Search Results for author:
Found 39 papers, 13 papers with code
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network
As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management.
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks
Software Defined Internet of Things (SD-IoT) Networks profits from centralized management and interactive resource sharing which enhances the efficiency and scalability of IoT applications.
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension
In this paper, we propose the scheme for annotating large-scale multi-party chat dialogues for discourse parsing and machine comprehension.
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
Research into the area of multiparty dialog has grown considerably over recent years.
Ranked #7 on
Discourse Parsing
on Molweni
Synchrotron Microtomography and Neutron Radiography Characterization of the Microstruture and Water Absorption of Concrete from Pompeii
In the present work, samples were drilled from the "Hospitium" in Pompeii and were analyzed by synchrotron microtomography (uCT) and neutron radiography to study how the microstructure, including the presence of induced cracks, affects their water adsorption.
Applied Physics Materials Science Geophysics
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation
Robust principal component analysis (RPCA) is a widely used tool for dimension reduction.
Robust error bounds for quantised and pruned neural networks
With the rise of smartphones and the internet-of-things, data is increasingly getting generated at the edge on local, personal devices.
Unified Principles For Multi-Source Transfer Learning Under Label Shifts
We study the label shift problem in multi-source transfer learning and derive new generic principles to control the target generalization risk.
Insights into ventilation hysteresis shift due to flow unsteadiness in ventilated supercavitation
Imposing flow unsteadiness on the incoming flow has shown an increment in higher CQf at low free stream velocity and lower CQf at high free stream velocity.
Fluid Dynamics
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension
Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC.
Ranked #4 on
Question Answering
on Molweni
Aggregating From Multiple Target-Shifted Sources
Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain.
$\textit{Ab Initio}$ Discovery of Biological Knowledge from scRNA-Seq Data Using Machine Learning
The study illustrated the feasibility, significance, and limitation of $\textit{ab initio}$ ML knowledge discovery on complex biological problems.
Directional Domain Generalization
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search
Compared with the traditional rule-based pruning method, this pipeline saves human labor and achieves a higher compression ratio with lower accuracy loss.
Dynamic Flow Equilibrium of Transportation and Power Distribution Networks Considering Flexible Traveling Choices and Voltage Quality Improvement
The increasing global spread of electric vehicles (EVs) has introduced significant interdependence between transportation and power networks.
Fair Representation Learning through Implicit Path Alignment
We consider a fair representation learning perspective, where optimal predictors, on top of the data representation, are ensured to be invariant with respect to different sub-groups.
Evolving Domain Generalization
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.
Learning High-quality Proposals for Acne Detection
Acne detection is crucial for interpretative diagnosis and precise treatment of skin disease.
CrossHuman: Learning Cross-Guidance from Multi-Frame Images for Human Reconstruction
We propose CrossHuman, a novel method that learns cross-guidance from parametric human model and multi-frame RGB images to achieve high-quality 3D human reconstruction.
On Learning Fairness and Accuracy on Multiple Subgroups
In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding
Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair.
Towards Accurate Acne Detection via Decoupled Sequential Detection Head
In addition, we build a high-quality acne detection dataset named ACNE-DET to verify the effectiveness of DSDH.
When Source-Free Domain Adaptation Meets Learning with Noisy Labels
We also prove that such a difference makes existing LLN methods that rely on their distribution assumptions unable to address the label noise in SFDA.
Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners
On the whole, our analysis provides a novel perspective on the role of semantics in developing and evaluating language models' reasoning abilities.
Neural Video Depth Stabilizer
Video depth estimation aims to infer temporally consistent depth.
Ranked #15 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Diffusion-Augmented Depth Prediction with Sparse Annotations
We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner.
Stochastic Optimization of Coupled Power Distribution-Urban Transportation Network Operations with Autonomous Mobility on Demand Systems
Then, a bi-level power-traffic flow (PTF) model is proposed to characterize the interaction of power distribution networks (PDNs) and urban transportation networks (UTNs).
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive Learning
Firstly we retrieve the relevant embedding from the knowledge graph by utilizing group relations in metadata and then integrate it with other modalities.
LooGLE: Can Long-Context Language Models Understand Long Contexts?
In this paper, we present LooGLE, a Long Context Generic Language Evaluation benchmark for LLMs' long context understanding.
Hessian Aware Low-Rank Weight Perturbation for Continual Learning
We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method.
Digital Life Project: Autonomous 3D Characters with Social Intelligence
In this work, we present Digital Life Project, a framework utilizing language as the universal medium to build autonomous 3D characters, who are capable of engaging in social interactions and expressing with articulated body motions, thereby simulating life in a digital environment.
Ranked #2 on
Motion Synthesis
on InterHuman
Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems
This paper develops a new dimension-free Azuma-Hoeffding type bound on summation norm of a martingale difference sequence with random individual bounds.
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs).
Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models
Advances in language modeling have paved the way for novel human-AI co-writing experiences.
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
We explore how iterative revising a chain of thoughts with the help of information retrieval significantly improves large language models' reasoning and generation ability in long-horizon generation tasks, while hugely mitigating hallucination.
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos.
RAM: Towards an Ever-Improving Memory System by Learning from Communications
We introduce RAM, an innovative RAG-based framework with an ever-improving memory.
