arXiv:2604.02441v1 Announce Type: new Abstract: Hybrid state estimators that combine model-based Kalman filtering with learned components have shown promise on simulated data, yet their performance on real-world automotive data remains insufficient. In this work we present Adaptive Multi-modal KalmanNet (AM-KNet), an advancement of KalmanNet tailored to the multi-sensor autonomous driving setting. AM-KNet introduces sensor-specific measurement modules that enable the network to learn the distinct noise characteristics of radar, lidar, and camera independently. A hypernetwork with context modulation conditions […]

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arXiv:2602.05742v1 Announce Type: new Abstract: Weighted empirical risk minimization is a common approach to prediction under distribution drift. This article studies its out-of-sample prediction error under nonstationarity. We provide a general decomposition of the excess risk into a learning term and an error term associated with distribution drift, and prove oracle inequalities for the learning error under mixing conditions. The learning bound holds uniformly over arbitrary weight classes and accounts for the effective sample size induced by the […]

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arXiv:2602.03967v1 Announce Type: new Abstract: Principal Component Analysis (PCA) is a powerful and popular dimensionality reduction technique. However, due to its linear nature, it often fails to capture the complex underlying structure of real-world data. While Kernel PCA (kPCA) addresses non-linearity, it sacrifices interpretability and struggles with hyperparameter selection. In this paper, we propose a robust non-linear PCA framework that unifies the interpretability of PCA with the flexibility of neural networks. Our method parametrizes variable transformations via neural […]

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arXiv:2603.08734v1 Announce Type: new Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental computation in graph analytics, scientific simulation, and sparse deep learning workloads. However, the extreme irregularity of real-world sparse matrices prevents existing GPU-based methods from maintaining high Tensor Core utilization and stable throughput. We present textbf{RSH-SpMM}, a fine-grained row-structured hybrid SpMM framework designed to better align irregular sparsity with modern GPU execution pipelines. RSH-SpMM introduces adaptive row partitioning and employs the RS-Tile representation to expose Tensor-Core-efficient dense […]

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Monotone optimisation problems admit specialised global solvers such as the Polyblock Outer Approximation (POA) algorithm, but these methods typically require explicit objective and constraint functions. In many applications, these functions are only available through data, making POA difficult to apply directly. We introduce an algorithm-aware learning approach that integrates learned models into POA by directly predicting its projection primitive via the radial inverse, avoiding the costly bisection procedure used in standard POA. We propose Homogeneous-Monotone Radial Inverse (HM-RI) […]

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arXiv:2601.20901v1 Announce Type: new Abstract: With LLMs increasingly deployed in corporate data management, it is crucial to ensure that these models do not leak sensitive information. In the context of corporate data management, the concept of sensitivity awareness has been introduced, enabling LLMs to adhere to predefined access rights rules. However, it remains unclear how sensitivity awareness relates to established notions of privacy, such as differential privacy (DP), thereby making it difficult to deploy meaningfully in real-world applications. […]

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arXiv:2602.15899v1 Announce Type: new Abstract: We present SceneVGGT, a spatio-temporal 3D scene understanding framework that combines SLAM with semantic mapping for autonomous and assistive navigation. Built on VGGT, our method scales to long video streams via a sliding-window pipeline. We align local submaps using camera-pose transformations, enabling memory- and speed-efficient mapping while preserving geometric consistency. Semantics are lifted from 2D instance masks to 3D objects using the VGGT tracking head, maintaining temporally coherent identities for change detection. As […]

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After its release in August 2020, Deep Learning with PyTorch has been sitting on my shelf before I finally got a chance to read it during this winter break. It turned out to be the perfect easy-going reading material for a bit of productivity after the relaxing holidays. As promised last week, here are my thoughts.

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arXiv:2602.12338v1 Announce Type: new Abstract: Token Communications (TokenCom) has recently emerged as an effective new paradigm, where tokens are the unified units of multimodal communications and computations, enabling efficient digital semantic- and goal-oriented communications in future wireless networks. To establish a shared semantic latent space, the transmitters/receivers in TokenCom need to agree on an identical tokenizer model and codebook. To this end, an initial Tokenizer Agreement (TA) process is carried out in each communication episode, where the transmitter/receiver […]

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