3 citations · 3 across the 7 of their papers we have counts for
15 papers
ReactionMamba: Generating Short & Long Human Reaction Sequences
Hajra Anwar Beg, Baptiste Chopin, Hao Tang +1
We present ReactionMamba, a novel framework for generating long 3D human reaction motions. Reaction-Mamba integrates a motion VAE for efficient motion encoding with Mamba-based sta…
An Evaluation of Interleaved Instruction Tuning on Semantic Reasoning Performance in an Audio MLLM
Jiawei Liu, Enis Berk Çoban, Zarina Schevchenko +4
Standard training for Multi-modal Large Language Models (MLLMs) involves concatenating non-textual information, like vision or audio, with a text prompt. This approach may not enco…
Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
Mouyang Cheng, Weiliang Luo, Hao Tang +6
Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet, many existing approaches overlook essential chemical constraints such as ox…
Resolving Task Objective Conflicts in Unified Model via Task-Aware Mixture-of-Experts
Jiaxing Zhang, Hao Tang
Unified multimodal large language models (MLLMs) based on end-to-end autoregressive (AR) transformers effectively integrate both understanding and generation tasks within a single…
Improved Algorithms for Differentially Private Language Model Alignment
Keyu Chen, Hao Tang, Qinglin Liu +1
Language model alignment is crucial for ensuring that large language models (LLMs) align with human preferences, yet it often involves sensitive user data, raising significant priv…
Multimodal Large Language Models for Medicine: A Comprehensive Survey
Jiarui Ye, Hao Tang
MLLMs have recently become a focal point in the field of artificial intelligence research. Building on the strong capabilities of LLMs, MLLMs are adept at addressing complex multi-…