b'Bryan Hooi'

research

∙ 09/16/2023

Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals

Recently, the development of large language models (LLMs) has been signi...

0 Zhiyuan Hu, et al. ∙

research

∙ 06/22/2023

Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

The task of empowering large language models (LLMs) to accurately expres...

0 Miao Xiong, et al. ∙

research

∙ 06/16/2023

Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System

Dialogue systems and large language models (LLMs) have gained considerab...

0 Zhiyuan Hu, et al. ∙

research

∙ 06/14/2023

PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation

Poetry generation is a typical and popular task in natural language gene...

0 Zhiyuan Hu, et al. ∙

research

∙ 06/14/2023

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting

Traffic forecasting plays a critical role in smart city initiatives and ...

0 Xu Liu, et al. ∙

research

∙ 06/07/2023

Proximity-Informed Calibration for Deep Neural Networks

Confidence calibration is central to providing accurate and interpretabl...

0 Miao Xiong, et al. ∙

research

∙ 05/31/2023

Explanations as Features: LLM-Based Features for Text-Attributed Graphs

Representation learning on text-attributed graphs (TAGs) has become a cr...

7 Xiaoxin He, et al. ∙

research

∙ 05/30/2023

GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks

Label errors have been found to be prevalent in popular text, vision, an...

0 Yuwen Li, et al. ∙

research

∙ 05/23/2023

SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres

Event-centric structured prediction involves predicting structured outpu...

0 Shumin Deng, et al. ∙

research

∙ 05/22/2023

EntRED: Benchmarking Relation Extraction with Fewer Shortcuts

Entity names play an effective role in relation extraction (RE) and ofte...

0 Yiwei Wang, et al. ∙

research

∙ 05/10/2023

Towards Better Graph Representation Learning with Parameterized Decomposition Filtering

Proposing an effective and flexible matrix to represent a graph is a fun...

0 Mingqi Yang, et al. ∙

research

∙ 05/02/2023

Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement

Reliable application of machine learning is of primary importance to the...

0 Ailin Deng, et al. ∙

research

∙ 04/17/2023

TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks

Road safety is a major global public health concern. Effective traffic c...

0 Baixiang Huang, et al. ∙

research

∙ 02/25/2023

RETEXO: Scalable Neural Network Training over Distributed Graphs

Graph neural networks offer a promising approach to supervised learning ...

0 Aashish Kolluri, et al. ∙

research

∙ 02/06/2023

Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness

Trustworthy machine learning is of primary importance to the practical d...

0 Ailin Deng, et al. ∙

research

∙ 01/30/2023

Do We Really Need Graph Neural Networks for Traffic Forecasting?

Spatio-temporal graph neural networks (STGNN) have become the most popul...

0 Xu Liu, et al. ∙

research

∙ 12/27/2022

A Generalization of ViT/MLP-Mixer to Graphs

Graph Neural Networks (GNNs) have shown great potential in the field of ...

4 Xiaoxin He, et al. ∙

research

∙ 11/29/2022

Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation

How do we know when the predictions made by a classifier can be trusted?...

0 Miao Xiong, et al. ∙

research

∙ 11/25/2022

Expanding Small-Scale Datasets with Guided Imagination

The power of Deep Neural Networks (DNNs) depends heavily on the training...

0 Yifan Zhang, et al. ∙

research

∙ 10/24/2022

Reachability-Aware Laplacian Representation in Reinforcement Learning

In Reinforcement Learning (RL), Laplacian Representation (LapRep) is a t...

0 Kaixin Wang, et al. ∙

research

∙ 10/15/2022

MGNNI: Multiscale Graph Neural Networks with Implicit Layers

Recently, implicit graph neural networks (GNNs) have been proposed to ca...

0 Juncheng Liu, et al. ∙

research

∙ 09/25/2022

Joint Triplet Loss Learning for Next New POI Recommendation

Sparsity of the User-POI matrix is a well established problem for next P...

0 Nicholas Lim, et al. ∙

research

∙ 09/19/2022

Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks

As social media becomes a hotbed for the spread of misinformation, the c...

0 Jiaying Wu, et al. ∙

research

∙ 09/17/2022

Flashlight: Scalable Link Prediction with Effective Decoders

Link prediction (LP) has been recognized as an important task in graph l...

0 Yiwei Wang, et al. ∙

research

∙ 08/23/2022

Neural PCA for Flow-Based Representation Learning

Of particular interest is to discover useful representations solely from...

0 Shen Li, et al. ∙

research

∙ 08/18/2022

A Scalable, Interpretable, Verifiable Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables

We propose 𝒯ruth 𝒯able net (𝒯𝒯net), a novel Convolutional Neural Network...

