arXiv:2510.25327v3 Announce Type: replace-cross Abstract: Real-time multimodal inference on resource-constrained edge devices is essential for applications such as autonomous driving, human-computer interaction, and mobile health. However, prior work often overlooks the tight coupling between sensing dynamics and model execution, as…
arXiv:2510.27004v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models improve transformer efficiency but lack a unified theoretical explanation, especially when both feed-forward and attention layers are allowed to specialize. To this end, we study the Mixture-of-Transformers (MoT), a tractable theoretical framework…
arXiv:2303.03272v2 Announce Type: replace Abstract: Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamental interest to get a better theoretical understanding of…
World Bank data show how the organization of programs influences political conflict — indicating a path to better aid delivery.
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.
arXiv:2503.13497v3 Announce Type: replace-cross Abstract: The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical applications. However, the generalisability and robustness of EEG-based ML models often hinge on the amount and…
arXiv:2508.13831v2 Announce Type: replace-cross Abstract: Functional data, i.e., smooth random functions observed over a continuous domain, are increasingly available in areas such as biomedical research, health informatics, and epidemiology. However, effective statistical analysis for functional data is often hindered by…
arXiv:2510.22951v2 Announce Type: replace Abstract: Deep neural networks using state space models as layers are well suited for long-range sequence tasks but can be challenging to compress after training. We use that regularizing the sum of Hankel singular values of…
arXiv:2407.16407v2 Announce Type: replace-cross Abstract: This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost functions are…
arXiv:2505.23724v3 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), are indispensable for efficiently customizing Large Language Models (LLMs). However, vanilla LoRA suffers from slow convergence speed and knowledge forgetting problems. Recent studies have leveraged the power…
