Search Results for author:
Found 18 papers, 4 papers with code
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space
Learning high-quality sentence representations is a fundamental problem of natural language processing which could benefit a wide range of downstream tasks.
CAMEL: Curvature-Augmented Manifold Embedding and Learning
CAMEL utilizes a topology metric defined on the Riemannian manifold, and a unique Riemannian metric for both distance and curvature to enhance its expressibility.
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing
Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations.
Smart Tracking Tray System for A Smart and Sustainable Wet Lab Community
Positive feedback has been collected from pilot tests in several labs.
Functional Connectivity of the Brain Across Rodents and Humans
This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity.
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change.
AnANet: Modeling Association and Alignment for Cross-modal Correlation Classification
Thus researchers study the definition of cross-modal correlation category and construct various classification systems and predictive models.
A Direct Slip Ratio Estimation Method based on an Intelligent Tire and Machine Learning
The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%.
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications.
An Examination of Preference-based Reinforcement Learning for Treatment Recommendation
Treatment recommendation is a complex multi-faceted problem with many conflicting objectives, e. g., optimizing the survival rate (or expected lifetime), mitigating negative impacts, reducing financial expenses and time costs, avoiding over-treatment, etc.
Differentially Private Adversarial Robustness Through Randomized Perturbations
Deep Neural Networks, despite their great success in diverse domains, are provably sensitive to small perturbations on correctly classified examples and lead to erroneous predictions.
Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems
It is delineated that the proposed intelligent tire system can provide reliable information about the tire-road interactions even in the case of high slip angles.
Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association
In multimodal context, sarcasm is no longer a pure linguistic phenomenon, and due to the nature of social media short text, the opposite is more often manifested via cross-modality expressions.
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
The bottom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network.
VASPKIT: A User-friendly Interface Facilitating High-throughput Computing and Analysis Using VASP Code
The executable versions of VASPKIT and the related examples, together with the tutorials, are available in its official website vaspkit. com.
Materials Science
Diagnosing Reinforcement Learning for Traffic Signal Control
In this paper, we propose to re-examine the RL approaches through the lens of classic transportation theory.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering
In this method, we use the subspace representations of different views to adaptively learn a consensus similarity matrix, uncovering the subspace structure and avoiding noisy nature of original data.
