arXiv ·
Researchers developed a data-driven toolkit for short-term traffic forecasting using high-resolution traffic data from urban road sensors. The method models forecasting as a matrix completion problem, mapping inputs to a higher-dimensional space using kernels and adaptive boosting. Validated using real-world data from Abu Dhabi, UAE, the method outperforms state-of-the-art algorithms.
MBZUAI ·
Emilio Porcu from Khalifa University presented on temporally evolving generalized networks, where graphs evolve over time with changing topologies. The presentation addressed challenges in building semi-metrics and isometric embeddings for these networks. The research uses kernel specification and network-based metrics and is illustrated using a traffic accident dataset. Why it matters: This work advances the application of kernel methods to dynamic graph structures, relevant for modeling evolving relationships in various domains.
MBZUAI ·
The article discusses the importance of sample correlations in computer graphics, vision, and machine learning, highlighting how tailored randomness can improve the efficiency of existing models. It covers various correlations studied in computer graphics and tools to characterize them, including the use of neural networks for developing different correlations. Gurprit Singh from the Max Planck Institute for Informatics will be presenting on the topic. Why it matters: Optimizing sampling techniques via understanding and applying correlations can lead to significant advancements and efficiency gains across multiple AI fields.
MBZUAI ·
MBZUAI researchers have developed a new kernel-based method to identify dependence patterns in data, especially in small regions exhibiting 'rare dependence' where relationships between variables differ. The method uses sample importance reweighting, assigning more importance to regions with rare dependence. Tested on synthetic and real-world data, the algorithm successfully identified relations between variables even with rare dependence, outperforming traditional methods like HSIC. Why it matters: This advancement can improve data analysis in fields like public health, economics, genomics, and AI, enabling more accurate insights from complex observational data.
MBZUAI ·
This article discusses methods for handling label noise in deep learning, including extracting confident examples and modeling label noise. Tongliang Liu from the University of Sydney presented these approaches. The talk aimed to provide participants with a basic understanding of learning with noisy labels. Why it matters: As AI models are increasingly trained on large, noisy datasets, techniques for robust learning become crucial for reliable real-world performance.
MBZUAI ·
A new neural network architecture called Orchid was introduced that uses adaptive convolutions to achieve quasilinear computational complexity O(N logN) for sequence modeling. Orchid adapts its convolution kernel dynamically based on the input sequence. Evaluations across language modeling and image classification show that Orchid outperforms attention-based architectures like BERT and Vision Transformers, often with smaller model sizes. Why it matters: Orchid extends the feasible sequence length beyond the practical limits of dense attention layers, representing progress toward more efficient and scalable deep learning models.
MBZUAI ·
An associate professor of Statistics at the University of Toronto gave a talk on how ensemble learning stabilizes and improves the generalization performance of an individual interpolator. The talk focused on bagged linear interpolators and introduced the multiplier-bootstrap-based bagged least square estimator. The multiplier bootstrap encompasses the classical bootstrap with replacement as a special case, along with a Bernoulli bootstrap variant. Why it matters: While the talk occurred at MBZUAI, the content is about ensemble learning which is a core area for improving AI model performance, and is of general interest to the AI research community.
MBZUAI ·
A presentation will demonstrate the construction of well-calibrated, distribution-free neural Temporal Point Process (TPP) models from multiple event sequences using conformal prediction. The method builds a distribution-free joint prediction region for event arrival time and type with a finite-sample coverage guarantee. The refined method is based on the highest density regions, derived from the joint predictive density of event arrival time and type to address the challenge of creating a joint prediction region for a bivariate response that includes both continuous and discrete data types. Why it matters: This research from a KAUST postdoc improves uncertainty quantification in neural TPPs, which are crucial for modeling continuous-time event sequences, with applications in various fields, by providing more reliable prediction regions.