Sweeping AAAI’25 success

We have been fortunate to have 3 papers accepted at AAAI’25.

Hung and colleagues will present their work on explainability of time series classification. InteDisUX aims to create explanations that are accessible and meaningful to users (real people) by identifying subsequences of the time series that provide positive or negative influence on a prediction. It uses a segment-level integrated gradient to merge successive segments into variable-length segments with high faithfulness and robustness. Follow the paper here: https://pure.qub.ac.uk/en/publications/intedisux-intepretation-guided-discriminative-user-centric-explan or come visit Hung at poster #8580. This work is funded by the MSCA-DN network RELAX.

Zichi and colleagues will present their work on WaveletMixer, a new time series forecasting method that leverages wavelets to create a latent representation at multiple levels of resolution and phases. It creates distinct forecasting models for each resolution, where the relationships between different frequency domains are exploited to update each of the models. Zichi also introduces a new MLP model for timeseries forecasting that works well in this setting. Follow the paper here: https://pure.qub.ac.uk/en/publications/waveletmixer-a-multi-resolution-wavelets-based-mlp-mixer-for-mult or come visit Zich at poster #10198. Zichi is supported by a scholarship from the China Scholarship Council.

Kazi Hasan Ibn Arif is a PhD student at Virginia Tech who we collaborate with through the US-Ireland project ‘SWEET’ (USI-226). Kazi has developed a new technique to improve the computational efficiency of high-resolution Vision-Language Models. A VLM combines two models, one to generate language tokens from the image, followed by a large language model. The technique uses attention in the token generation model to selectively drop tokens according to predefined budgets. The paper is on arxiv: https://arxiv.org/abs/2408.10945. Come visit Kazi at poster #7547.