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20242026
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cs.LG2026

Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation

Anastasis Kratsios, Simone Brugiapaglia, Bum Jun Kim +2

Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily capture…

cs.LG2026

k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS

Jonas De Schouwer, Haitz Sáez de Ocáriz Borde, Xiaowen Dong

Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties in modeling long-range dependencies. Ho…

cs.LG2026

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement

Huidong Liang, Haitz Sáez de Ocáriz Borde, Baskaran Sripathmanathan +2

Long-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks, offering limited ins…

cs.LG2026

Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning

Haitz Sáez de Ocáriz Borde, Artem Lukoianov, Anastasis Kratsios +2

We propose Scalable Message Passing Neural Networks (SMPNNs) and demonstrate that, by integrating standard convolutional message passing into a Pre-Layer Normalization Transformer-…

cs.LG2025

Beyond Parallelism: Synergistic Computational Graph Effects in Multi-Head Attention

Haitz Sáez de Ocáriz Borde

Multi-head attention powers Transformer networks, the primary deep learning architecture behind the success of large language models (LLMs). Yet, the theoretical advantages of mult…

cs.LG2025

Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscaling

Carlo Saccardi, Maximilian Pierzyna, Haitz Sáez de Ocáriz Borde +6

Kilometer-scale weather data is crucial for real-world applications but remains computationally intensive to produce using traditional weather simulations. An emerging solution is…