8 citations · 9 across the 14 of their papers we have counts for
16 papers
Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models
Junpeng Wang, Yuzhong Chen, Menghai Pan +2
The evaluation of large language models (LLMs) on coding tasks has primarily focused on performance metrics such as pass@k. As LLMs continue to advance, many models now meet baseli…
TiCT: A Synthetically Pre-Trained Foundation Model for Time Series Classification
Chin-Chia Michael Yeh, Uday Singh Saini, Junpeng Wang +5
The ubiquity of time series data creates a strong demand for general-purpose foundation models, yet developing them for classification remains a significant challenge, largely due…
TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding
Chin-Chia Michael Yeh, Uday Singh Saini, Xin Dai +12
Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling this data can enable applications suc…
TransactionGPT
Yingtong Dou, Zhimeng Jiang, Tianyi Zhang +26
We present TransactionGPT (TGPT), a foundation model for consumer transaction data within one of the world's largest payment networks. TGPT is designed to understand and generate t…
Empowering Time Series Forecasting with LLM-Agents
Chin-Chia Michael Yeh, Vivian Lai, Uday Singh Saini +5
Large Language Model (LLM) powered agents have emerged as effective planners for Automated Machine Learning (AutoML) systems. While most existing AutoML approaches focus on automat…
Towards Efficient Large Scale Spatial-Temporal Time Series Forecasting via Improved Inverted Transformers
Jiarui Sun, Chin-Chia Michael Yeh, Yujie Fan +10
Time series forecasting at scale presents significant challenges for modern prediction systems, particularly when dealing with large sets of synchronized series, such as in a globa…
Visual Attention Exploration in Vision-Based Mamba Models
Junpeng Wang, Chin-Chia Michael Yeh, Uday Singh Saini +1
State space models (SSMs) have emerged as an efficient alternative to transformer-based models, offering linear complexity that scales better than transformers. One of the latest a…
UltraSTF: Ultra-Compact Model for Large-Scale Spatio-Temporal Forecasting
Chin-Chia Michael Yeh, Xiran Fan, Zhimeng Jiang +9
Spatio-temporal data, prevalent in real-world applications such as traffic monitoring, financial transactions, and ride-share demands, represents a specialized case of multivariate…
Preserving Individuality while Following the Crowd: Understanding the Role of User Taste and Crowd Wisdom in Online Product Rating Prediction
Liang Wang, Shubham Jain, Yingtong Dou +9
Numerous algorithms have been developed for online product rating prediction, but the specific influence of user and product information in determining the final prediction score r…
Matrix Profile for Anomaly Detection on Multidimensional Time Series
Chin-Chia Michael Yeh, Audrey Der, Uday Singh Saini +11
The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This paper delves into the problem of anoma…
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
Prince Aboagye, Yan Zheng, Junpeng Wang +8
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets. Traditionally, these models are assesse…
CARL-G: Clustering-Accelerated Representation Learning on Graphs
William Shiao, Uday Singh Saini, Yozen Liu +3
Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of…
Subspace Clustering Based Analysis of Neural Networks
Uday Singh Saini, Pravallika Devineni, Evangelos E. Papalexakis
Tools to analyze the latent space of deep neural networks provide a step towards better understanding them. In this work, we motivate sparse subspace clustering (SSC) with an aim t…
Analyzing Representations inside Convolutional Neural Networks
Uday Singh Saini, Evangelos E. Papalexakis
How can we discover and succinctly summarize the concepts that a neural network has learned? Such a task is of great importance in applications of networks in areas of inference th…
A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens
Uday Singh Saini, Evangelos E. Papalexakis
Despite their increasing popularity and success in a variety of supervised learning problems, deep neural networks are extremely hard to interpret and debug: Given and already trai…
Relation Schema Induction using Tensor Factorization with Side Information
Madhav Nimishakavi, Uday Singh Saini, Partha Talukdar
Given a set of documents from a specific domain (e.g., medical research journals), how do we automatically build a Knowledge Graph (KG) for that domain? Automatic identification of…