A5011309559- Natural Language Processing Techniques
- Genetic factors in colorectal cancer
- Adversarial Robustness in Machine Learning
- BRCA gene mutations in cancer
- Generative Adversarial Networks and Image Synthesis
- Digital Media Forensic Detection
- Colorectal Cancer Screening and Detection
- Colorectal Cancer Treatments and Studies
- linguistics and terminology studies
- Health Sciences Research and Education
- Speech Recognition and Synthesis
- Cancer Genomics and Diagnostics
- Gastric Cancer Management and Outcomes
- Advanced Neural Network Applications
- Topic Modeling
- DNA Repair Mechanisms
- Global Cancer Incidence and Screening
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Data Classification
Pancreatic Cancer Action Network
2016
Alex Warstadt, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, Bhargavi Paranjabe, Adina Williams, Tal Linzen, Ryan Cotterell. Proceedings of the BabyLM Challenge at 27th Conference on Computational Natural Language Learning. 2023.
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, faithfulness of underlying problems. Neglecting fundamental importance data given rise inaccuracy, bias, fragility in real-world applications, is hindered by saturation across existing dataset benchmarks. In response, we present DataPerf, a community-led benchmark suite evaluating data-centric algorithms. We aim foster...
Human feedback plays a central role in the alignment of Large Language Models (LLMs). However, open questions remain about methods (how), domains (where), people (who) and objectives (to what end) human collection. To navigate these questions, we introduce PRISM, new dataset which maps sociodemographics stated preferences 1,500 diverse participants from 75 countries, to their contextual fine-grained 8,011 live conversations with 21 LLMs. PRISM contributes (i) wide geographic demographic...
With text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks mitigate the generation of offensive images. By focusing on "implicitly adversarial" prompts (those that trigger T2I generate unsafe images for reasons), we isolate a set difficult safety issues human creativity well-suited uncover. To this end, built Adversarial Nibbler Challenge, red-teaming methodology crowdsourcing diverse implicitly adversarial...
The generative AI revolution in recent years has been spurred by an expansion compute power and data quantity, which together enable extensive pre-training of powerful text-to-image (T2I) models. With their greater capabilities to generate realistic creative content, these T2I models like DALL-E, MidJourney, Imagen or Stable Diffusion are reaching ever wider audiences. Any unsafe behaviors inherited from pretraining on uncurated internet-scraped datasets thus have the potential cause...
The TTT program consisted of didactics and seminars to capacitate participants become trainers in CRC prevention. This project was evaluated using three components: (1) training workshops; (2) community educational sessions; (3) the participant's experience as a trainer. Pre - post-tests on screening knowledge were given participants. Program effectiveness determined by pre- post-tests, number workshop completing session within months members reached.
With the rise of text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks mitigate generation offensive images. By focusing on ``implicitly adversarial'' prompts (those that trigger T2I generate unsafe images for reasons), we isolate a set difficult safety issues human creativity well-suited uncover. To this end, built Adversarial Nibbler Challenge, red-teaming methodology crowdsourcing diverse implicitly...
Abstract Background: Cancer is the second most common cause of death in Puerto Rico (PR) after heart disease. Colorectal cancer (CRC) commonly diagnosed among both men and women. The screening guidelines given by American Society indicate that adults should begin CRC at age 50. Although self-reported from 1997 to 2006 have showed an increase PR, trends are lower than United States. With low rates high mortality health promotion programs aimed education prevention needed. Methods: purpose...