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Found 9 papers, 4 papers with code
Robust Deep Learning Based Sentiment Classification of Code-Mixed Text
This embedding is further used in the proposed deep learning based architecture for robust classification.
ContextGNN: Beyond Two-Tower Recommendation Systems
Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings.
Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections
To the best of our knowledge, we are the first to successfully tackle the challenging problem of generating controlled and faithful mirror reflections of an object in a scene using diffusion based models.
Reproducibility Study of CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
(3) We try to verify the effectiveness of the gradient-alignment training method specified in the original paper, which is used to update the network parameters and pseudo labels.
Contextual Code Switching for Machine Translation using Language Models
We posit that the efficacy of multilingual large language models in contextual code switching is constrained by their training methodologies.
Carbon Emission Prediction on the World Bank Dataset for Canada
Machine learning is one of the most advanced and efficient techniques for predicting the amount of carbon emissions from current data.
Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis
In this paper, we aim to elicit, explain, and evaluate, qualitatively and quantitatively, major advancements in deep learning methods aimed at detecting or localizing community-acquired pneumonia (CAP), viral pneumonia, and covid-19 from images of chest X-rays and CT scans.
SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods
Further, the performance of CPU-GPU algorithms are compared with the GPU-only implementations to illustrate the significantly lower memory requirements.
Mathematical Software 65F10, 65F50, 65N55, 65Y05
Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images
Current analysis of tumor proliferation, the most salient prognostic biomarker for invasive breast cancer, is limited to subjective mitosis counting by pathologists in localized regions of tissue images.
