Cornell University

Recent advancements in (self) supervised learning models have been driven by empirical scaling laws, where a model's performance scales with its size. However, such scaling laws have been challenging to establish in reinforcement learning (RL). Unlike...
Large Language Models (LLMs) have significantly evolved in recent times, especially in the areas of text understanding and generation. However, there have been certain difficulties in optimizing LLMs for more effective human instruction delivery. While LLMs...

Latest Computer Vision Research From Cornell and Adobe Proposes An Artificial Intelligence (AI) Method To Transfer The Artistic Features Of An Arbitrary Style Image...

Art is a fascinating yet extremely complex discipline. Indeed, the creation of artistic images is often not only a time-consuming problem but also requires...

Researchers at MIT, Cornell, and McGill University created a new Machine Learning Model that, on its own discovers Linguistic Rules that often match up...

The capacity of humans to develop theories about the world is a fundamental feature of intelligence. The recorded history of science is where this...

Meet ‘BirdNET Sound ID App’: An AI-Powered Bird Sound Recognition App Using A Neural Network To Identify Birds By The Sounds They Make

Only recently did we learn about the enormous amount of work that goes into the BirdNET platform. Cornell Lab of Ornithology and Chemnitz University...

Cornell Physicists And Computer Scientists Collaborated To Build An Unsupervised And Interpretable Machine Learning Algorithm, XRD Temperature Clustering (X-TEC)

Modern X-ray facilities have acquired a significantly higher fraction of this data during the past ten years, thanks to advancements in source brightness and...

Researchers From MIT and Cornell Develop STEGO (Self-Supervised Transformer With Energy-Based Graph Optimization): A Novel AI Framework That Distills Unsupervised Features Into High-Quality Discrete...

This Article Is Based On The Research Paper 'UNSUPERVISED SEMANTIC SEGMENTATION BY DISTILLING FEATURE CORRESPONDENCES'. All Credit For This Research Goes To The Researchers...

Google and Cornell Researchers Introduce FLASH: A Machine Learning Model That can Achieve High Transformer Quality in Linear Time

The introduction of attention-based transformer architectures has permitted numerous language and vision tasks improvements. However, their use is limited to small context sizes due...

Cornell And NTT Researchers Introduces Deep Physical Neural Networks To Train Physical Systems To Perform Machine Learning Computations Using Backpropagation

Deep-learning models have become commonplace in all fields of research and engineering. However, their energy requirements are limiting their ability to scale. Synergistic hardware...

Cornell and Harvard University Researchers Develops Correlation Convolutional Neural Networks (CCNN): To Determine Which Correlations Are Most Important

A team of researchers from Cornell and Harvard University introduces a novel approach to parse quantum matter and make crucial data distinctions. This proposed...

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