Examining LLMs' capability to address mathematical problems, especially modular arithmetic.
― 7 min read
Zhenmei Shi
Exploring the importance of softmax in neural network performance and applications.
― 4 min read
A new method enhances attention mechanisms in language models for better performance.
― 6 min read
Exploring the fundamentals and applications of diffusion models in various fields.
― 5 min read
Exploring tensor attention and its impact on data processing in AI models.
― 4 min read
Examining why larger models struggle with in-context learning compared to smaller ones.
― 6 min read
Advancements in fine-tuning language models using innovative techniques.
― 6 min read
Current models struggle with spatial reasoning, relying more on text than images.
― 6 min read
Examining differential privacy and NTK regression to protect user data in AI.
― 6 min read
Exploring differential privacy to protect sensitive information in AI applications.
― 5 min read
Exploring how LLMs perform on composite tasks that combine simpler tasks.
― 7 min read
A new method enhances John ellipsoid computation while protecting sensitive data.
― 7 min read
SparseGPT improves the speed and efficiency of large language models through parameter pruning.
― 4 min read
A new approach enhances gradient calculations, improving transformer efficiency in machine learning.
― 4 min read
A method to enhance efficiency of language models with long text inputs.
― 5 min read
Exploring the capabilities and challenges of Transformer technology in understanding language.
― 6 min read
A closer look at how MHNs can enhance machine learning.
― 6 min read
A look at Mamba and State-Space Models in AI capabilities.
― 6 min read
Discover how tensor attention transforms AI language processing.
― 7 min read
New methods improve RoPE attention, speeding up AI computations significantly.
― 5 min read