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Found 14 papers, 5 papers with code
Town Hall Debate Prompting: Enhancing Logical Reasoning in LLMs through Multi-Persona Interaction
Debate is a commonly used form of human communication catered towards problem-solving because of its efficiency.
Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting
We find that Ground Truth prompting results in a 55\% performance increase on GSM8K for a distilled Llama 3. 1 8B Instruct compared to the same model distilled without prompting.
Turkey's Earthquakes: Damage Prediction and Feature Significance Using A Multivariate Analysis
Accurate damage prediction is crucial for disaster preparedness and response strategies, particularly given the frequent earthquakes in Turkey.
A Debate-Driven Experiment on LLM Hallucinations and Accuracy
Large language models (LLMs) have achieved a degree of success in generating coherent and contextually relevant text, yet they remain prone to a significant challenge known as hallucination: producing information that is not substantiated by the input or external knowledge.
Medical Imaging Complexity and its Effects on GAN Performance
The proliferation of machine learning models in diverse clinical applications has led to a growing need for high-fidelity, medical image training data.
Time-series Crime Prediction Across the United States Based on Socioeconomic and Political Factors
Traditional crime prediction techniques are slow and inefficient when generating predictions as crime increases rapidly \cite{r15}.
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection
Preoperative Magnetic Resonance Imaging (MRI) images are often ineffective during surgery due to factors such as brain shift, which alters the position of brain structures and tumors.
Enhancing Depression Diagnosis with Chain-of-Thought Prompting
When using AI to detect signs of depressive disorder, AI models habitually draw preemptive conclusions.
Rethinking the Hyperparameters for Fine-tuning
Our findings challenge common practices of fine-tuning and encourages deep learning practitioners to rethink the hyperparameters for fine-tuning.
Toward Understanding Catastrophic Forgetting in Continual Learning
As an application, we apply our procedure to study two properties of a task sequence: (1) total complexity and (2) sequential heterogeneity.
Task2Vec: Task Embedding for Meta-Learning
We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks (e. g., tasks based on classifying different types of plants are similar) We also demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a new task.
Unsupervised Video Summarization With Adversarial LSTM Networks
The summarizer is the autoencoder long short-term memory network (LSTM) aimed at, first, selecting video frames, and then decoding the obtained summarization for reconstructing the input video.
Fine-Grained Recognition as HSnet Search for Informative Image Parts
This motivates us to formulate our problem as a sequential search for informative parts over a deep feature map produced by a deep Convolutional Neural Network (CNN).
HC-Search for Structured Prediction in Computer Vision
The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function.
