Zobrazeno 1 - 10
of 335
pro vyhledávání: '"Pham, Chau"'
AI-powered chatbots (ChatGPT, Claude, etc.) require users to create an account using their email and phone number, thereby linking their personally identifiable information to their conversational data and usage patterns. As these chatbots are increa
Externí odkaz:
http://arxiv.org/abs/2407.08792
Autor:
Dhanania, Garima, Mysore, Sheshera, Pham, Chau Minh, Iyyer, Mohit, Zamani, Hamed, McCallum, Andrew
Topic models are widely used to analyze document collections. While they are valuable for discovering latent topics in a corpus when analysts are unfamiliar with the corpus, analysts also commonly start with an understanding of the content present in
Externí odkaz:
http://arxiv.org/abs/2406.19928
Existing research on instruction following largely focuses on tasks with simple instructions and short responses. In this work, we explore multi-constraint instruction following for generating long-form text. We create Suri, a dataset with 20K human-
Externí odkaz:
http://arxiv.org/abs/2406.19371
Autor:
Schulhoff, Sander, Ilie, Michael, Balepur, Nishant, Kahadze, Konstantine, Liu, Amanda, Si, Chenglei, Li, Yinheng, Gupta, Aayush, Han, HyoJung, Schulhoff, Sevien, Dulepet, Pranav Sandeep, Vidyadhara, Saurav, Ki, Dayeon, Agrawal, Sweta, Pham, Chau, Kroiz, Gerson, Li, Feileen, Tao, Hudson, Srivastava, Ashay, Da Costa, Hevander, Gupta, Saloni, Rogers, Megan L., Goncearenco, Inna, Sarli, Giuseppe, Galynker, Igor, Peskoff, Denis, Carpuat, Marine, White, Jules, Anadkat, Shyamal, Hoyle, Alexander, Resnik, Philip
Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prom
Externí odkaz:
http://arxiv.org/abs/2406.06608
Autor:
Pham, Chau, Plummer, Bryan A.
Multi-Channel Imaging (MCI) contains an array of challenges for encoding useful feature representations not present in traditional images. For example, images from two different satellites may both contain RGB channels, but the remaining channels can
Externí odkaz:
http://arxiv.org/abs/2405.16419
Noisy labels can impair model performance, making the study of learning with noisy labels an important topic. Two conventional approaches are noise modeling and noise detection. However, these two methods are typically studied independently, and ther
Externí odkaz:
http://arxiv.org/abs/2312.00827
Most deep neural networks are trained under fixed network architectures and require retraining when the architecture changes. If expanding the network's size is needed, it is necessary to retrain from scratch, which is expensive. To avoid this, one c
Externí odkaz:
http://arxiv.org/abs/2311.04251
Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea leaves" to interpret; additionally, they offer users minimal contro
Externí odkaz:
http://arxiv.org/abs/2311.01449
Autor:
Chen, Zitong, Pham, Chau, Wang, Siqi, Doron, Michael, Moshkov, Nikita, Plummer, Bryan A., Caicedo, Juan C.
Most neural networks assume that input images have a fixed number of channels (three for RGB images). However, there are many settings where the number of channels may vary, such as microscopy images where the number of channels changes depending on
Externí odkaz:
http://arxiv.org/abs/2310.19224
This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. A typical approach
Externí odkaz:
http://arxiv.org/abs/2310.17109