A5100780530- Topic Modeling
- Natural Language Processing Techniques
- Text Readability and Simplification
- Monoclonal and Polyclonal Antibodies Research
- vaccines and immunoinformatics approaches
- Computational Drug Discovery Methods
- Video Analysis and Summarization
- Sentiment Analysis and Opinion Mining
- Artificial Intelligence in Healthcare
- Biomedical and Engineering Education
- Recommender Systems and Techniques
- Misinformation and Its Impacts
- Image Retrieval and Classification Techniques
University of California, Berkeley
2022
Grammar School
2022
University of Waterloo
2020-2021
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Anuoluwapo Aremu, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Chinenye Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, Harsh Jhamtani, Yangfeng Ji, Shailza Jolly, Mihir Kale, Dhruv Kumar, Faisal Ladhak, Aman Madaan, Mounica Maddela, Khyati Mahajan, Saad Mahamood, Bodhisattwa...
We present a novel iterative, edit-based approach to unsupervised sentence simplification. Our model is guided by scoring function involving fluency, simplicity, and meaning preservation. Then, we iteratively perform word phrase-level edits on the complex sentence. Compared with previous approaches, our does not require parallel training set, but more controllable interpretable. Experiments Newsela WikiLarge datasets show that nearly as effective state-of-the-art supervised approaches.
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on constantly evolving ecosystem of automated metrics, datasets, human evaluation standards. Due to this moving target, new models often still evaluate divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging identify the limitations current opportunities progress. Addressing limitation, GEM provides...
We introduce DebateBench, a novel dataset consisting of an extensive collection transcripts and metadata from some the world's most prestigious competitive debates. The consists British Parliamentary debates debating tournaments on diverse topics, annotated with detailed speech-level scores house rankings sourced official adjudication data. curate 256 speeches across 32 each debate being over 1 hour long input average 32,000 tokens. Designed to capture long-context, large-scale reasoning...
Revision is an essential part of the human writing process. It tends to be strategic, adaptive, and, more importantly, iterative in nature. Despite success large language models on text revision tasks, they are limited non-iterative, one-shot revisions. Examining and evaluating capability for making continuous revisions collaborating with writers a critical step towards building effective assistants. In this work, we present human-in-the-loop system, Read, Revise, Repeat (R3), which aims at...
We identify discussion communities on the Reddit social curation platform that frequently mention COVID-19related terms, and we apply Non-negative Matrix Factorization (NMF) topic modelling algorithm to extract topics discussed by these communities. In addition forums dedicated COVID-19, find such discussions general question-answering forums, a forum for teenagers, advice specifically medical, mental health, relationship, legal advice. Topic results reveal hardships of life during pandemic...
ADVANCING APPLIED TECHNOLOGY EDUCATION IN BIOMEDICINE THROUGH UNDERGRADUATE-LED COHORT-BASED TRAINING PROGRAMS
The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to experimental study, have become popular research topics. As the computational community has grown, in order benchmark various advances methodology, organizations Drug Design Data Resource begun hosting blinded grand challenges seeking identify best methods for ligand pose-prediction, affinity ranking, free energy...
ABSTRACT The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to experimental study, have become popular research topics. As the computational community has grown, in order benchmark various advances methodology, organizations Drug Design Data Resource begun hosting blinded grand challenges seeking identify best methods for ligand pose-prediction, affinity ranking, free...
Nowadays, every user purchases a ticket based on the movie's story ranking. Some users enjoy horror films, fight and other genres; similarly, can choose film purchase ticket. In our proposed model, we use Neural Network with recommended system to provide better movies movie ratings feedback. We get accuracy have by using Networks user's previous experience. improved prediction level of in model feedback ratings. Compared existing system, provides recommends that tickets booking.