no code implementations • 2 Jan 2023 • Prasad Bhavana, Vineet Padmanabhan
Matrix Factorization (MF) on large scale data takes substantial time on a Central Processing Unit (CPU).
no code implementations • 26 Mar 2022 • Sowmini Devi Veeramachaneni, Arun K Pujari, Vineet Padmanabhan, Vikas Kumar
In this paper, we come up with a novel transfer learning approach for cross-domain recommendation, wherein the cluster-level rating pattern(codebook) of the source domain is obtained via a co-clustering technique.
2 code implementations • 14 Dec 2021 • Akshay Badola, Cherian Roy, Vineet Padmanabhan, Rajendra Lal
Interpretability of Deep Neural Networks has become a major area of exploration.
no code implementations • 18 Sep 2021 • Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan, Vikas Kumar
The conformal recommender system uses the experience of a user to output a set of recommendations, each associated with a precise confidence value.
no code implementations • 2 Aug 2021 • Sowmini Devi Veeramachaneni, Arun K Pujari, Vineet Padmanabhan, Vikas Kumar
By making use of hinge loss function we transfer the learnt codebook of the source domain to target.
no code implementations • 17 Jul 2019 • Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan
With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems.
no code implementations • 29 Jan 2019 • Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan, Vikas Kumar
We describe a greedy approach for attribute aggregation that satisfies the first three properties, but not the fourth, i. e., compound justified representation, which we prove to be NP-complete.
no code implementations • 24 Dec 2018 • Vikas Kumar, Arun K Pujari, Vineet Padmanabhan, Venkateswara Rao Kagita
Multi-label learning is concerned with the classification of data with multiple class labels.
no code implementations • 15 Nov 2018 • Thomas Cherian, Akshay Badola, Vineet Padmanabhan
Long Short-Term Memory (LSTM) architecture solves the inadequacies of the standard RNN in modeling long-range contexts.