Computer Science, Computer Vision and Pattern Recognition
Author: LLama 2 7B Chat
Page 42/179
LLaMA-2, the next generation of LLaMA. Meta trained and released LLaMA-2 in three model sizes: 7, 13, and 70 billion parameters. The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. The accompanying preprint also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets.
Computer Science, Machine Learning
Unlocking Chemical Innovation with Machine Learning: A Review of Recent Advances
Computer Science, Computer Vision and Pattern Recognition
Color Constancy Methods for Digital Photography: A Comparative Study
Computer Science, Software Engineering
Optimizing Code Generation with Standardized Prompts: A Key to High-Quality Output
Computer Science, Computer Vision and Pattern Recognition
Enhancing Confidence Calibration in Semantic Segmentation with Refinement Networks
Mathematics, Numerical Analysis
Efficient Solution of Nonlinear Eigenvalue Problems via Parallel SVD and Recursive Integral Methods
Computer Science, Machine Learning
Enhancing Adversarial Transferability via Lipschitz Regularization: A Novel Approach
Computer Science, Cryptography and Security
Enhancing Named Entity Recognition with Deep Learning Techniques
Computer Science, Computers and Society
Advances in Generative Agents Driven by Large Language Models: A Survey
Electrical Engineering and Systems Science, Image and Video Processing
Adversarial Strategy-Based Hyperspectral Unmixing: A Novel Approach to Simulating Suitable Mixed Pixels
Distributionally Robust Learning: A Comprehensive Survey
Computer Science, Machine Learning
Improving Robustness in Deep Learning Models: A Generated Data-based Approach
Computational Complexity, Computer Science
Solving Games with Imperfect Information: A Comprehensive Review
Time-Contrastive Networks: Self-Supervised Learning from Video
Computer Science, Machine Learning
Data Augmentation Techniques for Improved Machine Learning Models
Recognizing Underwater Acoustic Signals with Multilevel Cascading and Anonymization
Computer Science, Data Structures and Algorithms
Pruning Rules in Flash-TB: Optimizing Preprocessing Time
Computer Science, Computer Vision and Pattern Recognition
Comparative Study of NeRF-Based Methods for 3D Reconstruction from a Single Image
Computer Science, Neural and Evolutionary Computing