Blog(1)

• Website Clustering via Transformer Context Models

  Individual characteristics as age, gender etc. are often relevant features in tasks such as lifetime value or uplift modelling. Those variables are sufficiently good in describing some high-level heuristics about a population.

Research Areas(0)

Publications(19)

• LIFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning

  AuthorMinyoung Kim, Timothy Hospedales

  PublishedInternational Conference on Learning Representation (ICLR)

  Date2025-04-25

• Principled and Efficient Motif Finding for Structure Learning in Lifted Graphical Models

  AuthorEfthymia Tsamoura

  PublishedConference on Artificial Intelligence (AAAI)

  Date2023-02-03

• From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery

  AuthorEnrique Sanchez, Georgios Tzimiropoulos

  PublishedIEEE Transactions on Pattern Analysis and Machine Intelligence

  Date2023-01-04

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News(3)

• [Blog] A Graph Clustering-Based Resource Reservation Solution for Low-Latency Networks in Massive MIMO Systems

  The low-latency networks (LLNs), which have stringent latency requirements, play a crucial role in the digital transformation of network services in Industry 4.0.

• [Blog] Clustering-based Hard Negative Sampling for Supervised Contrastive Speaker Verification

  In speaker verification, contrastive learning is gaining popularity as an alternative to the traditionally used classification-based approaches.

• [Blog] Website Clustering via Transformer Context Models

  Individual characteristics as age, gender etc. are often relevant features in tasks such as lifetime value or uplift modelling. Those variables are sufficiently good in describing some high-level heuristics about a population.

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