Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Zhu, Jichen"'
Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the {\em effective explanations} needed by users. In order to improve XAI systems' usability, practical interpretability, and
Externí odkaz:
http://arxiv.org/abs/2403.14496
Ongoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theorie
Externí odkaz:
http://arxiv.org/abs/2403.09374
Autor:
Grabe, Imke, Zhu, Jichen
Originating from the premise that Generative Adversarial Networks (GANs) enrich creative processes rather than diluting them, we describe an ongoing PhD project that proposes to study GANs in a co-creative context. By asking How can GANs be applied i
Externí odkaz:
http://arxiv.org/abs/2304.09477
Autor:
Grabe, Imke, Zhu, Jichen
This paper presents a speculation on a fictive co-creation scenario that extends classical interaction patterns with generative models. While existing interfaces are restricted to the input and output layers, we suggest hidden layer interaction to ex
Externí odkaz:
http://arxiv.org/abs/2304.00266
Autor:
Gray, Robert C., Villareale, Jennifer, Fox, Thomas B., Dallal, Diane H., Ontañón, Santiago, Arigo, Danielle, Jabbari, Shahin, Zhu, Jichen
Algorithmic fairness is an essential requirement as AI becomes integrated in society. In the case of social applications where AI distributes resources, algorithms often must make decisions that will benefit a subset of users, sometimes repeatedly or
Externí odkaz:
http://arxiv.org/abs/2302.09298
Autor:
Nguyen, Thu, Zhu, Jichen
Human-Center eXplainable AI (HCXAI) literature identifies the need to address user needs. This paper examines how existing XAI research involves human users in designing and developing XAI systems and identifies limitations in current practices, espe
Externí odkaz:
http://arxiv.org/abs/2212.03186
This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles. Finding the latent vectors in the generator's latent space that correspond
Externí odkaz:
http://arxiv.org/abs/2204.00592
Autor:
Zhu, Jichen, El-Nasr, Magy Seif
Data is becoming an important central point for making design decisions for most software. Game development is not an exception. As data-driven methods and systems start to populate these environments, a good question is: can we make models developed
Externí odkaz:
http://arxiv.org/abs/2110.05810
Autor:
Zhu, Jichen
Human interaction with technical artifacts is often mediated by treating them as if they are alive. We exclaim "my car doesn't want to start," or "my computer loves to crash." Of increasing cultural importance are software systems designed explicitly
Externí odkaz:
http://hdl.handle.net/1853/34730
Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning
Autor:
Löwe, Mathias, Villareale, Jennifer, Freed, Evan, Sladek, Aleksanteri, Zhu, Jichen, Risi, Sebastian
Applying neural network (NN) methods in games can lead to various new and exciting game dynamics not previously possible. However, they also lead to new challenges such as the lack of large, clean datasets, varying player skill levels, and changing g
Externí odkaz:
http://arxiv.org/abs/2107.02052