2.4k citations · 3.1k across the 11 of their papers we have counts for
13 papers
FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework
Mario Rodríguez Béjar, B. Romera-Paredes, Jose L. Hernández-Ramos
Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initialization and sampling variance, whic…
Automated near-term quantum algorithm discovery for molecular ground states
Fabian Finger, Frederic Rapp, Pranav Kalidindi +10
Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform…
Quantum Circuit Optimization with AlphaTensor
Francisco J. R. Ruiz, Tuomas Laakkonen, Johannes Bausch +10
A key challenge in realizing fault-tolerant quantum computers is circuit optimization. Focusing on the most expensive gates in fault-tolerant quantum computation (namely, the T gat…
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein +5
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest,…
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch +26
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manua…
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer +6
Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer…
Prototypical Priors: From Improving Classification to Zero-Shot Learning
Saumya Jetley, Bernardino Romera-Paredes, Sadeep Jayasumana +1
Recent works on zero-shot learning make use of side information such as visual attributes or natural language semantics to define the relations between output visual classes and th…
Recurrent Instance Segmentation
Bernardino Romera-Paredes, Philip H. S. Torr
Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image. Current instance segmentation approaches consist of ensemb…
The Benefit of Multitask Representation Learning
Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-lea…
Conditional Random Fields as Recurrent Neural Networks
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes +5
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learnin…
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning
Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
From concentration inequalities for the suprema of Gaussian or Rademacher processes an inequality is derived. It is applied to sharpen existing and to derive novel bounds on the em…
A New Convex Relaxation for Tensor Completion
Bernardino Romera-Paredes, Massimiliano Pontil
We study the problem of learning a tensor from a set of linear measurements. A prominent methodology for this problem is based on a generalization of trace norm regularization, whi…
Sparse coding for multitask and transfer learning
Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
We investigate the use of sparse coding and dictionary learning in the context of multitask and transfer learning. The central assumption of our learning method is that the tasks p…