Search Results for author:
Found 13 papers, 3 papers with code
PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison
Recent studies proposed evaluation metrics that assess generated responses by considering their relevance to previous dialogue histories.
SimCKP: Simple Contrastive Learning of Keyphrase Representations
Keyphrase generation (KG) aims to generate a set of summarizing words or phrases given a source document, while keyphrase extraction (KE) aims to identify them from the text.
PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration
Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in a document.
Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes
This paper presents the deep learning-based recent achievements to resolve the problem of autonomous mobility control and efficient resource management of autonomous vehicles and UAVs, i. e., (i) multi-agent reinforcement learning (MARL), and (ii) neural Myerson auction.
HistRED: A Historical Document-Level Relation Extraction Dataset
To demonstrate the usefulness of our dataset, we propose a bilingual RE model that leverages both Korean and Hanja contexts to predict relations between entities.
SplitGP: Achieving Both Generalization and Personalization in Federated Learning
A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i. e., to individual clients) and generalization (i. e., to unseen data) properties concurrently.
Bayesian deep learning framework for uncertainty quantification in high dimensions
A BNN efficiently learns the posterior distribution of the parameters in deep neural networks by performing Bayesian inference on the network parameters.
Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification
As deep neural networks achieve unprecedented performance in various tasks, neural architecture search (NAS), a research field for designing neural network architectures with automated processes, is actively underway.
Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications
Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency.
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries
While federated learning (FL) allows efficient model training with local data at edge devices, among major issues still to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries.
Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles
To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones.
FedMes: Speeding Up Federated Learning with Multiple Edge Servers
Our key idea is to utilize the devices located in the overlapping areas between the coverage of edge servers; in the model-downloading stage, the devices in the overlapping areas receive multiple models from different edge servers, take the average of the received models, and then update the model with their local data.
Sself: Robust Federated Learning against Stragglers and Adversaries
While federated learning allows efficient model training with local data at edge devices, two major issues that need to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries.
