Header logo is


2015


no image
Haptic Textures for Online Shopping

Culbertson, H., Kuchenbecker, K. J.

Interactive demonstrations in The Retail Collective exhibit, presented at the Dx3 Conference in Toronto, Canada, March 2015 (misc)

hi

[BibTex]

2015


[BibTex]


no image
Derivation of phenomenological expressions for transition matrix elements for electron-phonon scattering

Illg, C., Haag, M., Müller, B. Y., Czycholl, G., Fähnle, M.

2015 (misc)

mms

link (url) [BibTex]

2011


no image
Please \soutdo not touch the robot

Romano, J. M., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), San Francisco, California, sep 2011 (misc)

hi

[BibTex]

2011


[BibTex]


no image
Body-Grounded Tactile Actuators for Playback of Human Physical Contact

Stanley, A. A., Kuchenbecker, K. J.

Hands-on demonstration presented at IEEE World Haptics Conference, Istanbul, Turkey, June 2011 (misc)

hi

[BibTex]

[BibTex]


no image
Projected Newton-type methods in machine learning

Schmidt, M., Kim, D., Sra, S.

In Optimization for Machine Learning, pages: 305-330, MIT Press, Cambridge, MA, USA, 2011 (incollection)

Abstract
{We consider projected Newton-type methods for solving large-scale optimization problems arising in machine learning and related fields. We first introduce an algorithmic framework for projected Newton-type methods by reviewing a canonical projected (quasi-)Newton method. This method, while conceptually pleasing, has a high computation cost per iteration. Thus, we discuss two variants that are more scalable, namely, two-metric projection and inexact projection methods. Finally, we show how to apply the Newton-type framework to handle non-smooth objectives. Examples are provided throughout the chapter to illustrate machine learning applications of our framework.}

mms

link (url) [BibTex]

link (url) [BibTex]