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Found 8 papers, 3 papers with code
Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation
The clinical utility of deep learning models for medical image segmentation is severely constrained by their inability to generalize to unseen domains.
The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
To improve the training efficiency of federated learning (FL), previous research has employed low-rank decomposition techniques to reduce communication overhead.
Mixed-Precision Embeddings for Large-Scale Recommendation Models
Specifically, to reduce the size of the search space, we first group features by frequency and then search precision for each feature group.
Neural refractive index field: Unlocking the Potential of Background-oriented Schlieren Tomography in Volumetric Flow Visualization
Background-oriented Schlieren tomography (BOST) is a prevalent method for visualizing intricate turbulent flows, valued for its ease of implementation and capacity to capture three-dimensional distributions of a multitude of flow parameters.
Masked Random Noise for Communication Efficient Federated Learning
For this purpose, we propose Federated Masked Random Noise (FedMRN), a novel framework that enables clients to learn a 1-bit mask for each model parameter and apply masked random noise (i. e., the Hadamard product of random noise and masks) to represent model updates.
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
To this end, we propose Federated Binarization-Aware Training (FedBAT), a novel framework that directly learns binary model updates during the local training process, thus inherently reducing the approximation errors.
Statistically consistent term structures have affine geometry
From a practical perspective, this requires that the chosen set of possible yield curves is compatible with any obtained diffusion coefficient.
Weighted Laplacian and Its Theoretical Applications
In this paper, we develop a novel weighted Laplacian method, which is partially inspired by the theory of graph Laplacian, to study recent popular graph problems, such as multilevel graph partitioning and balanced minimum cut problem, in a more convenient manner.
