Search Results for author:
Found 21 papers, 9 papers with code
A Multi-Modal Deep Learning Based Approach for House Price Prediction
In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector.
Generating Faithful and Salient Text from Multimodal Data
In this paper, we develop a framework to generate faithful and salient text from mixed-modal data, which includes images and structured data ( represented in knowledge graphs or tables).
MapCoder: Multi-Agent Code Generation for Competitive Problem Solving
In this paper, we introduce a new approach to code generation tasks leveraging multi-agent prompting that uniquely replicates the full cycle of program synthesis as observed in human developers.
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Towards Detecting, Recognizing, and Parsing the Address Information from Bangla Signboard: A Deep Learning-based Approach
With the advent of deep learning-based methods, different sophisticated techniques have been proposed for text detection and text recognition from the natural scene.
The Word2vec Graph Model for Author Attribution and Genre Detection in Literary Analysis
By using these Word2vec graph based features, we perform classification to perform author attribution and genre detection tasks.
Understanding Social Structures from Contemporary Literary Fiction using Character Interaction Graph -- Half Century Chronology of Influential Bengali Writers
Social structures and real-world incidents often influence contemporary literary fiction.
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph.
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals.
Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning
In this paper, we propose a novel deep learning approach, Seismic Contrastive Graph Neural Network (SC-GNN), for highly accurate seismic intensity prediction using a small portion of initial seismic waveforms from a few seismic stations.
BSpell: A CNN-Blended BERT Based Bangla Spell Checker
A specialized BERT model named BSpell has been proposed in this paper targeted towards word for word correction in sentence level.
Unsupervised Space Partitioning for Nearest Neighbor Search
We achieve the above edge by formulating a multi-objective custom loss function that does not need ground truth labels to quantify the quality of a given data-space partition, making it entirely unsupervised.
Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language
Though there has been a large body of recent works in language modeling (LM) for high resource languages such as English and Chinese, the area is still unexplored for low resource languages like Bengali and Hindi.
DeepAltTrip: Top-k Alternative Itineraries for Trip Recommendation
In this paper, we propose a deep learning-based framework, called DeepAltTrip, that learns to recommend top-k alternative itineraries for given source and destination POIs.
Learning Indoor Layouts from Simple Point-Clouds
Reconstructing a layout of indoor spaces has been a crucial part of growing indoor location based services.
A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations
Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features.
BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data
Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities.
Boosting House Price Predictions using Geo-Spatial Network Embedding
Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.
Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks
Motivated by this, in this paper, we present a user study conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares the quality (as perceived by the users) of the alternative routes generated by four of the most popular existing approaches including the routes provided by Google Maps.
CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting
In this paper, we introduce a new variant of crowd counting problem, namely "Categorized Crowd Counting", that counts the number of people sitting and standing in a given image.
Keyword Aware Influential Community Search in Large Attributed Graphs
We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching with the query terms (words or phrases).
Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks
Being motivated by the success of CNNs in image classification, deep learning based approaches have been developed in this paper for detecting diseases and pests from rice plant images.
