5 citations · 16 across the 26 of their papers we have counts for
13 papers · 1 filter
Seeing but Not Believing: Probing the Disconnect Between Visual Attention and Answer Correctness in VLMs
Zhining Liu, Ziyi Chen, Hui Liu +9
Vision-Language Models (VLMs) achieve strong results on multimodal tasks such as visual question answering, yet they can still fail even when the correct visual evidence is present…
Hierarchical LoRA MoE for Efficient CTR Model Scaling
Zhichen Zeng, Mengyue Hang, Xiaolong Liu +11
Deep models have driven significant advances in click-through rate (CTR) prediction. While vertical scaling via layer stacking improves model expressiveness, the layer-by-layer seq…
Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
Hyunsik Yoo, Ting-Wei Li, SeongKu Kang +4
While large language models (LLMs) achieve strong performance in recommendation, they face challenges in continual learning as users, items, and user preferences evolve over time.…
Flow Matching Meets Biology and Life Science: A Survey
Zihao Li, Zhichen Zeng, Xiao Lin +9
Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological…
Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting
Zhining Liu, Ze Yang, Xiao Lin +6
Time-series forecasting plays a critical role in many real-world applications. Although increasingly powerful models have been developed and achieved superior results on benchmark…
MORALISE: A Structured Benchmark for Moral Alignment in Visual Language Models
Xiao Lin, Zhining Liu, Ze Yang +10
Warning: This paper contains examples of harmful language and images. Reader discretion is advised. Recently, vision-language models have demonstrated increasing influence in moral…
PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment
Qi Yu, Zhichen Zeng, Yuchen Yan +5
Network alignment (NA) aims to identify node correspondence across different networks and serves as a critical cornerstone behind various downstream multi-network learning tasks. D…
CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
Zhining Liu, Zihao Li, Ze Yang +6
Class-imbalanced learning (CIL) on tabular data is important in many real-world applications where the minority class holds the critical but rare outcomes. In this paper, we presen…
ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method
Dongqi Fu, Yada Zhu, Zhining Liu +10
Climate science studies the structure and dynamics of Earth's climate system and seeks to understand how climate changes over time, where the data is usually stored in the format o…
CATS: Mitigating Correlation Shift for Multivariate Time Series Classification
Xiao Lin, Zhichen Zeng, Tianxin Wei +3
Unsupervised Domain Adaptation (UDA) leverages labeled source data to train models for unlabeled target data. Given the prevalence of multivariate time series (MTS) data across var…
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative
Zihao Li, Xiao Lin, Zhining Liu +8
While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information, rema…
SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence
Zhining Liu, Rana Ali Amjad, Ravinarayana Adkathimar +2
Providing Language Models (LMs) with relevant evidence in the context (either via retrieval or user-provided) can significantly improve their ability to provide better-grounded res…
THeGCN: Temporal Heterophilic Graph Convolutional Network
Yuchen Yan, Yuzhong Chen, Huiyuan Chen +6
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in diverse graph learning tasks, particularly on static homophilic graphs. Recent attention has pivoted towards more…