Search Results for author: Yan Fang

Found 9 papers, 1 papers with code

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

1 code implementation ACL 2020 Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.

Task-Oriented Dialogue Systems

Image Segmentation Using Frequency Locking of Coupled Oscillators

no code implementations9 May 2014 Yan Fang, Matthew J. Cotter, Donald M. Chiarulli, Steven P. Levitan

Synchronization of coupled oscillators is observed at multiple levels of neural systems, and has been shown to play an important function in visual perception.

Image Segmentation Segmentation +1

Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation

no code implementations22 Mar 2020 Ashwin Sanjay Lele, Yan Fang, Justin Ting, Arijit Raychowdhury

However, training a legged robot to walk by learning the synchronization patterns of central pattern generators (CPG) in an SNN framework has not been shown.

reinforcement-learning Reinforcement Learning (RL)

DesignGPT: Multi-Agent Collaboration in Design

no code implementations20 Nov 2023 Shiying Ding, Xinyi Chen, Yan Fang, Wenrui Liu, Yiwu Qiu, Chunlei Chai

Generative AI faces many challenges when entering the product design workflow, such as interface usability and interaction patterns.

Representation Learning on Event Stream via an Elastic Net-incorporated Tensor Network

no code implementations16 Jan 2024 Beibei Yang, Weiling Li, Yan Fang

Event cameras are neuromorphic sensors that capture asynchronous and sparse event stream when per-pixel brightness changes.

Representation Learning Tensor Decomposition

Scaling Laws For Dense Retrieval

no code implementations27 Mar 2024 Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, Yiqun Liu

In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models.

Data Augmentation Retrieval +1

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