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Found 18 papers, 4 papers with code
Enhancing Temporal Knowledge Graph Forecasting with Large Language Models via Chain-of-History Reasoning
Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based on given histories.
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects.
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
Data annotation remains the sine qua non of machine learning and AI.
Making Data Work Count
To examine this, we draw on sociological and socio-technical scholarship on quantification and develop the lens of a 'regime of counting' that makes explicit the specific counts, practices, actors and structures that underpin the pervasive counting in annotation.
A Framework to Assess (Dis)agreement Among Diverse Rater Groups
Recent advancements in conversational AI have created an urgent need for safety guardrails that prevent users from being exposed to offensive and dangerous content.
Evolutionary City: Towards a Flexible, Agile and Symbiotic System
This paper introduces a novel approach, aiming to enable cities to evolve and respond more effectively to such dynamic demand.
IR Design for Application-Specific Natural Language: A Case Study on Traffic Data
In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits.
TransWorldNG: Traffic Simulation via Foundation Model
To the best of our knowledge, this is the first traffic simulator that can automatically learn traffic patterns from real-world data and efficiently generate accurate and realistic traffic environments.
Building Transportation Foundation Model via Generative Graph Transformer
This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy and provides a foundation for solving complex transportation problems with real data.
An Adaptive GViT for Gas Mixture Identification and Concentration Estimation
The gas identification and concentration estimation model called GCN-ViT(GViT) is proposed in this paper; we view the sensor data to be a one-way chain that has only been downscaled to retain the majority of the original in-formation.
Few Clean Instances Help Denoising Distant Supervision
Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems.
How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India
We report on a qualitative study with 29 financially-stressed users of instant loan platforms in India.
Learning Deep Neural Networks under Agnostic Corrupted Supervision
Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance.
Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films
We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).
Mesoscale and Nanoscale Physics
"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment
Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS).
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders
Unsupervised anomaly detection plays a crucial role in many critical applications.
Provable Robust Learning under Agnostic Corrupted Supervision
Training deep neural models in the presence of corrupted supervisions is challenging as the corrupted data points may significantly impact the generalization performance.
Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing.
