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Found 16 papers, 5 papers with code
Debatrix: Multi-dimensinal Debate Judge with Iterative Chronological Analysis Based on LLM
How can we construct an automated debate judge to evaluate an extensive, vibrant, multi-turn debate?
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models.
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks.
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining
In this paper, we propose the Hierarchical Argumentation Graph (Hi-ArG), a new structure to organize arguments.
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning.
Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency
Specifically, we employ a Triple-branch multi-Dilated network (TDNet) with one encoder and three decoders using different dilation rates to capture features from different receptive fields that are complementary to each other to generate high-quality soft pseudo labels.
Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrieval
Therefore, we propose Dual-view Curricular Optimal Transport (DCOT) to learn with noisy correspondence in CCR.
Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review
Overall, this survey paper aims to provide a comprehensive overview of the current state-of-the-art in FL for CVD detection and to highlight its potential for improving the accuracy and privacy of CVD detection models.
Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling
In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge.
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs
The results demonstrate the effectiveness of our method on logical reasoning over KGs in both inductive and transductive settings.
Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response
A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms.
"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution
Experiment results show that the MeTFA-smoothed explanation can significantly increase the robust faithfulness.
Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach
A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties.
Large-scale Multi-view Subspace Clustering in Linear Time
A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years.
Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks
Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition.
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study
Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection.
