Search Results for author: Sheng Cheng

Found 12 papers, 6 papers with code

Tool-Planner: Dynamic Solution Tree Planning for Large Language Model with Tool Clustering

1 code implementation6 Jun 2024 Yanming Liu, Xinyue Peng, Yuwei Zhang, Jiannan Cao, Xuhong Zhang, Sheng Cheng, Xun Wang, Jianwei Yin, Tianyu Du

Tool-Planner groups tools based on the API functions with the same function into a toolkit and allows LLMs to implement planning across the various toolkits.

Language Modelling Large Language Model

Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds

no code implementations27 Jan 2024 Yuliang Gu, Sheng Cheng, Naira Hovakimyan

Quadrotors are increasingly used in the evolving field of aerial robotics for their agility and mechanical simplicity.

Decoder Meta-Learning +1

ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations

no code implementations CVPR 2024 Maitreya Patel, Changhoon Kim, Sheng Cheng, Chitta Baral, Yezhou Yang

The T2I prior model alone adds a billion parameters compared to the Latent Diffusion Models, which increases the computational and high-quality data requirements.

Contrastive Learning Decoder

Adversarial Bayesian Augmentation for Single-Source Domain Generalization

1 code implementation ICCV 2023 Sheng Cheng, Tejas Gokhale, Yezhou Yang

Generalizing to unseen image domains is a challenging problem primarily due to the lack of diverse training data, inaccessible target data, and the large domain shift that may exist in many real-world settings.

Data Augmentation Photo to Rest Generalization

WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models

1 code implementation CVPR 2024 Changhoon Kim, Kyle Min, Maitreya Patel, Sheng Cheng, Yezhou Yang

This paper introduces a novel approach to model fingerprinting that assigns responsibility for the generated images, thereby serving as a potential countermeasure to model misuse.

Misinformation

A New Super-Resolution Measurement of Perceptual Quality and Fidelity

no code implementations10 Mar 2023 Sheng Cheng

Through a human subject study on super-resolution, we show that the proposed metric is highly correlated with the human perceptual quality, and better than most existing metrics.

Super-Resolution

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

no code implementations14 Feb 2023 Zhuohuan Wu, Sheng Cheng, Pan Zhao, Aditya Gahlawat, Kasey A. Ackerman, Arun Lakshmanan, Chengyu Yang, Jiahao Yu, Naira Hovakimyan

Quadrotors that can operate safely in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications.

SSR-GNNs: Stroke-based Sketch Representation with Graph Neural Networks

no code implementations27 Apr 2022 Sheng Cheng, Yi Ren, Yezhou Yang

This paper follows cognitive studies to investigate a graph representation for sketches, where the information of strokes, i. e., parts of a sketch, are encoded on vertices and information of inter-stroke on edges.

$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors

2 code implementations14 Sep 2021 Zhuohuan Wu, Sheng Cheng, Kasey A. Ackerman, Aditya Gahlawat, Arun Lakshmanan, Pan Zhao, Naira Hovakimyan

This paper introduces an $\mathcal{L}_1$ adaptive control augmentation for geometric tracking control of quadrotors.

Data-Driven Learning of 3-Point Correlation Functions as Microstructure Representations

1 code implementation6 Sep 2021 Sheng Cheng, Yang Jiao, Yi Ren

This paper considers the open challenge of identifying complete, concise, and explainable quantitative microstructure representations for disordered heterogeneous material systems.

Bayesian Optimization

Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks

no code implementations17 Jun 2021 Yutian Pang, Sheng Cheng, Jueming Hu, Yongming Liu

To evaluate the robustness gain of Bayesian neural networks on image classification tasks, we perform input perturbations, and adversarial attacks to the state-of-the-art Bayesian neural networks, with a benchmark CNN model as reference.

Decision Making Image Classification

An Optimality Gap Test for a Semidefinite Relaxation of a Quadratic Program with Two Quadratic Constraints

1 code implementation5 Jul 2019 Sheng Cheng, Nuno C. Martins

We propose a necessary and sufficient test to determine whether a solution for a general quadratic program with two quadratic constraints (QC2QP) can be computed from that of a specific convex semidefinite relaxation, in which case we say that there is no optimality gap.

Optimization and Control

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