THE NOVI SAD Seminar

 

PROGRAM

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Predavanja možete pratiti i online putem MITEAM stranice Novosadskog seminara:
https://miteam.mi.sanu.ac.rs/asset/3iuT7dhKfDxFv5kh3

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Plan rada Novosadskog seminara za APRIL 2026

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Četvrtak, 09.04.2026. u 12:00, svečana sala Ogranka SANU u Novom Sadu, Nikole Pašića 6 i Online
Goce Trajčevski, Department of Electrical and Computer Engineering, Iowa State University, USA
ENHANCING URBAN REGION REPRESENTATION VIA ADAPTIVE RISK-AWARE CONSENSUS LEARNING
In a typical urban environment, there are various kinds of regions with distinct characteristics like, for example, residential neighborhoods, business districts, industrial zones, entertainment zones, etc. Each of these regions exhibits significant differences in terms of spatial structure, functional distribution and occupation frequency with inhabitants. In recent years, learning region representation with (deep) neural networks has become crucial for improving urban planning and influencing the development of smart cities. Accurately depicting the characteristics of urban regions can be used to facilitate various downstream tasks – e.g., crime prediction, traffic flow forecasting, socio-economic aspects estimation, etc. However, despite the notable achievements, the existing practices still face certain challenges: (1) When multiple views contain distinct semantic information, ignoring reliability and possibly inadequate collection differences (e.g., data missingness) among those views may degrade the representation robustness. (2) Consensus semantics extracted from different views are often fused in a simplistic manner, without considering the uniformity of embeddings (quality variations) and the complementarity between views.
In this talk, we will present our recent results targeting such challenges – based on a novel Adaptive Risk-aware Consensus Learning (ARC) solution for urban region embeddings. Specifically, we design both local- and region-level masking within the inter-view representation, following the paradigm of masked autoencoders, to better handle uncertainty risks. More importantly, we introduce a self-weighted contrastive mechanism in consensus learning to achieve maximum alignment and mitigate degradation. To enhance the uniformity of embeddings, we employ entropy, ensuring the diversity and complementarity of information. Ultimately, we apply the learned embeddings to down-stream tasks, demonstrating remarkable improvements compared to several representative baselines.
Zajednički sastanak Novosadskog i AI Seminara MISANU.

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