Zobrazeno 1 - 10
of 4 819
pro vyhledávání: '"A. Helbling"'
Autor:
Naveen, Aryan, Morris, Jalil, Chan, Christian, Mhrous, Daniel, Helbling, E. Farrell, Hyun, Nak-Seung Patrick, Hills, Gage, Wood, Robert J.
Autonomous flapping-wing micro-aerial vehicles (FWMAV) have a host of potential applications such as environmental monitoring, artificial pollination, and search and rescue operations. One of the challenges for achieving these applications is the imp
Externí odkaz:
http://arxiv.org/abs/2411.06382
Autor:
Cho, Aeree, Kim, Grace C., Karpekov, Alexander, Helbling, Alec, Wang, Zijie J., Lee, Seongmin, Hoover, Benjamin, Chau, Duen Horng
Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present Transformer Explainer, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our t
Externí odkaz:
http://arxiv.org/abs/2408.04619
Autor:
Lau, Matthew, Wang, Haoran, Helbling, Alec, Hul, Matthew, Peng, ShengYun, Andreoni, Martin, Lunardi, Willian T., Lee, Wenke
The robustness of machine learning models has been questioned by the existence of adversarial examples. We examine the threat of adversarial examples in practical applications that require lightweight models for one-class classification. Building on
Externí odkaz:
http://arxiv.org/abs/2407.06372
Language Models (LMs) have achieved impressive performance on various linguistic tasks, but their relationship to human language processing in the brain remains unclear. This paper examines the gaps and overlaps between LMs and the brain at different
Externí odkaz:
http://arxiv.org/abs/2407.04680
Recently, researchers have proposed powerful systems for generating and manipulating images using natural language instructions. However, it is difficult to precisely specify many common classes of image transformations with text alone. For example,
Externí odkaz:
http://arxiv.org/abs/2404.04376
Autor:
Lee, Seongmin, Wang, Zijie J., Chakravarthy, Aishwarya, Helbling, Alec, Peng, ShengYun, Phute, Mansi, Chau, Duen Horng, Kahng, Minsuk
While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We pres
Externí odkaz:
http://arxiv.org/abs/2404.01361
Machine learning has enabled the development of powerful systems capable of editing images from natural language instructions. However, in many common scenarios it is difficult for users to specify precise image transformations with text alone. For e
Externí odkaz:
http://arxiv.org/abs/2402.07925
Autor:
Blalock, Justin, Munechika, David, Karanth, Harsha, Helbling, Alec, Mehta, Pratham, Lee, Seongmin, Chau, Duen Horng
The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes. Traditional try-on methods grapple with challenges in adapting to diverse backgrounds, poses, and subjects. While
Externí odkaz:
http://arxiv.org/abs/2402.01877
Recent text-to-image generative models can generate high-fidelity images from text prompts. However, these models struggle to consistently generate the same objects in different contexts with the same appearance. Consistent object generation is impor
Externí odkaz:
http://arxiv.org/abs/2310.06968
Autor:
Phute, Mansi, Helbling, Alec, Hull, Matthew, Peng, ShengYun, Szyller, Sebastian, Cornelius, Cory, Chau, Duen Horng
Large language models (LLMs) are popular for high-quality text generation but can produce harmful content, even when aligned with human values through reinforcement learning. Adversarial prompts can bypass their safety measures. We propose LLM Self D
Externí odkaz:
http://arxiv.org/abs/2308.07308