Danny Fox of MIT will present “Exhaustivity as cell identification” in the Linguistics Colloquium series organized by the GLSA. The talk is in ILC N400 at 3:30 – all are welcome! The abstract is below.

Abstract:

Under the Grammatical Theory of Scalar Implicatures (GT of SIs), SIs are logical entailments of ambiguous sentences – entailments of grammatical representations containing a (covert) focus sensitive operator, exh. Conceptual and empirical arguments have been presented in favor of GT. GT is consistent with conversational maxims that can be defended on a-priori grounds. By contrast, the pragmatic alternatives require the rejection of what are arguably basic truisms. In additions, GT is supported on various empirical grounds, among them considerations of modularity/blindness, the interaction between ignorance inferences and SIs, embedded implicatures, and various other areas of grammar in which Exhaustification plays a role (for a review and relevant references, see Chierchia, Fox and Spector 2012, Fox 2014, and Chierchia 2017).

However, the definitions of exh provided in most versions of GT have been suspiciously close to what is derived by Neo-Gricean mechanisms. I will argue that this conceptual difficulty might be eliminated when we look at the presuppositions of questions. Building on and modifying work of Dayal (1996) I will argue that exh is involved in the statement of these presuppositions. Specifically: a question, Q, presupposes that every cell in the partition it induces can be identified by a member of Q, via Exhaustification (?C?Partition(Q)[?p?Q[Exh(Q,p)=C]]), and conversely, that every member of Q identifies a cell in the partition (?p?Q[?C?Partition(Q)[Exh(Q,p)=C]]).

Drawing on recent work with Moshe Bar-Lev, I will provide an alternative definition of exh that is quite distinct from operators that reflect Neo-Gricean mechanisms. I will also argue that this definition can be understood based on considerations that come from the role of exh in question semantics (its role in cell identification). The empirical arguments will be based primarily on the distribution of negative islands and “mention some” readings.

Kyunghyun Cho (NYU) will present “Deep Learning, Where are you going?” in the Machine Learning and Friends Lunch Thursday Oct. 20 at 10:00am in CS 150. Abstract and bio follow.

Abstract:

There are three axes along which advances in machine learning and deep learning happen. They are (1) network architectures, (2) learning algorithms and (3) spatio-temporal abstraction. In this talk, I will describe a set of research topics I’ve pursued in each of these axes. For network architectures, I will describe how recurrent neural networks, which were largely forgotten during 90s and early 2000s, have evolved over time and have finally become a de facto standard in machine translation. I continue on to discussing various learning paradigms, how they related to each other, and how they are combined in order to build a strong learning system. Along this line, I briefly discuss my latest research on designing a query-efficient imitation learning algorithm for autonomous driving. Lastly, I present my view on what it means to be a higher-level learning system. Under this view each and every end-to-end trainable neural network serves as a module, regardless of how they were trained, and interacts with each other in order to solve a higher-level task. I will describe my latest research on trainable decoding algorithm as a first step toward building such a framework.
Bio:

Kyunghyun Cho is an assistant professor of computer science and data science at New York University. He was a postdoctoral fellow at University of Montreal until summer 2015, and received PhD and MSc degrees from Aalto University early 2014. He tries best to find a balance among machine learning, natural language processing and life, but often fails to do so.

David Kellen (Syracuse University) will present in the Cognitive Bag Lunch series, Wednesday at noon in Tobin 521B. All are welcome!

The Cognitive Science Society is seeking applications for a new Graduate Student Representative to the society’s Governing Board. The GSR will be a current graduate student in a cognitive science related field who will represent student interests as a non-voting member of the Governing Board. In addition, the GSR will sit on the society’s Communications Committee and will manage the twitter and facebook accounts of the society, staying active in updating these on a regular basis with society news and publications in the society journals. We anticipate a total commitment of 1-3 hrs/week plus a few more significant efforts (e.g., before Board meetings or in a revision of the website).

