Computer Science, Human-Computer Interaction
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.
Computer Science, Machine Learning
Uncovering User Behavior Insights through Clustering and Feature Analysis of Mobile GPS Trajectories
Computer Science, Computer Vision and Pattern Recognition
Unlocking Vision Transformers’ Potential with Attention-Aware CCA
Computer Science, Software Engineering
Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees
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Improving Video Retrieval via Linking Characters to Visual Representations
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Deep Learning for Poverty Mapping: A Comprehensive Review
Computer Science, Logic in Computer Science
Efficient Simulation-Based Logic Synthesis with STP and SAT-Sweeping
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Efficient Eye-Tracking via Low-Power Neuromorphic Approach
Bounds on Generalization Gap for Stochastic Optimization with Heavy-Tailed Noise
Towards 3-Dimensional Rewriting Theory: A Concisely Formulated Article
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Debiasing Language Models: A Review of Techniques and Challenges
Computer Science, Computer Vision and Pattern Recognition
Processing Captions to Enhance Image Captioning
Computer Science, Multiagent Systems
Reaching Consensus in Group Decision Making: A Moderator’s Perspective
Computer Science, Computer Vision and Pattern Recognition
“Inventing Surfboards: A Historical Journey
Computer Science, Computer Vision and Pattern Recognition
Neural Radiance Fields for View Synthesis: A Comprehensive Review
Computer Science, Computer Vision and Pattern Recognition
Enhancing Referring Expression Generation with Multimodal Fusion and Visual Guidance
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Enhancing Hyperdimensional Coding with Adaptive Training for Improved MNIST Recognition
Artificial Intelligence, Computer Science
Intrinsically Motivated Exploration of Novelty in Evolutionary Systems
Deep Trajectory Clustering with Autoencoders
Computer Science, Formal Languages and Automata Theory