A new Bayesian matrix factorization approach is explored for performance prediction in multilingual NLP, aiming to reduce the experimental burden of evaluating various language combinations. The approach outperforms state-of-the-art methods in NLP benchmarks like machine translation and cross-lingual entity linking. It also avoids hyperparameter tuning and provides uncertainty estimates over predictions. Why it matters: Accurate performance prediction methods accelerate multilingual NLP research by reducing computational costs and improving experimental efficiency, especially valuable for Arabic NLP tasks.
This paper introduces a mutually-regularized dual collaborative variational auto-encoder (MD-CVAE) for recommendation systems, addressing the limitations of user-oriented auto-encoders (UAEs) in handling sparse ratings and new items. MD-CVAE integrates item content and user ratings within a variational framework, regularizing UAE weights with item content to avoid non-optimal convergence. A symmetric inference strategy eliminates the need for retraining when introducing new items, enhancing efficiency in dynamic recommendation scenarios. Why it matters: The MD-CVAE approach offers a practical solution for improving recommendation accuracy and efficiency, especially in scenarios with data sparsity and frequent item updates, relevant to e-commerce and content platforms in the Middle East.
This paper introduces neural Bayes estimators for censored peaks-over-threshold models, enhancing computational efficiency in spatial extremal dependence modeling. The method uses data augmentation to encode censoring information in the neural network input, challenging traditional likelihood-based approaches. The estimators were applied to assess extreme particulate matter concentrations over Saudi Arabia, demonstrating efficacy in high-dimensional models. Why it matters: The research offers a computationally efficient alternative for environmental modeling and risk assessment in the region.
MBZUAI researchers collaborated with Carnegie Mellon University and the Broad Institute of MIT and Harvard to develop a new statistical method for analyzing data used for gene regulatory network inference. The method addresses the challenge of distinguishing true zero expression values from dropouts in single-cell RNA sequencing data. This research will be presented at the Twelfth International Conference on Learning Representations (ICLR 2024). Why it matters: Improving gene regulatory network inference can lead to better understanding of disease mechanisms and inform the development of new medicines.
This talk discusses the asymptotic study of large asymmetric spiked tensor models. It explores connections between these models and equivalent random matrices constructed through contractions of the original tensor. Mohamed El Amine Seddik, currently a senior researcher at TII in Abu Dhabi, presented the work. Why it matters: The research provides theoretical foundations relevant to machine learning algorithms that leverage low-rank tensor structures, potentially impacting AI research and applications in the region.
The paper introduces a novel method for short-term, high-resolution traffic prediction, modeling it as a matrix completion problem solved via block-coordinate descent. An ensemble learning approach is used to capture periodic patterns and reduce training error. The method is validated using both simulated and real-world traffic data from Abu Dhabi, demonstrating superior performance compared to other algorithms.
This paper introduces a unified deep autoregressive model (UAE) for cardinality estimation that learns joint data distributions from both data and query workloads. It uses differentiable progressive sampling with the Gumbel-Softmax trick to incorporate supervised query information into the deep autoregressive model. Experiments show UAE achieves better accuracy and efficiency compared to state-of-the-art methods.
A new mini-batch strategy using aggregated relational data is proposed to fit the mixed membership stochastic blockmodel (MMSB) to large networks. The method uses nodal information and stochastic gradients of bipartite graphs for scalable inference. The approach was applied to a citation network with over two million nodes and 25 million edges, capturing explainable structure. Why it matters: This research enables more efficient community detection in massive networks, which is crucial for analyzing complex relationships in various domains, but this article has no clear connection to the Middle East.