Search Results for author: Manik Varma

Found 17 papers, 10 papers with code

Graph Regularized Encoder Training for Extreme Classification

no code implementations28 Feb 2024 Anshul Mittal, Shikhar Mohan, Deepak Saini, Suchith C. Prabhu, Jain jiao, Sumeet Agarwal, Soumen Chakrabarti, Purushottam Kar, Manik Varma

The paper notices that in these settings, it is much more effective to use graph data to regularize encoder training than to implement a GCN.

Classification TAG

Multi-modal Extreme Classification

1 code implementation CVPR 2022 Anshul Mittal, Kunal Dahiya, Shreya Malani, Janani Ramaswamy, Seba Kuruvilla, Jitendra Ajmera, Keng-hao Chang, Sumeet Agarwal, Purushottam Kar, Manik Varma

This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors.

Classification Product Recommendation

DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents

1 code implementation12 Nov 2021 Kunal Dahiya, Deepak Saini, Anshul Mittal, Ankush Shaw, Kushal Dave, Akshay Soni, Himanshu Jain, Sumeet Agarwal, Manik Varma

Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set.

Multi-Label Learning

DECAF: Deep Extreme Classification with Label Features

1 code implementation1 Aug 2021 Anshul Mittal, Kunal Dahiya, Sheshansh Agrawal, Deepak Saini, Sumeet Agarwal, Purushottam Kar, Manik Varma

This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.

Classification Extreme Multi-Label Classification +5

ECLARE: Extreme Classification with Label Graph Correlations

1 code implementation31 Jul 2021 Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar, Manik Varma

This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.

Classification Extreme Multi-Label Classification +7

Extreme Regression for Dynamic Search Advertising

no code implementations15 Jan 2020 Yashoteja Prabhu, Aditya Kusupati, Nilesh Gupta, Manik Varma

This paper also introduces a (3) new labelwise prediction algorithm in XReg useful for DSA and other recommendation tasks.


DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases

no code implementations25 Sep 2019 Kunal Dahiya, Anshul Mittal, Deepak Saini, Kushal Dave, Himanshu Jain, Sumeet Agarwal, Manik Varma

The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set.

Learning Word Embeddings Multi-Label Learning +2

One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification

1 code implementation6 Sep 2019 Dhrubojyoti Roy, Sangeeta Srivastava, Aditya Kusupati, Pranshu Jain, Manik Varma, Anish Arora

Edge sensing with micro-power pulse-Doppler radars is an emergent domain in monitoring and surveillance with several smart city applications.

Feature Engineering General Classification

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

1 code implementation NeurIPS 2018 Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma

FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters.

Action Classification Language Modelling +3

Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things

1 code implementation ICML 2017 Ashish Kumar, Saurabh Goyal, Manik Varma

This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash.

Action Classification BIG-bench Machine Learning

Sparse Local Embeddings for Extreme Multi-label Classification

no code implementations NeurIPS 2015 Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set.

Classification Extreme Multi-Label Classification +3

Locally Non-linear Embeddings for Extreme Multi-label Learning

no code implementations9 Jul 2015 Kush Bhatia, Himanshu Jain, Purushottam Kar, Prateek Jain, Manik Varma

Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hence the effective number of labels can be reduced by projecting the high dimensional label vectors onto a low dimensional linear subspace.

Extreme Multi-Label Classification General Classification +2

Multiple Kernel Learning and the SMO Algorithm

no code implementations NeurIPS 2010 Zhaonan Sun, Nawanol Ampornpunt, Manik Varma, S. V. N. Vishwanathan

Our objective is to train $p$-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm.

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