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Found 13 papers, 0 papers with code
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy. Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification.
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and limitations for on-site deployment.
A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data
This algorithm takes advantage of edge computing for minimizing the load from the central server, where clients handle both the forward and backward propagation while sacrificing the overall train time to some extent.
An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank
As feature selection has been found to be effective for improving the accuracy of learning models in general, it is intriguing to investigate this process for learning-to-rank domain.
Feature Engineering in Learning-to-Rank for Community Question Answering Task
Thirdly, using our proposed concepts, we conduct an empirical investigation with different rank-learning algorithms, some of which have not been used so far in CQA domain.
UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input
The results ascertain the efficacy of our technique.
Plant Disease Detection using Region-Based Convolutional Neural Network
Deep Learning models are found to be very effective to automatically detect plant diseases from images of plants, thereby reducing the need for human specialists.
Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning
The performance of convolutional neural networks (CNN) depends heavily on their architectures.
Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps
We present a self-supervised learning method that employs Transformers for the first time for the task of outdoor localization using LiDAR data.
Crime Prediction using Machine Learning with a Novel Crime Dataset
This dataset is expected to serve as the foundation for crime incidence prediction systems for Bangladesh and other countries.
MangoLeafBD: A Comprehensive Image Dataset to Classify Diseased and Healthy Mango Leaves
Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango yield.
Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market
In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors.
DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing
In this research, a resource-aware dynamic task scheduling approach is proposed and implemented.
