Monthly Archives: October 2018

UMass Week of Memory and Forgetting Oct 29 – Nov 2, 2018

From https://websites.umass.edu/ions/event/umass-week-of-memory-and-forgetting/

This is a week of events relating memory and forgetting from a variety of perspectives. It shows how memory is both an individual and a societal feature.

The Week of Memory and Forgetting is a collaboration between The Initiative on Neurosciences (IONs), the Fine Art Center (FAC), the Institute for Holocaust, Genocide, and Memory Studies (IHGMS), and faculty in the Department of Spanish and Portuguese Studies.

UMass Linguistics hiring in Psycholinguistics

Please share this job posting widely! Note that the Department of Linguistics hopes to find someone who:

…can engage with the development of the Cognitive Science Institute, a broad, interdisciplinary group focused on fostering Cognitive Science research across the UMass Community

Also, please note that  applicants at both the Assistant and Associate Professor levels are welcome to apply, and that the Department of Linguistics is fully committed to the University’s diversity goals summarized in the last paragraph of the posting.

https://careers.insidehighered.com/job/1614433/associate-professor-of-linguistics/

Zhu in MLFL Thurs. Oct. 4 at 11:45

who: Jun-Yan Zhu, MIT
when: October 4th, 11:45 A.M. – 1:00 P.M.
where: Computer Science Building, Room 150/151
food: Athena’s Pizza

Learning To Generate Images

Abstract: Deep learning has revolutionized the field of visual recognition. Since 2012, we witnessed an enormous jump in recognition performance on the standard benchmarks as well as many real-world applications. Meantime, many people in computer vision and graphics were wondering if deep learning can help visual synthesis. Unfortunately, it turned out that using deep neural networks to generate high-dimensional data such as images was extremely difficult. In this talk, I will discuss its main challenges and present a few end-to-end learning frameworks (e.g., pix2pix, CycleGAN, pix2pixHD) for generating and manipulating natural images. Then, I will show various applications such as generating synthetic training data (computer vision), photo manipulation and synthesis (computer graphics), converting MRIs into CT scans (medical imaging), and applications in NLP and speech synthesis. Finally, I will briefly discuss our ongoing efforts on learning to synthesize 3D textured objects and high-res videos, with the ultimate goal of recreating our visual world.

Bio: Jun-Yan Zhu is a postdoctoral researcher at MIT CSAIL. He obtained his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley in 2017 after spending five years at CMU and UC Berkeley. He received his B.E in Computer Sciences from Tsinghua University in 2012. His research interests are in computer vision, computer graphics, and machine learning, with the goal of building machines capable of understanding and recreating our visual world. His Ph.D. work was supported by a Facebook Fellowship. His dissertation won the 2018 ACM SIGGRAPH Outstanding Doctoral Dissertation Award from SIGGRAPH and 2017-18 David J. Sakrison Memorial Prize for outstanding doctoral research from the UC Berkeley EECS Department. He has served as a Technical Paper Committee member at SIGGRAPH Asia 2018 and a guest editor of International Journal of Computer Vision.

Juhasz in cognitive bag lunch Weds. 10/3 at noon

Barb Juhasz (Wesleyan) will present “Using eye movements to explore how experience with words in childhood impacts word recognition during college.” in the cognitive bag lunch in Tobin 521B, 12:00-1:25. An abstract follows. All are welcome!

Abstract. The age at which a word is first acquired has been found to affect word recognition in adulthood. Words that are rated as having an early age-of-acquisition (AoA) are processed faster than words rated as having a late AoA in many tasks. However, even words that are learned early in life may differ in how frequently they are encountered during childhood. Frequency trajectory refers to the pattern of frequency exposure across schooling and can be measured by comparing word frequency counts for texts that are relevant for early elementary students with frequency counts for college-level texts. Some words are more frequent in early grades compared to college (e.g. rabbit) while others become more frequent in college-level texts (e.g. brain). Other words maintain a consistently high or low word frequency across grades. In this talk, I will discuss current research projects that explore the time course of AoA and frequency trajectory effects on eye movements during reading in college students. These projects have demonstrated that both the age at which a word is initially acquired and its pattern of frequency exposure during schooling impact word recognition.