85+
Research Papers
32
Active Projects
14
Open Datasets
NeuroEvolution: Self-Optimizing Neural Networks Through Genetic Algorithms
Alex Johnson, Daniel Lee • Aug 15, 2023
This paper introduces a novel approach to neural network optimization using genetic algorithms that dynamically evolve network architectures. Our method demonstrates significant improvements in both training speed and model performance across multiple domains.
Multi-Modal Attention Mechanisms for Improved Visual Reasoning
Sarah Chen, Robert Patel • Nov 2, 2023
We propose a new attention mechanism that integrates visual and textual information more effectively than previous approaches. Our results show a 24% improvement on the Visual QA benchmark and 18% on the CLEVR dataset.
Transfer Learning Optimization for Low-Resource Languages
Jamal Ibrahim, Luis Garcia, Emma Schmidt • Sep 27, 2023
This research addresses the challenges of applying NLP to low-resource languages through specialized transfer learning techniques. We demonstrate state-of-the-art results on 12 previously underrepresented languages.
Quantum-Inspired Neural Networks for Combinatorial Optimization
Jennifer Wu, Thomas Müller • Oct 19, 2023
We present a novel neural network architecture inspired by quantum computing principles to solve complex combinatorial optimization problems. Our approach significantly outperforms traditional methods on traveling salesman and graph coloring tasks.
Hierarchical Reinforcement Learning for Robotic Control
Marcus Zhang, Priya Patel, David Chen • Jan 15, 2024
This paper presents a hierarchical approach to reinforcement learning that enables robots to learn complex, multi-stage tasks more efficiently. Our method shows 35% faster learning and 42% better performance on manipulation tasks.
Foundation Models for Scientific Discovery: Protein Structure Prediction
Elena Rodriguez, Samantha Kim, Oliver Brown • Feb 28, 2024
We introduce a new foundation model architecture specifically designed for scientific discovery, focusing on protein structure prediction. Our approach leverages both evolutionary and physical constraints to achieve state-of-the-art accuracy.
Federated Learning With Privacy Guarantees for Healthcare Applications
Jordan Lee, Aisha Hassan, Ryan Mitchell • Mar 10, 2024
This paper presents a novel federated learning approach with formal privacy guarantees suitable for sensitive healthcare data. We demonstrate how multi-party computation can be combined with differential privacy to enable collaborative model training without data sharing.
Emergent Abilities in Large Language Models: Analysis and Prediction
Nadia Williams, Kojo Asante, Wei Zhang • Apr 5, 2024
Our research provides a framework for understanding and predicting emergent abilities in large language models. By analyzing the correlation between model size, training data, and task performance, we identify key threshold points where new capabilities emerge.
Key Research Areas
Our teams are focused on pushing boundaries in these critical domains
Foundational Model Research
Advancing the core capabilities of large-scale AI models through innovations in architecture, training methodologies, and optimization techniques.
Trustworthy AI
Developing methods to make AI systems more reliable, safe, fair, and aligned with human values and intentions.
Multimodal Intelligence
Creating AI systems that can seamlessly understand and generate content across multiple modalities including text, images, audio, and video.
AI for Scientific Discovery
Applying AI to accelerate breakthroughs in science and medicine through hypothesis generation, experiment design, and data analysis.
Human-AI Collaboration
Exploring new paradigms for humans and AI systems to work together, enhancing human capabilities while addressing AI limitations.
Embodied Intelligence
Building AI systems that can perceive, reason about, and act in the physical world through robotics and simulation.