Monotonicity is a common and often necessary assumption in biomedical research. In multiplex assays, biomarker expression is expected to have a monotonic association with disease outcome; similarly, in dose-finding studies, the probability of a response or toxicity outcome is expected to increase with dose.
Energy-based Models (EBMs) represent a crucial class of generative models in machine learning. While conceptually appealing due to their ability to model tractable unnormalized densities, EBMs are notoriously difficult to optimize in practice. By combining techniques from diffusion models and density-ratio estimation, Energy-based Diffusion Models (DiffEBMs) have emerged as a powerful modern solution.
In this talk we will explore a zero-player game based on an information isolation constraint. The dynamics of the game emerge from a “no-barber” selection principle that prohibits external structure. The aim is for the game to avoid impredictive-style inconsistencies. Motivated by the selection principle we will derive a “selected" trajectory in the game that consists of a second-order constrained maximum entropy production along the information geometry.
Kirsty Pringle - Software Sustainability Institute; EPCC, University of Edinburgh
Research Software Engineers (RSEs) collaborate with researchers to develop and maintain software, helping to embed best practices that improve reliability and reduce inefficiencies in research workflows.
As awareness grows of the environmental impact of computational research, a new specialism - Green RSE - is beginning to emerge.
Green RSEs integrate sustainability into software development, ensuring environmental considerations are addressed alongside performance and usability.
Abstract: Neural networks have shown remarkable performance across data domains, especially in regimes of increasing compute budgets. However, fundamental insights into how neural networks process information, share representations and traverse loss landscapes remain uncertain. In this work, we quantify the functional impact of distribution matching, facilitated by knowledge sharing mechanisms such as knowledge distillation, under student-teacher optimisation strategies.
This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.
If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/5dHfs6vJrrvTbqst5. Sign-up is possible from May 21 midday (12pm) until May 25 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by May 27 midday.
AthenaZero is a bimanual manipulator designed to maximize control authority while minimizing inertia. By utilizing quasi-direct drive actuation and transmission remotization techniques, the system achieves an effective endpoint mass comparable to that of a human. Trading off trajectory tracking stiffness as compared to conventional high-impedance manipulators, this architecture reduces reflected inertia by an order of magnitude.
Dr Anna Breger, Project Leader, University of Cambridge
In this talk we discuss the potential of non-invasive imaging and machine learning techniques for the reconstruction of degraded medieval music notation. Our examples include manuscripts and fragments that suffer from different kinds of degradations rendering parts of the notation illegible. Such degradations may happen due to chemical or physical damage, for example from iron-gall acidity or from deliberate erasure.
Dr Charles Emogor, Dept of Computer Science and Technology
Are you an early career researcher (ECR) thinking about applying for your first grant or fellowship but are not sure where to start?
If you are interested in learning more about effective grant writing and what makes a strong application then please join us for this half day workshop.
The dominant paradigm in language modeling—scaling next-token prediction with parametric knowledge storage—delivers impressive capabilities but also fundamental limitations: brittle factual memory, inefficient parameters, and myopic reasoning. Progress requires a shift toward external memory and architectures that reason globally before committing to tokens.
Positional encodings are essential for transformer-based language models to understand sequence order, yet their influence extends far beyond simple position tracking. This talk explores the landscape of positional encoding methods in LLMs and reveals surprising insights about how these architectural choices shape model behavior.
We begin with the fundamental challenge: why attention mechanisms require explicit positional information.
Hamiltonian Monte Carlo (HMC) and its variants are among the most widely used algorithms for sampling from probability distributions. Despite their popularity, quantitative convergence guarantees for unadjusted HMC remain limited, especially in divergences that provide strong relative-density control such as KL divergence and Rényi divergence. In this talk, we establish regularization properties for unadjusted HMC via one-shot couplings, which enable Wasserstein convergence guarantees to be upgraded to guarantees in KL and Rényi divergence.
This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.
If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/oKKFG78k4CrcE6JK6. Sign-up is possible from June 4 midday (12pm) until June 8 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by June 10 midday.
