Search Results for author: Julien Monteil

Found 7 papers, 1 papers with code

Learning Visual Hierarchies with Hyperbolic Embeddings

no code implementations26 Nov 2024 Ziwei Wang, Sameera Ramasinghe, Chenchen Xu, Julien Monteil, Loris Bazzani, Thalaiyasingam Ajanthan

Experiments in part-based image retrieval show significant improvements in hierarchical retrieval tasks, demonstrating the capability of our model in capturing visual hierarchies.

Image Retrieval Retrieval

Geometric Collaborative Filtering with Convergence

no code implementations4 Oct 2024 Hisham Husain, Julien Monteil

We present a geometric upper bound that gives rise to loss functions, and a way to meaningfully utilize the geometry of item-metadata to improve recommendations.

Collaborative Filtering

Personalised Outfit Recommendation via History-aware Transformers

no code implementations29 Jun 2024 Myong Chol Jung, Julien Monteil, Philip Schulz, Volodymyr Vaskovych

We present the history-aware transformer (HAT), a transformer-based model that uses shoppers' purchase history to personalise outfit predictions.

On model selection for scalable time series forecasting in transport networks

no code implementations29 Nov 2019 Julien Monteil, Anton Dekusar, Claudio Gambella, Yassine Lassoued, Martin Mevissen

We discuss that modelling temporal and spatial features into deep learning predictors can be helpful for long-term predictions, while simpler, not deep learning-based predictors, achieve very satisfactory performance for link-based and short-term forecasting.

Clustering Deep Learning +5

Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting

1 code implementation22 Oct 2018 Philipp Haehnel, Jakub Marecek, Julien Monteil, Fearghal O'Donncha

Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs).

Deep Learning

Bayesian Classifier for Route Prediction with Markov Chains

no code implementations31 Aug 2018 Jonathan P. Epperlein, Julien Monteil, Ming-ming Liu, Yingqi Gu, Sergiy Zhuk, Robert Shorten

We present here a general framework and a specific algorithm for predicting the destination, route, or more generally a pattern, of an ongoing journey, building on the recent work of [Y. Lassoued, J. Monteil, Y. Gu, G. Russo, R. Shorten, and M. Mevissen, "Hidden Markov model for route and destination prediction," in IEEE International Conference on Intelligent Transportation Systems, 2017].

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