Search Results for author:
Found 23 papers, 8 papers with code
Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs
Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs.
Towards Efficient Replay in Federated Incremental Learning
Then, the client trains the local model with both the cached samples and the samples from the new task.
FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems
To date, FaultProfIT has analyzed 10, 000+ incidents from 30+ cloud services, successfully revealing several fault trends that have informed system improvements.
Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context
We conducted preliminary experiments to validate the performance of IDECoder and observed that this synergy represents a promising trend for future exploration.
An Empirical Study on Large Language Models in Accuracy and Robustness under Chinese Industrial Scenarios
In this paper, we present a comprehensive empirical study on the accuracy and robustness of LLMs in the context of the Chinese industrial production area.
MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection
These methods are evaluated with five multivariate KPI datasets that are publicly available.
Ensemble-based Interactive Imitation Learning
We study interactive imitation learning, where a learner interactively queries a demonstrating expert for action annotations, aiming to learn a policy that has performance competitive with the expert, using as few annotations as possible.
Efficient Human-AI Coordination via Preparatory Language-based Convention
Developing intelligent agents capable of seamless coordination with humans is a critical step towards achieving artificial general intelligence.
Siamese-DETR for Generic Multi-Object Tracking
Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are proposed to track interested objects beyond pre-defined categories with the given text prompt and template image.
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.
Missing-modality Enabled Multi-modal Fusion Architecture for Medical Data
This study aimed to develop an efficient multi-modal fusion architecture for medical data that was robust to missing modalities and further improved the performance on disease diagnosis. X-ray chest radiographs for the image modality, radiology reports for the text modality, and structured value data for the tabular data modality were fused in this study.
Improving the Transferability of Adversarial Samples by Path-Augmented Method
However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples.
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment.
DaFKD: Domain-Aware Federated Knowledge Distillation
In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models.
On Efficient Online Imitation Learning via Classification
We make the following contributions: (1) we show that in the $\textbf{COIL}$ problem, any proper online learning algorithm cannot guarantee a sublinear regret in general; (2) we propose $\textbf{Logger}$, an improper online learning algorithmic framework, that reduces $\textbf{COIL}$ to online linear optimization, by utilizing a new definition of mixed policy class; (3) we design two oracle-efficient algorithms within the $\textbf{Logger}$ framework that enjoy different sample and interaction round complexity tradeoffs, and conduct finite-sample analyses to show their improvements over naive behavior cloning; (4) we show that under the standard complexity-theoretic assumptions, efficient dynamic regret minimization is infeasible in the $\textbf{Logger}$ framework.
Towards Making Deep Learning-based Vulnerability Detectors Robust
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
Network Architecture Search for Domain Adaptation
Deep networks have been used to learn transferable representations for domain adaptation.
Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation
Extensive experiments are conducted to demonstrate the power of our new datasets in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model.
Learning 3D Part Assembly from a Single Image
Autonomous assembly is a crucial capability for robots in many applications.
Learning Domain Adaptive Features with Unlabeled Domain Bridges
Secondly, we propose the Prototypical Adversarial Domain Adaptation (PADA) model which utilizes unlabeled bridge domains to align feature distribution between source and target with a large discrepancy.
Language Modeling with Graph Temporal Convolutional Networks
Recently, there have been some attempts to use non-recurrent neural models for language modeling.
Revisiting Image-Language Networks for Open-ended Phrase Detection
Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image.
A 20-Liter Test Stand with Gas Purification for Liquid Argon Research
We describe the design of a 20-liter test stand constructed to study fundamental properties of liquid argon (LAr).
Instrumentation and Detectors High Energy Physics - Experiment
