31 citations · 88 across the 22 of their papers we have counts for
6 papers · 2 filters
Universal In-Context Approximation By Prompting Fully Recurrent Models
Aleksandar Petrov, Tom A. Lamb, Alasdair Paren +2
Zero-shot and in-context learning enable solving tasks without model fine-tuning, making them essential for developing generative model solutions. Therefore, it is crucial to under…
Risks and Opportunities of Open-Source Generative AI
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen +22
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic chan…
Near to Mid-term Risks and Opportunities of Open-Source Generative AI
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen +21
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for thes…
Towards Certification of Uncertainty Calibration under Adversarial Attacks
Cornelius Emde, Francesco Pinto, Thomas Lukasiewicz +2
Since neural classifiers are known to be sensitive to adversarial perturbations that alter their accuracy, \textit{certification methods} have been developed to provide provable gu…
Prompting a Pretrained Transformer Can Be a Universal Approximator
Aleksandar Petrov, Philip H. S. Torr, Adel Bibi
Despite the widespread adoption of prompting, prompt tuning and prefix-tuning of transformer models, our theoretical understanding of these fine-tuning methods remains limited. A k…
Efficient Lifelong Model Evaluation in an Era of Rapid Progress
Ameya Prabhu, Vishaal Udandarao, Philip Torr +3
Standardized benchmarks drive progress in machine learning. However, with repeated testing, the risk of overfitting grows as algorithms over-exploit benchmark idiosyncrasies. In ou…