1k citations · 1k across the 15 of their papers we have counts for
17 papers
STAMP as a topology-aware molecular language for fragment-level learning and editing
Yuxuan Ren, Jiatong Li, Ziqiao Meng +1
Molecular fragments provide chemically meaningful units, but existing molecular languages rarely make their locations and connections explicit for editing. Here we introduce STAMP…
One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA
Zhi Zheng, Ziqiao Meng, Hao Luan +2
External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence. However, existing…
Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
Ziqi Gao, Chenyi Zi, Zijing Liu +3
Protein-protein interactions (PPIs) are fundamental to cellular function and disease mechanisms. Current learning-based PPI predictors focus on learning powerful protein representa…
Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery
Guikun Xu, Xiaohan Yi, Ziqiao Meng +2
Exploring molecular energy landscapes and identifying ground-state conformations are central challenges in computational chemistry. However, generating diverse low-energy conformer…
PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction
Ziqiao Meng, Qichao Wang, Zhiyang Dou +4
Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an art…
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction
Qichao Wang, Ziqiao Meng, Wenqian Cui +6
Inspired by the impressive capabilities of GPT-4o, there is growing interest in enabling speech language models (SLMs) to engage in natural, fluid spoken interactions with humans.…
Continual Optimization with Symmetry Teleportation for Multi-Task Learning
Zhipeng Zhou, Ziqiao Meng, Pengcheng Wu +2
Multi-task learning (MTL) is a widely explored paradigm that enables the simultaneous learning of multiple tasks using a single model. Despite numerous solutions, the key issues of…
PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction
Ziqiao Meng, Qichao Wang, Zhiyang Dou +4
Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an art…
VoxEval: Benchmarking the Knowledge Understanding Capabilities of End-to-End Spoken Language Models
Wenqian Cui, Xiaoqi Jiao, Ziqiao Meng +1
With the rising need for speech-based interaction models, end-to-end Spoken Language Models (SLMs) have emerged as a promising solution. While these models require comprehensive wo…
Recent Advances in Speech Language Models: A Survey
Wenqian Cui, Dianzhi Yu, Xiaoqi Jiao +5
Large Language Models (LLMs) have recently garnered significant attention, primarily for their capabilities in text-based interactions. However, natural human interaction often rel…
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
Haoyu Wang, Guozheng Ma, Ziqiao Meng +9
Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. However, most current methods complete the data collection and…
A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges
Ziqiao Meng, Peilin Zhao, Yang Yu +1
Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery comm…
Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
Ziqiao Meng, Peilin Zhao, Yang Yu +1
Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of elect…
Graph-adaptive Rectified Linear Unit for Graph Neural Networks
Yifei Zhang, Hao Zhu, Ziqiao Meng +2
Graph Neural Networks (GNNs) have achieved remarkable success by extending traditional convolution to learning on non-Euclidean data. The key to the GNNs is adopting the neural mes…
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Yankai Chen, Menglin Yang, Yingxue Zhang +4
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently…
FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
Menglin Yang, Ziqiao Meng, Irwin King
Dynamic graphs arise in a plethora of practical scenarios such as social networks, communication networks, and financial transaction networks. Given a dynamic graph, it is fundamen…
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
Xinyu Fu, Jiani Zhang, Ziqiao Meng +1
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed rich…