HAMLET (Human, Animal, and Machine Learning: Experiment and Theory)
HAMLET is an interdisciplinary proseminar series that started in 2008. The goal is to provide behavioral and computational graduate students with a common grounding in the learning sciences. Guest speakers give a talk each week, followed by discussions. It has been an outlet for fresh research results from various projects. Participants are typically from Computer Sciences, ECE, Psychology, Educational Psychology as well as other parts of the campus. Multiple federal research grants and publications at machine learning and cognitive psychology venues have resulted from the interactions of HAMLET participants.
Meetings: Fridays 3:45 p.m. – 5 p.m. Berkowitz room (338 Psychology Building)
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Fall 2017 Schedule:
Sept 15, Virtual and Physical: Two Frames of Mind, Bilge Mutlu (CS)
In creating interactive technologies, virtual and physical embodiments are often seen as two sides of the same coin. They utilize similar core technologies for perception, planning, and interaction and engage people in similar ways. Thus, designers consider these embodiments to be broadly interchangeable and choice of embodiment to primarily depend on the practical demands of an application. In this talk, I will make the case that virtual and physical embodiments elicit fundamentally different frames of mind in the users of the technology and follow different metaphors for interaction. These differences elicit different expectations, different forms of engagement, and eventually different interaction outcomes. I will discuss the design implications of these differences, arguing for different domains of interaction serving as appropriate context for virtual and physical embodiments.
October 13, Learning semantic representations for text: analysis of recent word embedding methods, Yingyu Liang (CS)
Recent advances in natural language processing build upon the approach of embedding words as low dimensional vectors. The fundamental observation that empirically justifies this approach is that these vectors can capture semantic relations. A probabilistic model for generating text is proposed to mathematically explain this observation and existing popular embedding algorithms. It also reveals surprising connections to classical notions such as Pointwise Mutual Information in computational linguistics, and allows to design novel, simple, and practical algorithms for applications such as embedding sentences as vectors.
October 20, Vlogging about research, Martina Rau (Ed Psych)
October 27, LUCID faculty meeting, No large group meeting
November 3, A Discussion of Open Science Practices, Martha W. Alibali (Psych)
This HAMLET session will be a discussion of open-science practices, led by Martha Alibali. We will start with brief discussion of the â€œreplication crisisâ€ and â€œquestionable research practicesâ€. We will then discuss solutions, including better research practices, data sharing and preregistration. Please read at least some of the provided papers, and come prepared to ask questions and share your experiences.
Replication crisis paper
Data sharing paper http://deevybee.blogspot.co.uk/2014/05/data-sharing-exciting-but-scary.html
November 10, Systematic misperceptions of 3D motion explained by Bayesian inference, Bas Rokers (Psych)
Abstract: Over the years, a number of surprising, but seemingly unrelated errors in 3D motion perception have been reported. Given the relevance of accurate motion perception to our everyday life, it is important to understand the cause of these perceptual errors. We considered that these perceptual errors might arise as a natural consequence of estimating motion direction given sensory noise and the geometry of 3D viewing. We characterized the retinal motion signals produced by objects moving along arbitrary trajectories through three dimensions and developed a Bayesian model of perceptual inference. The model predicted a number of known errors, including a lateral bias in the perception of motion trajectories, and a dependency of this bias on stimulus contrast and viewing distance. The model also predicted a number of previously unknown errors, including a dependency of perceptual bias on eccentricity, and a surprising tendency to misreport approaching motion as receding and vice versa. We then used standard 3D displays as well as a virtual reality (VR) headsets to test these predictions in naturalistic settings, and established that people make the predicted errors. In sum, we developed a quantitative model of 3D motion perception and provided a parsimonious account for a range of systematic perceptual errors in naturalistic environments.
November 17, Total variation regression under highly correlated designs, Becca Willett (ECE)
Abstract: I will describe a general method for solving high-dimensional linear inverse problems with highly correlated variables. This problem arises regularly in applications like neural decoding from fMRI data, where we often have two orders of magnitude more brain voxels than independent scans. Our approach leverages a graph structure that represents connections among voxels in the brain. This graph can be estimated from side sources, such as diffusion-weighted MRI, or from fMRI data itself. We will explore the underlying models, computational methods, and initial empirical results. This is joint work with Yuan Li and Garvesh Raskutti.
November 24 Thanksgiving Holiday
December 1, Micro-(Shape-And-Motion)-Scopes, Mohit Gupta (CS)
Imagine a drone looking for a safe landing site in a dense forest, or a social robot trying to determine the emotional state of a person by measuring her micro-saccade movements and skin-tremors due to pulse beats, or a surgical robot performing micro-surgery inside the body. In these applications, it is critical to resolve fine geometric details, such as tree twigs; to recover micro-motion due to biometric signals; and the precise motion of a robotic arm. Such precision is more than an order-of-magnitude beyond the capabilities of traditional vision techniques. I will talk about our recent work on designing extreme (micro) resolution 3D shape and motion sensors using unconventional, but low-cost optics, and computational techniques. These methods can measure highly subtle motions (< 10 microns), and highly detailed 3D geometry (<100 microns). These sensors can potentially detect a persons pulse or micro-saccade movements, and resolve fine geometric details such as a facial features, from a long distance.
December 8, Influence maximization in stochastic and adversarial settings, Po-Ling Loh (ECE)
We consider the problem of influence maximization in fixed networks, for both stochastic and adversarial contagion models. Such models may be used to model infection spreads in epidemiology, as well as the diffusion of information in viral marketing. In the stochastic setting, nodes are infected in waves according to linear threshold or independent cascade models. We establish upper and lower bounds for the influence of a subset of nodes in the network, where the influence is defined as the expected number of infected nodes at the conclusion of the epidemic. We quantify the gap between our upper and lower bounds in the case of the linear threshold model and illustrate the gains of our upper bounds for independent cascade models in relation to existing results. In the adversarial setting, an adversary is allowed to specify the edges through which contagion may spread, and the player chooses sets of nodes to infect in successive rounds. Our main result is to establish upper and lower bounds on the regret for possibly stochastic strategies of the adversary and player. This is joint work with Justin Khim (UPenn) and Varun Jog (UW-Madison).