
I completed my PhD at the University of Michigan. My adviser was Honglak Lee. Currently I am a senior research scientist at Google DeepMind. Linkedin: linkedin.com/in/reedscott GitHub: github.com/reedscot
Interests My current research topics are related to representation learning and deep learning, scalable object detection, multimodal learning, learning to disentangle factors of variation in sensory data, visual analogy-making, and learning compositional programs. Broadly I am interested in neural architectures that can automatically learn feature representations with strong generalization properties in a data-efficient manner.
Publications ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans [PDF][BibTex] Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Jurgen Stürm, Matthias Nießner In IEEE Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018. Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions [PDF][BibTex] Scott Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, Oriol Vinyals, S. M. Ali Eslami, Danilo Rezende, Nando de Freitas In International Conference on Learning Representations (ICLR), Vancouver, Canada, 2018. Robust Imitation of Diverse Behaviors [PDF][BibTex] Ziyu Wang, Josh Merel, Scott Reed, Greg Wayne, Nando de Freitas, Nicolas Heess In Advances in Neural Information Processing Systems (NIPS), Long Beach, 2017. Parallel Multiscale Autoregressive Density Estimation [PDF][BibTex] Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez, Ziyu Wang, Dan Belov, Nando de Freitas In Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, 2017. Text- and Structure-Conditional PixelCNN [PDF][BibTex] Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Victor Bapst, Matt Botvinick, Nando de Freitas Technical Report Learning What and Where to Draw [PDF][BibTex][Code and Data][Supplement] Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee Oral Presentation In Advances in Neural Information Processing Systems (NIPS), Barcelona, 2016. Generative Adversarial Text-to-Image Synthesis [PDF][Supplement][BibTex][Code] Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee. In Proceedings of the 33rd International Conference on Machine Learning (ICML), New York, USA, 2016. Neural Programmer-Interpreters [PDF][Slides][BibTex][Project page] Scott Reed and Nando de Freitas This work was done during my internship at Google DeepMind. Best Paper Award In International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, 2016. SSD: Single Shot MultiBox Detector [PDF][BibTex] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg In European Conference on Computer Vision (ECCV), Amsterdam, 2016. Learning Deep Representations of Fine-Grained Visual Descriptions [PDF][BibTex][Code] Scott Reed, Zeynep Akata, Bernt Schiele, Honglak Lee Spotlight Presentation In IEEE Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016. Deep Visual Analogy-Making [PDF][BibTex][Slides][Code][Data] Scott Reed, Yi Zhang, Yuting Zhang, Honglak Lee Oral Presentation In Advances in Neural Information Processing Systems (NIPS), Montreal, 2015. Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis [PDF][BibTex] Jimei Yang, Scott Reed, Ming-Hsuan Yang, Honglak Lee In Advances in Neural Information Processing Systems (NIPS), Montreal, 2015. Scalable, High-Quality Object Detection [PDF][BibTex] Christian Szegedy, Scott Reed, Dumitru Erhan, Dragomir Anguelov Evaluation of Output Embeddings for Fine-Grained Image Classification [PDF][BibTex] Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, Bernt Schiele In IEEE Computer Vision and Pattern Recognition (CVPR), Boston, USA, 2015. Going deeper with convolutions [PDF][BibTex] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich In IEEE Computer Vision and Pattern Recognition (CVPR), Boston, USA, 2015. Related Google Research Blog: Building a deeper understanding of images Learning to Disentangle Factors of Variation with Manifold Interaction [PDF][Code][BibTex] Scott Reed, Kihyuk Sohn, Yuting Zhang, Honglak Lee. In Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, 2014.
Awards and Honors National Science Foundation Graduate Research Fellowship Program (NSF GRFP) National Defense Science and Engineering Graduate Fellowship (NDSEG) Telluride Association Room and Board Scholarship (2007-2011) Chrysler Foundation Scholarship (2007-2011) Phi Beta Kappa (2011) NSF SUBMERGE REU scholarship (2009-2011)