Tag: Machine Learning

AI Nature Language Tech Research

Are Multilingual Language Models Fragile? IBM Adversarial Attack Strategies Cut MBERT QA Performance by 85%

An IBM research team proposes four multilingual adversarial attack strategies and attacks seven languages in a zero-shot setting on large multilingual pretrained language models (e.g. MBERT), reducing average performance by up to 85.6 percent.

AI AIoT Machine Learning & Data Science Research

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 Popular Research

NVIDIA, Stanford & Microsoft Propose Efficient Trillion-Parameter Language Model Training on GPU Clusters

A research team from NVIDIA, Stanford University and Microsoft Research propose a novel pipeline parallelism approach that improves throughput by more than 10 percent with a comparable memory footprint, showing such strategies can achieve high aggregate throughput while training models with up to a trillion parameters.

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

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.