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Found 17 papers, 6 papers with code
CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities
This paper describes our approach to develop a complex named entity recognition system in SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition, Track 9 - Chinese.
CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts
This paper presents a description of our system in SMM4H-2022, where we participated in task 1a, task 4, and task 6 to task 10.
Generalizing Cooperative Eco-driving via Multi-residual Task Learning
To address this, we introduce Multi-residual Task Learning (MRTL), a generic learning framework based on multi-task learning that, for a set of task scenarios, decomposes the control into nominal components that are effectively solved by conventional control methods and residual terms which are solved using learning.
ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic Cartilage Segmentation
Experiments on the mice cartilage dataset show the superiority of our new model compared to other competitive segmentation models.
SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification
We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images.
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks
The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors.
Non-Euclidean Spatial Graph Neural Network
Besides, existing spatial network representation learning methods can only consider networks embedded in Euclidean space, and can not well exploit the rich geometric information carried by irregular and non-uniform non-Euclidean space.
Hybrid System Stability Analysis of Multi-Lane Mixed-Autonomy Traffic
Through examining the influence of the lane-switch frequency on the system's stability, the analysis offers a principled explanation to the traffic break phenomena, and further discovers opportunities for less-intrusive traffic smoothing by employing less frequent lane-switching.
Large Language Models for Failure Mode Classification: An Investigation
In this paper we present the first investigation into the effectiveness of Large Language Models (LLMs) for Failure Mode Classification (FMC).
Integrated Analysis of Coarse-Grained Guidance for Traffic Flow Stability
While previous theoretical studies consider stability analysis for continuous AV control, this article presents the first integrated theoretical analysis that directly relates the guidance provided to the human drivers to the traffic flow stability outcome.
Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding
It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and we propose a convolutional and multi-relational representation learning model, ConvMR.
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
We show that in comparison to evaluating DRL methods on select MDP instances, evaluating the MDP family often yields a substantially different relative ranking of methods, casting doubt on what methods should be considered state-of-the-art.
Look Closer to Your Enemy: Learning to Attack via Teacher-Student Mimicking
Deep neural networks have significantly advanced person re-identification (ReID) applications in the realm of the industrial internet, yet they remain vulnerable.
Improving Question Answering over Knowledge Graphs Using Graph Summarization
The key idea is to represent questions and entities of a KG as low-dimensional embeddings.
Cooperation for Scalable Supervision of Autonomy in Mixed Traffic
A key takeaway is the potential value of cooperation in enabling the deployment of autonomy at scale.
Learning to Delegate for Large-scale Vehicle Routing
We frame subproblem selection as regression and train a Transformer on a generated training set of problem instances.
Conditional Linear Regression
Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers.
