Statistical Learning Theory as a Framework for the Philosophy of Induction (2008)
Gilbert Harman, Sanjeev Kulkarni
Lugosi 1996) is the basic theory behind contemporary machine learning and pattern recognition. We suggest that the theory provides an excellent framework for the philosophy of induction (see also...
Wu Min, Committee Profs, Bede Liu, Peter Ramadge, Sanjeev Kulkarni
� Multimedia data hiding (watermarking), digital rights management, and information security � Multimedia communication through network and wireless channels � Video and audio content analysis,...
Richard Radke, Sanjeev Kulkarni
This paper describes an integrated suite of Matlab tools designed for laboratory exercises in the introductory electrical engineering course at Princeton University. Our goal was to design intuitive...
Probabilistic coherence and proper scoring rules (2007)
Predd, Joel, Seiringer, Robert, Lieb, Elliott H., Osherson, Daniel, Poor, Vincent, Kulkarni, Sanjeev
We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem appears to...
Statistical Learning Theory as a Framework for the Philosophy of Induction (2007)
Gilbert Harman, Sanjeev Kulkarni
behind contemporary machine learning and data-mining. We suggest that the theory provides an excellent framework for philosophical thinking about inductive inference. 1 We begin by sketching certain...
Fisher III., John W., Cetin, Mujdat, Jaakkola, Tommi, Tsitsiklis, John, Verdu, Sergio, Kulkarni, Sanjeev, ...
This final report summarizes the research and activities under the ODDR&E MURI on Data Fusion in Large Arrays of Microsensors. The report reviews the intellectual themes and research concentration...
Efficiently synthesizing virtual video (2003)
Richard Radke, Peter Ramadge, Sanjeev Kulkarni, Tomio Echigo
Given a set of synchronized video sequences of a dynamic scene taken by different cameras, we address the problem of creating a virtual video of the scene from a novel viewpoint. A key aspect of our...
Using view interpolation for low bit-rate video (2001)
Richard Radke, Peter Ramadge, Sanjeev Kulkarni
The transmission and reconstruction of video in wireless multimedia poses a much more difficult problem than it does in a wired setting. There are three main issues that complicate matters:...
Estimating Correspondence in Digital Video (2001)
Richard Radke, Vitali Zagorodnov, Sanjeev Kulkarni, Peter J. Ramadge
This paper addresses several estimation problems involving correspondence in digital video. We present three cases, in order of increasing complexity: affine transformations, projective...
Estimating Correspondence in Digital Video (2001)
Richard Radke, Vitali Zagorodnov, Sanjeev Kulkarni, Peter J. Ramadge
This paper addresses several estimation problems involving correspondence in digital video. We present three cases, in order of increasing complexity: affine transformations, projective...
Output distribution of the Burrows–Wheeler transform (2000)
Karthik Visweswariah, Sanjeev Kulkarni, Sergio Verd
Abstract- The Burrows-Wheeler transform is a block-sorting algorithm which has been shown empir-ically to be useful in compressing text data. In this paper we study the output distribution of the...
Recursive propagation of correspondences with applications to the creation of virtual video (2000)
Richard Radke, Peter Ramadge, Sanjeev Kulkarni, Tomio Echigo, Shun-ichi Iisaku
4-2-1 Nukuikita-machi
Recursive propagation of correspondences with applications to the creation of virtual video (2000)
Richard Radke, Peter Ramadge, Sanjeev Kulkarni, Tomio Echigo
Shun-ichi lisaku
An enabling framework for master-worker applications on the computational grid (2000)
Jean-pierre Goux, Sanjeev Kulkarni, Jeff Linderoth, Michael Yoder
We describe MW-- a software framework that allows users to quickly and easily parallelize scientific computations using the masterworker paradigm on the computational grid. MW provides both a...
Recursive propagation of correspondences with applications to the creation of virtual video (2000)
Richard Radke, Peter Ramadge, Sanjeev Kulkarni, Tomio Echigo, Shun-ichi Iisaku
This paper is concerned with the efficient temporal propagation of correspondences between frames of two video sequences, an integral component of many video processing
An Enabling Framework for Master-Worker Applications on the Computational Grid (2000)
Jean-pierre Goux, Sanjeev Kulkarni, Jeff Linderoth, Michael Yoder
We describe MW -- a software framework that allows users to quickly and easily parallelize scientific computations using the master-worker paradigm on the computational grid. MW provides both a...
An enabling framework for master-worker applications on the computational grid (2000)
Jean-pierre Goux, Sanjeev Kulkarni, Jeff Linderoth, Michael Yoder
We describe MW – a software framework that allows users to quickly and easily parallelize scientific computations using the master-worker paradigm on the computational grid. MW provides both a...
Density estimation from an individual numerical sequence (1998)
Andrew B. Nobel, Gusztáv Morvai, Sanjeev Kulkarni
This paper considers estimation of a univariate density from an individual numerical sequence. It is assumed that (i) the limiting relative frequencies of the numerical se-quence are governed by an...
Source codes as random number generators (1998)
Karthik Visweswariah, Sanjeev Kulkarni, Sergio Verd
Abstract- The use of optimal variable-length source codes as optimal random bit generators is in-vestigated. We show in what sense source codes can be considered to be random bit generators. I.