44 citations · 63 across the 14 of their papers we have counts for
14 papers
Can You Delete a Year of Market Data? Machine Unlearning Against Exact Retraining Oracles
Junyi Ye
When a data license expires, deleting stored records does not remove influence encoded in a trained forecaster. Machine unlearning seeks to remove this influence without retraining…
Damage Predicts Recovery: When Calibration Data Matters in Compressing Financial LLMs
Junyi Ye, Mengjia Yu, Debapriya Hazra +1
Post-training quantization and pruning rely on a small calibration corpus. Whether specialized domains such as finance require domain-matched calibration data remains unsettled. We…
Calibration Bets on the Past: Post-Training Quantization for Financial Time-Series Forecasting
Junyi Ye, Ivy Gateri Wanjiku
Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memory and computational cost. Po…
Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting
Junyi Ye, Gargi Vijay Borde
Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should ent…
Illusions in Humans and AI: How Visual Perception Aligns and Diverges
Jianyi Yang, Junyi Ye, Ankan Dash +1
By comparing biological and artificial perception through the lens of illusions, we highlight critical differences in how each system constructs visual reality. Understanding these…
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
Jiayi Chen, Junyi Ye, Guiling Wang
Compound AI Systems (CAIS) are an emerging paradigm that integrates large language models (LLMs) with external components, including retrievers, agents, tools, and orchestrators, t…
Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding
Junyi Ye, Ankan Dash, Wenpeng Yin +1
Flowcharts are typically presented as images, driving the trend of using vision-language models (VLMs) for end-to-end flowchart understanding. However, two key challenges arise: (i…
Assessing the Creativity of LLMs in Proposing Novel Solutions to Mathematical Problems
Junyi Ye, Jingyi Gu, Xinyun Zhao +2
The mathematical capabilities of AI systems are complex and multifaceted. Most existing research has predominantly focused on the correctness of AI-generated solutions to mathemati…
From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical Problems
A M Muntasir Rahman, Junyi Ye, Wei Yao +5
Consider the math problem: "Lily received 3 cookies from her best friend yesterday and ate 5 for breakfast. Today, her friend gave her 3 more cookies. How many cookies does Lily ha…
From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
Junyi Ye, Bhaskar Goswami, Jingyi Gu +2
This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditiona…
DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure
Junyi Ye, Mengnan Du, Guiling Wang
This paper introduces DataFrame question answering (QA), a novel task that utilizes large language models (LLMs) to generate Pandas queries for information retrieval and data analy…
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control
Wenlu Du, Junyi Ye, Jingyi Gu +3
Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the…
A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing
Ankan Dash, Junyi Ye, Guiling Wang
We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another…
High Resolution Solar Image Generation using Generative Adversarial Networks
Ankan Dash, Junyi Ye, Guiling Wang
We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helio…