Search Results for author: Chun-Ta Lu

Found 19 papers, 4 papers with code

Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use

no code implementations5 Mar 2024 Imad Eddine Toubal, Aditya Avinash, Neil Gordon Alldrin, Jan Dlabal, Wenlei Zhou, Enming Luo, Otilia Stretcu, Hao Xiong, Chun-Ta Lu, Howard Zhou, Ranjay Krishna, Ariel Fuxman, Tom Duerig

Our framework leverages recent advances in foundation models, both large language models and vision-language models, to carve out the concept space through conversation and by automatically labeling training data points.

Image Classification Question Answering +2

Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models

no code implementations5 Dec 2023 Yushi Hu, Otilia Stretcu, Chun-Ta Lu, Krishnamurthy Viswanathan, Kenji Hata, Enming Luo, Ranjay Krishna, Ariel Fuxman

We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass.

Language Modelling Large Language Model +3

Benchmarking Robustness to Adversarial Image Obfuscations

1 code implementation NeurIPS 2023 Florian Stimberg, Ayan Chakrabarti, Chun-Ta Lu, Hussein Hazimeh, Otilia Stretcu, Wei Qiao, Yintao Liu, Merve Kaya, Cyrus Rashtchian, Ariel Fuxman, Mehmet Tek, Sven Gowal

We evaluate 33 pretrained models on the benchmark and train models with different augmentations, architectures and training methods on subsets of the obfuscations to measure generalization.


CARLS: Cross-platform Asynchronous Representation Learning System

1 code implementation26 May 2021 Chun-Ta Lu, Yun Zeng, Da-Cheng Juan, Yicheng Fan, Zhe Li, Jan Dlabal, Yi-Ting Chen, Arjun Gopalan, Allan Heydon, Chun-Sung Ferng, Reah Miyara, Ariel Fuxman, Futang Peng, Zhen Li, Tom Duerig, Andrew Tomkins

In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.

Representation Learning

MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing

no code implementations COLING 2020 Tao Zhang, Congying Xia, Chun-Ta Lu, Philip Yu

Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.

Entity Typing

Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks

no code implementations8 Nov 2019 Jiahao Liu, Guixiang Ma, Fei Jiang, Chun-Ta Lu, Philip S. Yu, Ann B. Ragin

Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity.


Graph-RISE: Graph-Regularized Image Semantic Embedding

1 code implementation14 Feb 2019 Da-Cheng Juan, Chun-Ta Lu, Zhen Li, Futang Peng, Aleksei Timofeev, Yi-Ting Chen, Yaxi Gao, Tom Duerig, Andrew Tomkins, Sujith Ravi

Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.

Clustering General Classification +4

A Self-Organizing Tensor Architecture for Multi-View Clustering

no code implementations18 Oct 2018 Lifang He, Chun-Ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, Fei Wang

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.


Spectral Collaborative Filtering

1 code implementation30 Aug 2018 Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, Philip S. Yu

Benefiting from the rich information of connectivity existing in the \textit{spectral domain}, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the \textit{cold-start} problem for CF.

Collaborative Filtering Recommendation Systems

Bringing Semantic Structures to User Intent Detection in Online Medical Queries

no code implementations22 Oct 2017 Chenwei Zhang, Nan Du, Wei Fan, Yaliang Li, Chun-Ta Lu, Philip S. Yu

The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries.

Intent Detection Multi-Task Learning +1

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

no code implementations12 Sep 2017 Guixiang Ma, Chun-Ta Lu, Lifang He, Philip S. Yu, Ann B. Ragin

Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis.

Clustering Graph Embedding +3

Kernelized Support Tensor Machines

no code implementations ICML 2017 Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.

Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification

no code implementations CVPR 2017 Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.

Classification General Classification

Learning from Multi-View Multi-Way Data via Structural Factorization Machines

no code implementations10 Apr 2017 Chun-Ta Lu, Lifang He, Hao Ding, Bokai Cao, Philip S. Yu

Real-world relations among entities can often be observed and determined by different perspectives/views.

Online Multi-view Clustering with Incomplete Views

no code implementations2 Nov 2016 Weixiang Shao, Lifang He, Chun-Ta Lu, Philip S. Yu

We model the multi-view clustering problem as a joint weighted nonnegative matrix factorization problem and process the multi-view data chunk by chunk to reduce the memory requirement.


Online Unsupervised Multi-view Feature Selection

no code implementations27 Sep 2016 Weixiang Shao, Lifang He, Chun-Ta Lu, Xiaokai Wei, Philip S. Yu

Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams?

Clustering feature selection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.