Statistical Learning
Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure
Additionally, convolutions are executed on nodes and edges simultaneously to generate comprehensive protein embeddings.
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.
Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms
We introduce Non-Euc lidean-MDS (Neuc-MDS), an extension of classical Multidimensional Scaling (MDS) that accommodates non-Euclidean and non-metric inputs. The main idea is to generalize the standard inner product to symmetric bilinear forms to utilize the negative eigenvalues of dissimilarity Gram matrices. Neuc-MDS efficiently optimizes the choice of (both positive and negative) eigenvalues of the dissimilarity Gram matrix to reduce STRESS, the sum of squared pairwise error. We provide an in-depth error analysis and proofs of the optimality in minimizing lower bounds of STRESS. We demonstrate Neuc-MDS's ability to address limitations of classical MDS raised by prior research, and test it on various synthetic and real-world datasets in comparison with both linear and non-linear dimension reduction methods.
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.
PowerPM: Foundation Model for Power Systems Shihao Tu
Deep learning models have advanced ETS modeling by effectively capturing sequence dependence. However, learning a generic representation of ETS data for various applications is challenging due to the inherently complex hierarchical structure of ETS data.
UniMTS: Unified Pre-training for Motion Time Series
Typically, existing models are trained and tested on the same dataset, leading to poor generalizability across variations in device location, device mounting orientation, and human activity type.
On the Complexity of Teaching a Family of Linear Behavior Cloning Learners
Figure 1: a) A board in a "Pick the Right Diamond" game.