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Found 7 papers, 0 papers with code
Leveraging LLM Reasoning Enhances Personalized Recommender Systems
Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting.
The Extrapolation Power of Implicit Models
In this paper, we investigate the extrapolation capabilities of implicit deep learning models in handling unobserved data, where traditional deep neural networks may falter.
State-driven Implicit Modeling for Sparsity and Robustness in Neural Networks
Implicit models are a general class of learning models that forgo the hierarchical layer structure typical in neural networks and instead define the internal states based on an ``equilibrium'' equation, offering competitive performance and reduced memory consumption.
Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm
To generate a summary with $k$ sentences, the algorithm only needs to execute $\approx k$ iterations, making it very efficient.
Style Control for Schema-Guided Natural Language Generation
Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods.
Text Analytics for Resilience-Enabled Extreme Events Reconnaissance
Post-hazard reconnaissance for natural disasters (e. g., earthquakes) is important for understanding the performance of the built environment, speeding up the recovery, enhancing resilience and making informed decisions related to current and future hazards.
Implicit Deep Learning
Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks.
