Search Results for author:
Found 6 papers, 5 papers with code
Neural Networks Learn Statistics of Increasing Complexity
The distributional simplicity bias (DSB) posits that neural networks learn low-order moments of the data distribution first, before moving on to higher-order correlations.
Eliciting Latent Knowledge from Quirky Language Models
Eliciting Latent Knowledge (ELK) aims to find patterns in a capable neural network's activations that robustly track the true state of the world, especially in hard-to-verify cases where the model's output is untrusted.
Representation Engineering: A Top-Down Approach to AI Transparency
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience.
Ranked #3 on
Question Answering
on TruthfulQA
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge.
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data
In many scenarios, it is necessary to monitor a complex system via a time-series of observations and determine when anomalous exogenous events have occurred so that relevant actions can be taken.
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties
Probabilistic forecasting of complex phenomena is paramount to various scientific disciplines and applications.
