DATT builds on a novel feedforward-feedback-adaptive control structure trained in simulation using reinforcement learning. When deployed on real hardware, DATT is augmented with a disturbance estimator using $\mathcal {L}_1$ adaptive control in closed-loop, without any fine-tuning.
DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields, including challenging scenarios where baselines completely fail.
The transcriptional response to genetic perturbation reveals fundamental insights into complex cellular systems. While current approaches have made progress in predicting genetic perturbation...
AAAI 2025 Workshop on Artificial Intelligence for Wireless Communications and Networking (AI4WCN) AI4WCN Philadelphia, Pennsylvania, USA Mar 03 2025 https://aaai2025-ai4wcn.webflow.io/ ai4wcn.workshop@gmail.com
The paper introduces QCircuitBench, a large-scale benchmarking dataset for AI-based quantum algorithm design with LLMs. It provides a systematic collection of 3 tasks (oracle construction, quantum algorithms design, random circuit synthesis), covering 23 algorithms, and 128,573 data points (i.e., specific instances of quantum algorithms tailored to a particular setting), across diverse problem ...
Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution. Despite being a...
Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. We propose MambaSL, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, guided by four TSC-specific hypotheses. To address benchmarking limitations ...
TL;DR: We propose a constrained fine-tuning method for continual learning in LLMs using SVD and effective rank to guide updates in subspaces spanned by low singular vectors, significantly reducing catastrophic forgetting and outperforming SOTA methods.
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