Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
Labroots

When Data Aren't Comparable: Why Harmonization Matters in High Throughput Proteomic

Do you agree? Data normalization isn’t the finish line. Harmonization is. Even after basic normalization, datasets can drift ...
Interesting Engineering

Anthropic says DeepSeek, other Chinese AI firms scraped Claude to train rival models

We’ve identified industrial-scale distillation attacks on our models by DeepSeek, Moonshot AI, and MiniMax. These labs ...
IEEE

Enhancing Explainability and Transparency in Deep Learning Models for Healthcare Applications

Abstract: Recently, it has been observed that deep learning models can be very useful in handling healthcare facilities mostly medical diagnosis and management. However, they are not interpretable and ...
IEEE

MoDL-PU: Model-Based Deep Learning for InSAR Phase Unwrapping

Abstract: Phase unwrapping (PU) is a critical process for numerous synthetic aperture radar interferometry (InSAR) applications. The advent of deep learning (DL) has revolutionized PU, with various DL ...