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ETH Zurich Leverages Spiking Neural Networks To Build Ultra-Low-Power Neuromorphic Processors

A research team from ETH Zurich leverages existing spike-based learning circuits to propose a biologically plausible architecture that is highly successful in classifying distinct and complex spatio-temporal spike patterns. The work contributes to the design of ultra-low-power mixed-signal neuromorphic processing systems capable of distinguishing spatio-temporal patterns in spiking activity.

AI Machine Learning & Data Science Research

TUM, Google, Nvidia & LMU München’s CodeTrans Pretrained Models Crack Source Code Tasks With SOTA Performance

A research team from Technical University of Munich, Google, Nvidia and LMU München proposes CodeTrans, an encoder-decoder transformer model which achieves state-of-the-art performance on six tasks in the software engineering domain, including Code Documentation Generation, Source Code Summarization, Code Comment Generation, etc.

AI Machine Learning & Data Science Research

Improving ML Fairness: IBM, UMich & ShanghaiTech Papers Focus on Statistical Inference and Gradient-Boosting

A team from University of Michigan, MIT-IBM Watson AI Lab and ShanghaiTech University publishes two papers on individual fairness for ML models, introducing a scale-free and interpretable statistically principled approach for assessing individual fairness and a method for enforcing individual fairness in gradient boosting suitable for non-smooth ML models.

AI Machine Learning & Data Science Research

Microsoft & Princeton’s Surprising Discovery: Text-Game Agents Achieve High Scores in Complete Absence of Semantics

A research team from Princeton University and Microsoft Research discover autonomous language-understanding agents are capable of achieving high scores even in the complete absence of language semantics, indicating that current RL agents for text-based games might not be sufficiently leveraging the semantic structure of game texts.