Computer Science, Computer Vision and Pattern Recognition
Tag: transformers
Page 1/6
Computer Science, Machine Learning
Optimizing Model Robustness through Dropout and Regularization
Computation and Language, Computer Science
Introducing Bode: A Fine-Tuned Large Language Model for Portuguese Prompt-Based Task.
Computer Science, Computer Vision and Pattern Recognition
Mlp-mixer: An All-Mlp Architecture for Vision
Computer Science, Machine Learning
Formal Verification of Machine Learning Models: A Comprehensive Review
Computer Science, Hardware Architecture
Optimizing Deep Neural Networks with Efficient Dataflow and Scalable Computation
Unlocking Localization at Internet Scale: A Batched Contrastive Approach
Computer Science, Machine Learning
Novel Approach to Time Series Imputation: Addressing Missing Data Complexities in Healthcare
Computer Science, Computer Vision and Pattern Recognition
Deep Learning Networks: A Comprehensive Overview
Electrical Engineering and Systems Science, Image and Video Processing
Improved Deep Retinopathy Detection via Self-Supervised Learning: A Comparative Study
Computer Science, Computer Vision and Pattern Recognition
Elegant and Effective Subject-Driven Text-to-Image Generation
Computer Science, Computer Vision and Pattern Recognition
Unlocking Image Understanding at Scale: Transformers for Image Recognition
Computer Science, Computer Vision and Pattern Recognition
Efficient and Robust Image Recognition via Saliency-Guided Transformers
Computer Science, Machine Learning
Adapting Word Embeddings for Molecular Information Retrieval Tasks
Electrical Engineering and Systems Science, Image and Video Processing
Improving Semantic Segmentation of CBCT Scans via Multi-task Learning and Image Reconstruction
Computer Science, Hardware Architecture
Optimizing Transformer Models for Efficient and Scalable Performance
Computer Science, Computer Vision and Pattern Recognition
Enhancing Data Efficiency in Monocular Depth Estimation with Scene Adaptation and Adapters
Computation and Language, Computer Science
Transformers Efficient and Affordable: Learned Token Pruning, Quantization, and Distillation
Computer Science, Software Engineering
Optimized Representation of Code Lines through Attention-based Encoding
Computer Science, Computer Vision and Pattern Recognition