Search Results for author: Sethuraman Panchanathan

Found 10 papers, 3 papers with code

Representation, Exploration and Recommendation of Music Playlists

no code implementations1 Jul 2019 Piyush Papreja, Hemanth Venkateswara, Sethuraman Panchanathan

Playlists have become a significant part of our listening experience because of the digital cloud-based services such as Spotify, Pandora, Apple Music.

Music Recommendation Sentence +1

Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation

no code implementations23 Jun 2017 Hemanth Venkateswara, Shayok Chakraborty, Troy McDaniel, Sethuraman Panchanathan

To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data.

General Classification Model Selection +1

Multiresolution Match Kernels for Gesture Video Classification

no code implementations23 Jun 2017 Hemanth Venkateswara, Vineeth N. Balasubramanian, Prasanth Lade, Sethuraman Panchanathan

The emergence of depth imaging technologies like the Microsoft Kinect has renewed interest in computational methods for gesture classification based on videos.

Classification General Classification +1

Coupled Support Vector Machines for Supervised Domain Adaptation

no code implementations22 Jun 2017 Hemanth Venkateswara, Prasanth Lade, Jieping Ye, Sethuraman Panchanathan

Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data.

Domain Adaptation

Nonlinear Embedding Transform for Unsupervised Domain Adaptation

no code implementations22 Jun 2017 Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan

The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions.

Unsupervised Domain Adaptation

Deep Hashing Network for Unsupervised Domain Adaptation

7 code implementations CVPR 2017 Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, Sethuraman Panchanathan

Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain.

Deep Hashing Transfer Learning +1

A Strategy for an Uncompromising Incremental Learner

1 code implementation2 May 2017 Ragav Venkatesan, Hemanth Venkateswara, Sethuraman Panchanathan, Baoxin Li

Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting.

Class Incremental Learning Incremental Learning +1

A Two-Stage Weighting Framework for Multi-Source Domain Adaptation

no code implementations NeurIPS 2011 Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye

In this paper we propose a two-stage domain adaptation methodology which combines weighted data from multiple sources based on marginal probability differences (first stage) as well as conditional probability differences (second stage), with the target domain data.

Domain Adaptation Vocal Bursts Valence Prediction

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