Biomedical Science and Machine Learning - A Two-Way Street (2023)
Explore the convergence of biomedical science and machine learning, discovering how these fields synergize to address modern biological questions and drive innovations in data analysis.
Learning to Read and Write Protein Evolution
Explore protein language models and their role in understanding evolutionary rules, predicting mutation effects, and guiding artificial evolution for antibody development and novel protein structures.
Deep Learning on Electronic Health Records: Unlocking the Power of Clinical Data - Lecture 1
Unlock the power of EHR data using deep learning NLP. Explore frameworks for extracting clinical information, leveraging pretrained transformer models, and addressing challenges in applying language models to healthcare data.
Artificial Intelligence for Opportunistic Screening of Cardiovascular Disease
Explore AI applications in opportunistic screening for cardiovascular disease, focusing on innovative approaches to early detection and prevention.
Active Learning for Absolute Binding Free Energy Calculations in Drug Discovery
Explore innovative active learning techniques for absolute binding free energy calculations, enhancing drug discovery processes and efficiency.
Physics Informed Representation Learning for Therapeutics
Dive into physics-informed representation learning for therapeutics with MIT Assistant Professor Rafael Gomez-Bombarelli, exploring innovative approaches in drug discovery and development.
Applying Machine Learning on Drug Discovery Projects
Dive into practical applications of machine learning in drug discovery projects with insights from Relay Therapeutics' Chief Data Officer, Pat Walters.
Machine Learning in Drug Discovery - Lightning Talks
Explore cutting-edge ML applications in drug discovery through lightning talks on antibody optimization, PROTAC design, small molecule binders, and protein-fragment interactions.
What's Holding Back AI in Drug Discovery?
Explore the challenges impeding AI's progress in drug discovery with insights from DeepCure's CEO Kfir Schreiber, shedding light on potential solutions and future directions.
Temperature Drives Cardiac Function
Uncover how temperature impacts cardiac function, exploring contractility changes during hypothermia and fever, and the potential of electronic health records for data mining.
Artificial Intelligence in Cardiology: Where We Are and Where We Are Going
Explore AI's impact on cardiology, from current applications to future potential. Gain skills to evaluate AI research and understand key terminology.
Using Artificial Intelligence to Predict Shock-Refractory Ventricular Fibrillation
Explore how AI-powered ECG analysis during CPR can predict shock-refractory ventricular fibrillation, enhancing cardiac arrest resuscitation strategies and patient outcomes.
MIRROR: A Multi-View Reciprocal Recommender System for Online Recruitment - Domain Specific RecSys
Explore a multi-view reciprocal recommender system for online recruitment, enhancing job matching efficiency and candidate-employer satisfaction.
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation - Lecture 6
Explore innovative techniques for multimedia-based new item recommendation using multimodality invariant learning, enhancing recommendation systems' performance and user experience.
Negative Sampling Techniques for Dense Passage Retrieval in a Multilingual Setting - Lecture 4
Explore negative sampling techniques for dense passage retrieval in multilingual settings, enhancing information retrieval across languages.
AI and Neuroengineering - Virtual Webinar Series
Explore the intersection of AI and neuroengineering in this virtual webinar featuring insights from speaker Xiaoxiao Sun.
Leveraging Business Intelligence in ERP Implementations
Unlock strategies for integrating business intelligence into ERP implementations, maximizing technology investments and enhancing analytics capabilities.
NFARec: A Negative Feedback-Aware Recommender Model - Recommendation Systems
Explore a novel recommender model that incorporates negative feedback for improved personalized recommendations in complex user-item interaction scenarios.
Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation - Session M3.5
Explore critical insights into beyond-accuracy performance metrics in next basket recommendation systems, challenging current assumptions and methodologies.
Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal Matching - Multimodal Session 3.3
Explore uncertainty-guided noisy correspondence learning for efficient cross-modal matching in multimodal systems, enhancing data analysis and information retrieval techniques.
DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation - Lecture 4
Explore a novel approach to group recommendation using disentangled hypergraph masked autoencoders, enhancing accuracy and efficiency in collaborative filtering systems.