Microsoft Research proposes energy-efficient time series predictions using spiking neural networks
Spiking neural networks (SNNs), a family of artificial neural networks that mimic the spiking behavior of biological neurons, have been ...
Tag: neural
CircuitNet: A brain-inspired neural network architecture for improved task performance across diverse domains
The success of ANNs is due to their ability to mimic simplified brain structures. Neuroscience reveals that neurons interact through ...
What if game engines could be powered by neural networks? This Google AI paper introduces GameNGen and explores how diffusion models are revolutionizing real-time gaming
A major challenge in ai-powered game simulation is the ability to accurately simulate complex interactive environments in real-time using neural ...
Neural Magic releases GuideLLM: a powerful tool to evaluate and optimize the implementation of large language models (LLM)
Join the fastest growing ai research newsletter read by researchers from Google + NVIDIA + Meta + Stanford + MIT ...
Neural Magic releases LLM Compressor: a new library to compress LLMs and achieve faster inference with vLLM
Neural Magic has released the LLM Compressora state-of-the-art tool for optimizing large language models that enables much faster inference through ...
On a neural implementation of Brenier polar factorization
In 1991, Brenier proved a theorem that generalizes polar decomposition to square matrices, factored as PSDs. ×<annotation encoding="application/x-tex">\times× unitary - ...
UT Austin researchers present PUTNAMBENCH: a comprehensive AI benchmark for evaluating the capabilities of neural theorem provers with Putnam math problems
Automating mathematical reasoning has been a long-standing goal of artificial intelligence, and formal frameworks such as Lean 4, Isabelle, and ...
Nvidia AI launches BigVGAN v2: a next-generation neural vocoder that transforms audio synthesis
In the rapidly developing field of audio synthesis, Nvidia recently introduced BigVGAN v2. This neural vocoder breaks previous records for ...
Revealing the utilized range of learning subspaces in neural networks
In this work, we study how well the learned weights of a neural network utilize the space available to them. ...
What are neural networks with radial basis functions?
Introduction Radial basis function neural networks (RBFNNs) are a type of neural network that uses radial basis functions for activation. ...
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