This position carries a two year term and has as compensation: society membership, free registration at the 2018 and 2019 meetings, travel reimbursement for the meetings each year (up to $1000 for domestic and $2000 for international), and a $1000 stipend each year. Applicants should have the expectation to be in a graduate program during their full term (e.g., should be relatively early on in their graduate career).

Applications should be sent to mcfrank@stanford.edu with the title “CogSci GSR” and should include a CV and a brief (200-300 word) statement describing the candidate’s professional goals, interest in the position (for example, what would be priorities as a representative), and relevant experience in communication and/or social media. Evidence of prior commitment to the Cognitive Science Society (e.g., membership, annual meeting attendance) will be taken into account in the process but is not necessary. The applicant should also arrange for a brief letter of recommendation from an academic mentor (email or letterhead) to be sent separately to the same address.

Please submit applications by 11/6 for full consideration by the Communications Committee. The new GSR will be notified in late November and the current term will start on 12/1.

The UMass Computational Social Science Institute invites you to an exciting talk by one of our own affiliates:

Song Gao
Associate Professor of Civil and Environmental Engineering, University of Massachusetts
Friday, October 13, 2017 • 12:30 p.m.-2:00 p.m. (lunch served at 12:15)
Computer Science Building, Room 150/151

Title: Travel Decision Making in an Uncertain, Dynamic, Information-Rich Urban Network

Abstract: Transportation systems are uncertain due to disruptions such as incidents and inclement weather. Real-time information, an integral element of smart cities, allows travelers to adapt to traffic conditions and potentially mitigate the adverse effects of uncertainty. Travelers create and suffer from congestion. The collective of all travelers’ choices defines the traffic load and its distribution. Understanding the choices made by travelers in an uncertain and dynamic environment who have access to real-time traffic information is paramount to better system design, management, and policy making. I have conducted a series of empirical studies using data from both laboratory experiments and in-vehicle tracking and monitoring devices in real-life urban networks. In this talk, I will focus on the modeling and optimization of adaptive route choice based on GPS (Global Positioning System) data from Stockholm, Sweden. It is found that two types of travelers both exist: one adapts to real-time information, and the other does not. Travelers are more likely to adapt during longer trips. Results from laboratory experiment suggest that travelers learn to adapt at a slower pace in a more complex network. An ongoing project on route optimization for taxi drivers’ customer searching using GPS data from Shanghai, China will also be discussed.

Bio: Song Gao is an associate professor of Civil and Environmental Engineering at the University of Massachusetts Amherst. Dr. Gao’s research focuses on transportation network optimization, econometric and psychological models of traveler behavior, equilibrium analysis of transportation systems with traveler information, with applications in smart and shared mobility, transportation planning under both normal and emergency conditions, and sustainable transportation systems. Prior to joining the faculty of the University of Massachusetts Amherst in 2007, Dr. Gao worked as a transportation engineer at Caliper Corporation, Newton, MA for three years, and developed advanced traffic assignment modules for TransCAD, a GIS-based transportation planning software. Dr. Gao received her Ph.D. and M.S. in Transportation from Massachusetts Institute of Technology in 2005 and 2002 respectively. She received her B.S. in Civil Engineering from Tsinghua University of China in 1999.

Varun Jampani (Nvidia) will present “Bilateral Neural Networks for Image, Video and 3D Vision” in the Machine Learning and Friends Lunch Thursday Oct. 12 from 12:00pm to 1:00pm in CS 150. Abstract and bio follow.

Abstract:

Natural images exhibit high information correlation across pixels. Bilateral filtering provides a simple yet powerful framework for information propagation across pixels. The common use-case is to manually choose a parametric filter type, usually a Gaussian filter. We generalize the parameterization using a high-dimensional linear approximation and derive a gradient descent algorithm so the filter parameters can be learned from data. We demonstrate the use of learned bilateral filters in several applications where Gaussian bilateral filters are traditionally employed where we consistently observed improvements with filter learning. In addition, the ability to learn generic high-dimensional sparse filters allows us to stack several parallel and sequential filters like in convolutional neural networks (CNN) resulting in a new breed of neural networks which we call ‘Bilateral Neural Networks’ (BNN). We demonstrate the use of BNNs on several 2D, video and 3D vision tasks. Experiments on diverse datasets and tasks demonstrate the use BNNs for a range of vision problems.

