Search Results for author:
Found 6 papers, 3 papers with code
AI capabilities can be significantly improved without expensive retraining
State-of-the-art AI systems can be significantly improved without expensive retraining via "post-training enhancements"-techniques applied after initial training like fine-tuning the system to use a web browser.
Overthinking the Truth: Understanding how Language Models Process False Demonstrations
The first phenomenon, overthinking, appears when we decode predictions from intermediate layers, given correct vs. incorrect few-shot demonstrations.
Auditing Visualizations: Transparency Methods Struggle to Detect Anomalous Behavior
Model visualizations provide information that outputs alone might miss.
Grounding Representation Similarity Through Statistical Testing
To understand neural network behavior, recent works quantitatively compare different networks' learned representations using canonical correlation analysis (CCA), centered kernel alignment (CKA), and other dissimilarity measures.
Grounding Representation Similarity with Statistical Testing
To understand neural network behavior, recent works quantitatively compare different networks' learned representations using canonical correlation analysis (CCA), centered kernel alignment (CKA), and other dissimilarity measures.
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference
In this contribution, we propose a new computationally efficient method to combine Variational Inference (VI) with Markov Chain Monte Carlo (MCMC).
