302 citations · 775 across the 27 of their papers we have counts for
27 papers
Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
Pengbo Hu, Ji Qi, Xingyu Li +5
There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning. This paradigm, known as LLM-…
Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing
Jie Zhang, Pengcheng Shi, Zaiwang Gu +2
The neural rendering of humans is a topic of great research significance. However, previous works mostly focus on achieving photorealistic details, neglecting the exploration of hu…
Overlap Bias Matching is Necessary for Point Cloud Registration
Pengcheng Shi, Jie Zhang, Haozhe Cheng +4
Point cloud registration is a fundamental problem in many domains. Practically, the overlap between point clouds to be registered may be relatively small. Most unsupervised methods…
Boosting Multi-modal Model Performance with Adaptive Gradient Modulation
Hong Li, Xingyu Li, Pengbo Hu +3
While the field of multi-modal learning keeps growing fast, the deficiency of the standard joint training paradigm has become clear through recent studies. They attribute the sub-o…
Evaluation and Analysis of Hallucination in Large Vision-Language Models
Junyang Wang, Yiyang Zhou, Guohai Xu +9
Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many…
Contrastive Label Enhancement
Yifei Wang, Yiyang Zhou, Jihua Zhu +3
Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity. Since it is difficult to directly obtain label distributions, many studies are foc…
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
Timothy Castiglia, Yi Zhou, Shiqiang Wang +3
We propose LESS-VFL, a communication-efficient feature selection method for distributed systems with vertically partitioned data. We consider a system of a server and several parti…
DualGenerator: Information Interaction-based Generative Network for Point Cloud Completion
Pengcheng Shi, Haozhe Cheng, Xu Han +2
Point cloud completion estimates complete shapes from incomplete point clouds to obtain higher-quality point cloud data. Most existing methods only consider global object features,…
Edge-cloud Collaborative Learning with Federated and Centralized Features
Zexi Li, Qunwei Li, Yi Zhou +3
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud serve…
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu +15
Large language models (LLMs) have demonstrated impressive zero-shot abilities on a variety of open-ended tasks, while recent research has also explored the use of LLMs for multi-mo…
Semantically Consistent Multi-view Representation Learning
Yiyang Zhou, Qinghai Zheng, Shunshun Bai +1
In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from m…
Multi-view Semantic Consistency based Information Bottleneck for Clustering
Wenbiao Yan, Jihua Zhu, Yiyang Zhou +2
Multi-view clustering can make use of multi-source information for unsupervised clustering. Most existing methods focus on learning a fused representation matrix, while ignoring th…
MCoCo: Multi-level Consistency Collaborative Multi-view Clustering
Yiyang Zhou, Qinghai Zheng, Wenbiao Yan +3
Multi-view clustering can explore consistent information from different views to guide clustering. Most existing works focus on pursuing shallow consistency in the feature space an…
Federated XGBoost on Sample-Wise Non-IID Data
Katelinh Jones, Yuya Jeremy Ong, Yi Zhou +1
Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to creat…
DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting
Runhua Xu, Nathalie Baracaldo, Yi Zhou +3
Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated…
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
Yi Zhou, Parikshit Ram, Theodoros Salonidis +3
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a gener…
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
Yi Zhou, Parikshit Ram, Theodoros Salonidis +3
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss suRface Aggregation (FLoRA), the fir…
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
Cheng Chen, Bhavya Kailkhura, Ryan Goldhahn +1
Federated learning is an emerging data-private distributed learning framework, which, however, is vulnerable to adversarial attacks. Although several heuristic defenses are propose…
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
Runhua Xu, Nathalie Baracaldo, Yi Zhou +3
Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this…
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Syed Zawad, Ahsan Ali, Pin-Yu Chen +5
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on…
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo +1
Federated Learning (FL) is an approach to collaboratively train a model across multiple parties without sharing data between parties or an aggregator. It is used both in the consum…
Mitigating Bias in Federated Learning
Annie Abay, Yi Zhou, Nathalie Baracaldo +3
As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored. FL is a rising approach for collaborative ML, in…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
TiFL: A Tier-based Federated Learning System
Zheng Chai, Ahsan Ali, Syed Zawad +7
Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that ex…