(Theory) Pranjal Awasthi, Princeton: Learning Halfspaces With Noise
- Start time: 2:00pm
- End date: Monday, September 29th
- End time: 3:00pm
- Where: CSE 4258
Title: Learning Halfspaces with Noise
Speaker: Pranjal Awasthi (Princeton University)
Place: CSE 4258
Time: 2 PM, October 6th, 2014
Abstract: We study the problem of learning halfspaces in the malicious noise model of Valiant. In this model, an adversary can corrupt an η fraction of both the label part and the feature part of an example. We design a polynomial-time algorithm for learning halfspaces in R^d under the uniform distribution with near optimal noise tolerance.
Our results also imply the first active learning algorithm for learning halfspaces that can handle malicious noise.
Joint work with Nina Balcan and Phil Long.
Monday, October 6th
DLS Speaker: Paul Debevec
- Start time: 11:00am
- End date: Monday, October 6th
- End time: 12:00pm
- Where: CSE 1202
Title: Advances in Photoreal Digital Humans in Film and in Real-Time
Amazingly, we have entered an age where even the human actors in a movie can be created as computer generated imagery. Somewhere between "Final Fantasy" in 2001 and "The Curious Case of Benjamin Button" in 2008, digital actors crossed the "Uncanny Valley" from looking strangely synthetic to believably real. This talk describes how the Light Stage scanning systems and HDRI lighting techniques developed at the USC Institute for Creative Technologies have helped create digital actors in a wide range of recent films. For in‐depth examples, the talk describes how high‐resolution face scanning, advanced character rigging, and performance‐driven facial animation were combined to create "Digital Emily", a collaboration with Image Metrics (now Faceware) yielding one of the first photoreal digital actors, and 2013’s “Digital Ira”, a collaboration with Activision Inc., yielding the most realistic real‐time digital actor to date. The talk includes recent developments in HDRI lighting, polarization difference imaging, and skin reflectance measurement, 3D object scanning, and concludes with advances in autostereoscopic 3D displays enabling 3D teleconferencing, holographic characters, and cultural preservation.
Paul Debevec is a Research Professor in the University of Southern California’s Viterbi School of Engineering and the Chief Visual Officer at USC's Institute for Creative Technologies where he leads the Graphics Laboratory. Since his 1996 UC Berkeley Ph.D. Thesis, Paul has helped develop data‐driven techniques for photorealistic computer graphics including image‐based modeling and rendering, high dynamic range imaging, image‐based lighting, appearance capture, and 3D displays. His short films, including The Campanile Movie, Rendering with Natural Light and Fiat Lux provided early examples of the virtual cinematography and HDR lighting techniques seen in The Matrix trilogy and have become standard practice in visual effects. Debevec’s Light Stage systems for photoreal facial scanning have contributed to groundbreaking digital character work in movies such as Spider‐Man 2, Superman Returns, The Curious Case of Benjamin Button, Avatar, The Avengers, Oblivion, Gravity, and Maleficent and earned him and his colleagues a 2010 Scientific and Engineering Award from the Academy of Motion Picture Arts and Sciences (AMPAS). He currently serves as Co-Chair of the AMPAS Science and Technology Council and is also a member of the Visual Effects Society. http://www.pauldebevec.com/
Tuesday, October 28th
CNS Lecture: "Resource Virtualization for Software-defined Networks"
- Start time: 11:00am
- End date: Tuesday, October 28th
- End time: 12:00pm
- Where: CSE Room 1202
Title: Resource Virtualization for Software-defined Networks
Abstract: Software defined networking centralizes control plane functionality, separating it from the data plane which is responsible for packet forwarding. Many management tasks such as finding heavy hitters for multi-path routing may run using SDN in a network with limited resources. However, by abstracting them from resources at individual switches, a resource manager at the controller can optimize their resource usage. As management tasks often have a measurement-control loop, my projects, DREAM and vCRIB, work on measurement and control tasks, respectively: First, Dream ensures a minimum user-specified level of accuracy for tasks instead of allocating a fixed amount of resources to each task. Therefore, it dynamically allocates resources across tasks in reaction to traffic dynamics and task dynamics, which allows resource multiplexing. DREAM is 2x better at the tail of minimum accuracy satisfaction comparing to current practice even in cases with moderate load. Next, vCRIB automatically distributes control rules on all switches in the network giving the abstraction of a centralized rule repository with resources equal to the combined resources of all switches. vCRIB can find feasible rule placement with less than 10% traffic overhead in cases where traffic-optimal rule placement is not feasible with respect to CPU and memory constraints.
Bio: Masoud Moshref is a 5th year PhD candidate in University of Southern California. He works on resource virtualization in Software-Defined Networks in Networked Systems Lab under supervision of Ramesh Govindan and Minlan Yu. He got MSc and BSc in Information Technology Engineering from Sharif University of Technology in Iran.