16 citations · 18 across the 11 of their papers we have counts for
11 papers
UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs
Amirhossein Abaskohi, Amirhossein Dabiriaghdam, Liang Luo +4
We introduce UnpredictaBench, an evaluation that tests the ability of large language models (LLMs) to capture true underlying distributions. As LLMs are increasingly used as substi…
VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
Amirhossein Dabiriaghdam, Shayan Vassef, Mohammadreza Bakhtiari +5
Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then re…
Test-Time Steering for Lossless Text Compression via Weighted Product of Experts
Qihang Zhang, Muchen Li, Ziao Wang +2
Lossless compression techniques are crucial in an era of rapidly growing data. Traditional universal compressors like gzip offer low computational overhead, high speed, and broad a…
StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams
Zike Wu, Qi Yan, Xuanyu Yi +2
Real-time reconstruction of dynamic 3D scenes from uncalibrated video streams demands robust online methods that recover scene dynamics from sparse observations under strict latenc…
MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation
Yuxiang Fu, Qi Yan, Lele Wang +2
In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans based on their past trajectories…
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
Jiahe Liu, Youran Qu, Qi Yan +3
Significant advancements have been made in video generative models recently. Unlike image generation, video generation presents greater challenges, requiring not only generating hi…
BCAmirs at SemEval-2024 Task 4: Beyond Words: A Multimodal and Multilingual Exploration of Persuasion in Memes
Amirhossein Abaskohi, Amirhossein Dabiriaghdam, Lele Wang +1
Memes, combining text and images, frequently use metaphors to convey persuasive messages, shaping public opinion. Motivated by this, our team engaged in SemEval-2024 Task 4, a hier…
Joint Generative Modeling of Grounded Scene Graphs and Images via Diffusion Models
Bicheng Xu, Qi Yan, Renjie Liao +2
We introduce a framework for joint grounded scene graph - image generation, a challenging task involving high-dimensional, multi-modal structured data. To effectively model this co…
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
Qi Yan, Zhengyang Liang, Yang Song +2
Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts,…
Specformer: Spectral Graph Neural Networks Meet Transformers
Deyu Bo, Chuan Shi, Lele Wang +1
Spectral graph neural networks (GNNs) learn graph representations via spectral-domain graph convolutions. However, most existing spectral graph filters are scalar-to-scalar functio…
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models
Lele Wang, Bin Liu
Training an ensemble of diverse sub-models has been empirically demonstrated as an effective strategy for improving the adversarial robustness of deep neural networks. However, cur…