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Found 23 papers, 2 papers with code
Multimodal Fusion Learning with Dual Attention for Medical Imaging
We show that the multi-branch fusion attention of DRIFA learns enhanced representations for each modality, such as dermoscopy, pap smear, MRI, and CT-scan, whereas multimodal information fusion attention module learns more refined multimodal shared representations, improving the network's generalization across multiple tasks and enhancing overall performance.
Citation-Based Summarization of Landmark Judgments
Landmark judgments are of prime importance in the Common Law System because of their exceptional jurisprudence and frequent references in other judgments.
Human Centered AI for Indian Legal Text Analytics
Legal research is a crucial task in the practice of law.
Infusing Knowledge into Large Language Models with Contextual Prompts
Knowledge infusion is a promising method for enhancing Large Language Models for domain-specific NLP tasks rather than pre-training models over large data from scratch.
Deep dive into language traits of AI-generated Abstracts
Generative language models, such as ChatGPT, have garnered attention for their ability to generate human-like writing in various fields, including academic research.
UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations
We consider two prominent CF techniques, namely Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks.
Conformal Group Recommender System
The traditional models of group recommendation are designed to act like a black box with a strict focus on improving recommendation accuracy, and most often, they place the onus on the users to interpret recommendations.
A Bibliographic Study on Artificial Intelligence Research: Global Panorama and Indian Appearance
The trend is based on Country-wise publications, year-wise publications, topical terms in AI, top-cited articles, prominent authors, major institutions, involvement of industries in AI and Indian appearance.
Cross-domain Recommender Systems via Multimodal Domain Adaptation
Several approaches in the literature have been proposed to tackle the problem of data sparsity, among which cross-domain collaborative filtering (CDCF) has gained significant attention in the recent past.
Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization
We exploit the inherent characteristics of CF algorithms to assess the confidence level of individual ratings and propose a semi-supervised approach for rating augmentation based on self-training.
Personalized Summarization of Scientific Scholarly Texts
In this paper, we present a proposal for an unsupervised algorithm, P-Summ, that generates an extractive summary of scientific scholarly text to meet the personal knowledge needs of the user.
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data
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.
Inductive Conformal Recommender System
The conformal recommender system uses the experience of a user to output a set of recommendations, each associated with a precise confidence value.
A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data
By making use of hinge loss function we transfer the learnt codebook of the source domain to target.
OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas
A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout.
Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management
Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature.
Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare
Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems.
Emergency Department Optimization and Load Prediction in Hospitals
Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs).
Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition
Facial expression recognition from videos in the wild is a challenging task due to the lack of abundant labelled training data.
Ranked #7 on
Facial Expression Recognition (FER)
on Acted Facial Expressions In The Wild (AFEW)
(using extra training data)
Facial Expression Recognition
Facial Expression Recognition (FER)
Collaborative Filtering and Multi-Label Classification with Matrix Factorization
We extended the concept of matrix factorization for yet another important problem of machine learning namely multi-label classification which deals with the classification of data with multiple labels.
Block based Singular Value Decomposition approach to matrix factorization for recommender systems
With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems.
Committee Selection with Attribute Level Preferences
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.
Group Preserving Label Embedding for Multi-Label Classification
Multi-label learning is concerned with the classification of data with multiple class labels.
