Deep Learning
Toward Dynamic Non-Line-of-Sight Imaging with Mamba Enforced Temporal Consistency
Our method capitalizes on neighbouring transient frames to aggregate the target 3D hidden volume.
Reconstruct and Match: Out-of-Distribution Robustness via Topological Homogeneity Chaoqi Chen 1 Luyao T ang 2 Hui Huang 1 1 College of Computer Science and Software Engineering, Shenzhen University
Since deep learning models are usually deployed in non-stationary environments, it is imperative to improve their robustness to out-of-distribution (OOD) data.
EMVP: Embracing Visual Foundation Model for Visual Place Recognition with Centroid-Free Probing
Specifically, it achieves 93.9%, 96.5%, and 94.6% Recall@1 on the MSLS V alidation, Pitts250k-test, and SPED datasets, respectively, while saving 64.3% of trainable parameters compared with the existing SOT A PEFT method.
], and simulating complex circuits
In particular, we consider the sparse linear regression task, i.e., the data is generated from a
FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-A ware Aggregation
Personalization in federated learning has emerged as a crucial strategy to cater to diverse client-specific data characteristics.
NeurIPS24_QuadMamba (64).pdf
The module estimates the locality score of each token from their features, before adaptively partitioning tokens into window quadrants.
Factuality-Aware Alignment for Large Language Models
This makes SFT less factual as it trains on human-labeled data that may be novel to the LLM. Furthermore, reward functions used in standard RL often inadequately capture factuality and favor longer and more detailed responses, which inadvertently promote hallucination.