Search Results for author:
Found 8 papers, 2 papers with code
MoNDE: Mixture of Near-Data Experts for Large-Scale Sparse Models
Mixture-of-Experts (MoE) large language models (LLM) have memory requirements that often exceed the GPU memory capacity, requiring costly parameter movement from secondary memories to the GPU for expert computation.
Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning
We believe that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications.
ComMU: Dataset for Combinatorial Music Generation
Commercial adoption of automatic music composition requires the capability of generating diverse and high-quality music suitable for the desired context (e. g., music for romantic movies, action games, restaurants, etc.).
UBoCo: Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection
Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events.
UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection
Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events.
Winning the CVPR'2021 Kinetics-GEBD Challenge: Contrastive Learning Approach
Generic Event Boundary Detection (GEBD) is a newly introduced task that aims to detect "general" event boundaries that correspond to natural human perception.
GradPIM: A Practical Processing-in-DRAM Architecture for Gradient Descent
In this paper, we present GradPIM, a processing-in-memory architecture which accelerates parameter updates of deep neural networks training.
Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques
Comparing the F1-scores, the features we created outperformed the features used for bot detection on Facebook and Twitter.
