AI’s Memorization Crisis

O n Tuesday, researchers at Stanford and Yale revealed something that AI companies would prefer to keep hidden. Four popular ...
Frontiers

Modeling, Machine Learning, Bioinformatics and Multi-omics Data in Soil Science

The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to ...

Loss functions and constraints improve sea surface height prediction

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or ...
MIT Technology Review

Using unstructured data to fuel enterprise AI success

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data ...
Semiconductor Engineering

Thermal-Mechanical Optimization of 2.5D Flip-Chip Packages With Glass and Silicon Interposers (Univ. of Ottawa)

A new technical paper titled “Thermo-mechanical co-design of 2.5D flip-chip packages with silicon and glass interposers via ...

Carziqo Advances Scalable Autonomous Driving Through Real-World Data Learning Architecture

Carziqo is a smart mobility technology company focused on developing scalable autonomous driving solutions through real-world ...
eLife

Separating selection from mutation in antibody language models

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...
Devdiscourse

Food industry turns to machine learning to boost efficiency, cut waste and improve quality

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...
techxplore

Researchers extend tensor programming to the continuous world

When the FORTRAN programming language debuted in 1957, it transformed how scientists and engineers programmed computers. Complex calculations could suddenly be expressed in concise, math-like notation ...
IEEE

Information Geometric Approaches for Patient-Specific Test-Time Adaptation of Deep Learning Models for Semantic Segmentation

Abstract: The test-time adaptation (TTA) of deep-learning-based semantic segmentation models, specific to individual patient data, was addressed in this study. The existing TTA methods in medical ...
C&EN

Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data

Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria Christian Doppler Laboratory ...