AIthority || Considered the “black box” of machine learning, AI neural networks can expand the range of innovations and applications in different areas. AGOP is a unifying mathematical mechanism that establishes the core fundamentals explaining how neural networks learn from general machine learning models.
UC San Diego Today || The insights, published in the journal Science, can also be used to make other types of machine learning architectures more effective.
Science || Work by TILOS Foundations team member Mikahil Belkin and TILOS graduate students presents a unifying mathematical mechanism called Average Gradient Outer Product (AGOP) that characterizes feature learning in neural networks.
MIT Technology Review || An interview with TILOS Foundations team member Mikhail (Misha) Belkin about one of the biggest scientific puzzles of our time.
Notre Dame Observer || Professor Henrik Christensen speaks to the challenges and benefits of using automated vehicles for trips of short to moderate distance (zero to five miles), which account for a staggering 60% of all travel in the United States.
AI Magazine || Optimization is a universal quest, reflecting the basic human need todo better. Improved optimizations of energy-efficiency, safety, robustness, and other criteria in engineered systems would bring incalculable societal benefits. But, fundamental challenges of scale and complexity keep many such real-world optimization needs beyond reach. TILOS aims to overcome these challenges in three […]
National University News || Bridging mathematics and technology: National University’s mathematics department incorporates data science courses developed through TILOS into an updated Bachelor of Science degree program.
MIT News || Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.
University Business || “The CHIPS ACT [is] a specific example of how we want to retake high-tech manufacturing and put it on American shores so that American workers benefit,” says Jodi Reeves, chair of National University’s Department of Data Science & Analytics. “That’s something that all educational institutions want to support.”
As of January 2024, TILOS Foundations team member Misha Belkin is the editor-in-chief of the Society for Industrial and Applied Mathematics (SIAM) Journal on Mathematics of Data Science (SIMODS), a leading journal on the foundations of data science.
