Search Results for author:
Found 9 papers, 3 papers with code
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Based on such insights, we propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data.
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models.
Pathway to Future Symbiotic Creativity
This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation.
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering
To study the performance of multivariate time series (MTS), we evaluate AUTOSHAPE on 30 UEA archive datasets with 5 competitive methods.
Reliable Adversarial Distillation with Unreliable Teachers
However, when considering adversarial robustness, teachers may become unreliable and adversarial distillation may not work: teachers are pretrained on their own adversarial data, and it is too demanding to require that teachers are also good at every adversarial data queried by students.
Budget Constrained Interactive Search for Multiple Targets
To address these two limitations, in this paper, we study a new problem of budget constrained interactive graph search for multiple targets called kBM-IGS-problem.
Image Classification
Product Categorization
Databases
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art
For E2E learning methods, the learning of graph embeddings does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems.
Authenticated Key-Value Stores with Hardware Enclaves
To circumvent the limited enclave memory (128 MB with the latest Intel CPUs), we propose to place the memory buffer of the eLSM store outside the enclave and protect the buffer using a new authenticated data structure by digesting individual LSM-tree levels.
Cryptography and Security Databases Distributed, Parallel, and Cluster Computing Data Structures and Algorithms
