Computer Science, Computer Vision and Pattern Recognition
Author: LLama 2 7B Chat
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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.
Unsupervised Audio-Caption Alignment via Correspondence Learning
Computer Science, Machine Learning
Efficient Method for ML Model Accuracy Improvement in Non-IID Data Settings
Computer Science, Computer Vision and Pattern Recognition
Accelerating Space Debris Removal Efforts through Autonomous Robotic Servicing
High Energy Astrophysical Phenomena, Physics
Importance of Energy and Variability in Classification
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Exploring Different Active Learning Techniques for Improved Sequence Labeling
Balancing Tensor Train Decomposition Factors Through Regularization
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Augmenting Language Models with Negative Samples for Improved Generalization
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Positivity-Preserving Truncated Euler-Maruyama Method for Generalized Ait-Sahalia Model
Electrical Engineering and Systems Science, Signal Processing
Optimizing Grassmann Constellations for Efficient Data Transmission
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Deep Learning for Image Captioning: A Survey of Attacks and Defenses
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Learning Abstract Causal Generative Models for Higher-Level Concepts
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State-of-the-Art Object Detection Methods: A Comparative Analysis
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Nonrigid Registration-based Reparametrization Method for 3D Point Clouds
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Numerical Solutions of Linear Systems: A Comprehensive Review of Algorithms and Applications
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Comparing Lossy Compression Methods for Differentially Private Mechanisms
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Attention Mechanism for Point Cloud Processing: A Comprehensive Review
Computer Science, Computer Vision and Pattern Recognition
Out-of-Distribution Detection Methods: A Comprehensive Survey
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Exploring Adaptive Relative Scale Encoding Methods for Efficient Video Quality Assessment
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