Search Results for author:
Found 7 papers, 2 papers with code
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction.
Mixed Supervised Graph Contrastive Learning for Recommendation
Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss.
Cyclic Neural Network
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property.
NMTSloth: Understanding and Testing Efficiency Degradation of Neural Machine Translation Systems
Neural Machine Translation (NMT) systems have received much recent attention due to their human-level accuracy.
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models
To further understand such efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to evaluate the efficiency robustness of NICG models.
TransSlowDown: Efficiency Attacks on Neural Machine Translation Systems
To further the understanding of such efficiency-oriented threats and raise the community’s concern on the efficiency robustness of NMT systems, we propose a new attack approach, TranSlowDown, to test the efficiency robustness of NMT systems.
AttackDist: Characterizing Zero-day Adversarial Samples by Counter Attack
We first theoretically clarify under which condition AttackDist can provide a certified detecting performance, then show that a potential application of AttackDist is distinguishing zero-day adversarial examples without knowing the mechanisms of new attacks.