Short Bio:

Varun Jampani works as a research scientist at Nvidia Research in Westford, US. He obtained his PhD at Max Planck Institute for Intelligent Systems (MPI) in Tübingen, Germany under the supervision of Prof. Peter V. Gehler. He works in the areas of machine learning and computer vision and his main research interests include probabilistic inference and neural networks. He obtained his BTech and MS from International Institute of Information Technology, Hyderabad (IIIT-H), India, where he was a gold medalist. During his studies, he did internships at Microsoft research institutes in Redmond (US), Cambridge (UK) and Cairo (Egypt); MPI, Tübingen (Germany) and; GE global research, Bangalore (India). He also worked as a volunteer teacher in Tibetan Children’s Village, Dharamsala, India.

Luke Huszar (from Dave’s lab) will present in the Cognitive Bag Lunch series, Wednesday at noon in Tobin 521B. All are welcome!

The UMass Computational Social Science Institute invites you to an exciting talk by one of our own affiliates:

Florence Sullivan
Associate Professor of Education, University of Massachusetts
Friday, October 6, 2017 • 12:30 p.m.-2:00 p.m. (lunch served at 12:15)
Computer Science Building, Room 150/151

Title: Microgenetic Learning Analytics: Exploring Computational Means to Support Research on Collaborative Learning

Abstract: Microgenetic analysis is an educational research method that aims to capture and describe the development of conceptual understanding in children over short periods of time (minutes, hours, days). The method requires high density observations that span the period of rapidly changing competence. The level of explanatory power made possible by microgenetic analysis concerning the development of higher ordered thinking processes is unparalleled by other analytic techniques. However, the time-intensive nature of data collection and analysis using microgenetic techniques has, thus far, limited the generalizability of findings. To address this issue, we have begun to explore computational methods for supporting microgenetic analysis. Using the natural language processing approach of parts-of-speech tagging, we developed and implemented a new educational research method termed “Microgenetic Learning Analytics (MLA).” Results of research on student collaborative learning with educational robotics using the MLA method will be discussed.

Bio: Florence R. Sullivan is an associate professor of learning technology in the College of Education at UMass, Amherst. Dr. Sullivan received her Ph.D. in Cognitive Studies in Education from Teachers College, Columbia University. Her work focuses on student collaborative learning with computational media; with a special emphasis on girls’ learning and engagement with robotics. The aim of Dr. Sullivan’s research is to aid national efforts to improve the diversity of individual’s involved in computer science through educational interventions.

Francesco Orabona (Stony Brook University) will present “Coin Betting For Backprop Without Learning Rates And More” in the Machine Learning and Friends Lunch Thursday Oct. 5 from 12:00pm to 1:00pm in CS 150. Abstract and bio follow.

Abstract:

Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. In this talk, I will propose a new stochastic gradient descent procedure that does not require any learning rate setting. Contrary to previous methods, we do not adapt the learning rates nor we make use of the assumed curvature of the objective function. Instead, we reduce the optimization process to a game of betting on a non-stochastic coin and we propose an optimal strategy based on a generalization of Kelly betting. Moreover, this reduction can be also used for other machine learning problems. Theoretical convergence is proven for convex and quasi-convex functions and empirical evidence shows the advantage of our algorithm over popular stochastic gradient algorithms.

Bio:

Francesco Orabona is an Assistant Professor at Stony Brook University. His research interests are in the area of theoretically motivated and efficient machine learning algorithms, with emphasis on online and stochastic methods. He received the PhD degree in Electrical Engineering at the University of Genoa, in 2007. He is (co)author of more than 60 peer reviewed papers.

Nancy Jordan (University of Delaware) will present “Living on the number line:  Development of numerical magnitude understanding in children at risk for learning difficulties in mathematics” in the Cognitive Bag Lunch series, Wednesday at noon in Tobin 423. All are welcome!