Search Results - "Dev, Bhisham"

Evaluation of Metarhizium rileyi Farlow (Samson) impregnated with azadirachtin and indoxacarb against Helicoverpa armigera (Hubner) by Dev, Bhisham, Verma, Subhash Chander, Sharma, Prem Lal, Chandel, Rajeshwar Singh, Gaikwad, Mahesh Balaso, Banshtu, Tanuja, Sharma, Priyanka

“…Background Entomopathogenic fungi are the most versatile having a wide host range, capable of infecting insects at different developmental stages. In the…”
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Dimensionality Reduction for Categorical Data by Bera, Debajyoti, Pratap, Rameshwar, Verma, Bhisham Dev

“…Categorical attributes are those that can take a discrete set of values, e.g., colours. This work is about compressing vectors over categorical attributes to…”
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Variance reduction in feature hashing using MLE and control variate method by Verma, Bhisham Dev, Pratap, Rameshwar, Thakur, Manoj

“…The feature hashing algorithm introduced by Weinberger et al. ( 2009 ) is a popular dimensionality reduction algorithm that compresses high dimensional data…”
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Unbiased estimation of inner product via higher order count sketch by Verma, Bhisham Dev, Pratap, Rameshwar, Thakur, Manoj

“…Count sketch [1] is one of the popular sketching algorithms widely used for frequency estimation in data streams, and pairwise inner product for real-valued…”
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Improving compressed matrix multiplication using control variate method by Verma, Bhisham Dev, Dubey, Punit Pankaj, Pratap, Rameshwar, Thakur, Manoj

“…The seminal work by Pagh [1] proposed a matrix multiplication algorithm for real-valued squared matrices called Compressed Matrix Multiplication (CMM) having a…”
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Sparsifying Count Sketch by Verma, Bhisham Dev, Pratap, Rameshwar, Dubey, Punit Pankaj

“…The seminal work of Charikar et al. [1] called Count-Sketch suggests a sketching algorithm for real-valued vectors that has been used in frequency estimation…”
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QUINT: Node Embedding Using Network Hashing by Bera, Debajyoti, Pratap, Rameshwar, Verma, Bhisham Dev, Sen, Biswadeep, Chakraborty, Tanmoy

“…Representation learning using network embedding has received tremendous attention due to its efficacy to solve downstream tasks. Popular embedding methods…”
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Parallel multi-agent real-coded genetic algorithm for large-scale black-box single-objective optimisation by Akopov, Andranik S., Beklaryan, Levon A., Thakur, Manoj, Verma, Bhisham Dev

“…There is an ongoing evolution involving a new approach to large-scale optimisations based on co-evolutionary searches using interacting heterogeneous…”
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Efficient binary embedding of categorical data using BinSketch by Verma, Bhisham Dev, Pratap, Rameshwar, Bera, Debajyoti

“…In this work, we present a dimensionality reduction algorithm, aka. sketching, for categorical datasets. Our proposed sketching algorithm Cabin constructs…”
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Improving LSH via Tensorized Random Projection by Verma, Bhisham Dev, Pratap, Rameshwar

“…Locality sensitive hashing (LSH) is a fundamental algorithmic toolkit used by data scientists for approximate nearest neighbour search problems that have been…”
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Improving \textit{Tug-of-War} sketch using Control-Variates method by Pratap, Rameshwar, Verma, Bhisham Dev, Kulkarni, Raghav

“…Computing space-efficient summary, or \textit{a.k.a. sketches}, of large data, is a central problem in the streaming algorithm. Such sketches are used to…”
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Dimensionality Reduction for Categorical Data by Bera, Debajyoti, Pratap, Rameshwar, Verma, Bhisham Dev

“…Categorical attributes are those that can take a discrete set of values, e.g., colours. This work is about compressing vectors over categorical attributes to…”
Get full text

Efficient Binary Embedding of Categorical Data using BinSketch by Verma, Bhisham Dev, Pratap, Rameshwar, Bera, Debajyoti

“…In this work, we present a dimensionality reduction algorithm, aka. sketching, for categorical datasets. Our proposed sketching algorithm Cabin constructs…”
Get full text

QUINT: Node embedding using network hashing by Bera, Debajyoti, Pratap, Rameshwar, Verma, Bhisham Dev, Sen, Biswadeep, Chakraborty, Tanmoy

“…Representation learning using network embedding has received tremendous attention due to its efficacy to solve downstream tasks. Popular embedding methods…”
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