9 Adrien Benamira, et al. ∙

research

∙ 06/15/2022

ARES: Locally Adaptive Reconstruction-based Anomaly Scoring

How can we detect anomalies: that is, samples that significantly differ ...

0 Adam Goodge, et al. ∙

research

∙ 05/08/2022

GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction

Entity types and textual context are essential properties for sentence-l...

0 Yiwei Wang, et al. ∙

research

∙ 05/08/2022

Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis

Recent literature focuses on utilizing the entity information in the sen...

0 Yiwei Wang, et al. ∙

research

∙ 05/06/2022

LPGNet: Link Private Graph Networks for Node Classification

Classification tasks on labeled graph-structured data have many importan...

0 Aashish Kolluri, et al. ∙

research

∙ 05/05/2022

Dangling-Aware Entity Alignment with Mixed High-Order Proximities

We study dangling-aware entity alignment in knowledge graphs (KGs), whic...

0 Juncheng Liu, et al. ∙

research

∙ 02/22/2022

EIGNN: Efficient Infinite-Depth Graph Neural Networks

Graph neural networks (GNNs) are widely used for modelling graph-structu...

0 Juncheng Liu, et al. ∙

research

∙ 01/30/2022

The Geometry of Robust Value Functions

The space of value functions is a fundamental concept in reinforcement l...

0 Kaixin Wang, et al. ∙

research

∙ 12/18/2021

Time-Aware Neighbor Sampling for Temporal Graph Networks

We present a new neighbor sampling method on temporal graphs. In a tempo...

0 Yiwei Wang, et al. ∙

research

∙ 12/10/2021

LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

Many well-established anomaly detection methods use the distance of a sa...

0 Adam Goodge, et al. ∙

research

∙ 12/02/2021

Probabilistic Contrastive Loss for Self-Supervised Learning

This paper proposes a probabilistic contrastive loss function for self-s...

18 Shen Li, et al. ∙

research

∙ 12/01/2021

Structure-Aware Label Smoothing for Graph Neural Networks

Representing a label distribution as a one-hot vector is a common practi...

0 Yiwei Wang, et al. ∙

research

∙ 11/11/2021

Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series

Given a cardiac-arrest patient being monitored in the ICU (intensive car...

5 Shubhranshu Shekhar, et al. ∙

research

∙ 10/25/2021

SSMF: Shifting Seasonal Matrix Factorization

Given taxi-ride counts information between departure and destination loc...

0 Koki Kawabata, et al. ∙

research

∙ 10/09/2021

Deep Long-Tailed Learning: A Survey

Deep long-tailed learning, one of the most challenging problems in visua...

0 Yifan Zhang, et al. ∙

research

∙ 08/26/2021

Spatio-Temporal Graph Contrastive Learning

Deep learning models are modern tools for spatio-temporal graph (STG) fo...

0 Xu Liu, et al. ∙

research

∙ 07/20/2021

Test-Agnostic Long-Tailed Recognition by Test-Time Aggregating Diverse Experts with Self-Supervision

Existing long-tailed recognition methods, aiming to train class-balance ...

1 Yifan Zhang, et al. ∙

research

∙ 07/12/2021

Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing

The Laplacian representation recently gains increasing attention for rei...

26 Kaixin Wang, et al. ∙

research

∙ 06/13/2021

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

Given high-dimensional time series data (e.g., sensor data), how can we ...

0 Ailin Deng, et al. ∙

research

∙ 06/08/2021

Sketch-Based Streaming Anomaly Detection in Dynamic Graphs

Given a stream of graph edges from a dynamic graph, how can we assign an...

0 Siddharth Bhatia, et al. ∙

research

∙ 06/07/2021

MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

Given a stream of entries over time in a multi-aspect data setting where...

0 Siddharth Bhatia, et al. ∙

research

∙ 04/04/2021

Isconna: Streaming Anomaly Detection with Frequency and Patterns

An edge stream is a common form of presentation of dynamic networks. It ...

0 Rui Liu, et al. ∙

research

∙ 03/20/2021

PathEnum: Towards Real-Time Hop-Constrained s-t Path Enumeration

We study the hop-constrained s-t path enumeration (HcPE) problem, which ...

0 Shixuan Sun, et al. ∙

research

∙ 02/12/2021

Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning

Contrastive self-supervised learning (CSL) leverages unlabeled data to t...

0 Yifan Zhang, et al. ∙

research

∙ 12/30/2020

Dynamic Graph-Based Anomaly Detection in the Electrical Grid

Given sensor readings over time from a power grid, how can we accurately...

2 Shimiao Li, et al. ∙

Bryan Hooi

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