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Exploring uncertainty in MT tasks with Conformal Prediction

As (large) language models find applications across an increasingly broad spectrum of tasks, the necessity for reliable confidence estimates—or uncertainty quantification (UQ)—on their predictions is critical. However, the selection of appropriate and efficient UQ methods presents a considerable challenge, particularly … Read More

From COMET to XCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection

Coronary X-ray angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease, which accounts for roughly 16% of global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, … Read More

Few-shot learning with transformers via graph embeddings for molecular property discovery

Molecular property prediction is an essential task in drug discovery. Recently, deep neural networks have accelerated the discovery of compounds with improved molecular profiles for effective drug development. In particular, graph neural networks (GNNs) have played a pivotal role in … Read More

Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation: Applications in Stenosis Detection

Coronary X-ray angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease, which accounts for roughly 16% of global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, … Read More

Towards End-to-end Speech-to-text Abstractive Summarization

Speech-to-text summarization is a time-saving technique used to filter and keep pace with the daily influx of broadcast news uploaded online. The emergence of powerful deep learning-based language models, boasting impressive text generation capabilities, has directed research attention towards summarization … Read More

Artificial Intelligence in Chest Radiography: Growing pains and Interpretability

Chest radiography is one of the most ubiquitous medical imaging modalities. Nevertheless, the interpretation of chest radiography images is time-consuming and complex meaning that the field is ripe for a takeover from artificial intelligence systems. The high image throughput has … Read More

Causal DiConStruct

Model interpretability plays a central role in human-AI decision-making systems. Ideally, explanations should be expressed through semantic concepts and their causal relations in an interpretable way for the human experts. Additionally, explanation methods should be efficient, and not compromise the … Read More

Scaling Laws for Multilingual Neural Machine Translation

In this talk, we provide a large-scale empirical study of the scaling properties of multilingual neural machine translation models. We examine how increases in the model size affect the model performance and investigate the role of the training mixture composition … Read More

Artificial evolution in the natural world

Evolutionary algorithms (EA) have proved as a robust optimisation meta-heuristic in engineering problems.  When we tackle problems in multi-agent systems, the nonlinear interactions set up an increasingly challenging context.  And if the multi-agents are groups of animals interacting with artificial … Read More

Model-Value Self-Consistent Updates and Applications

Learned models of the environment provide reinforcement learning agents with flexible ways of making predictions about the environment. Models enable planning, i.e. using more computation to improve value functions or policies, without requiring additional environment interactions. In this talk, we … Read More