Christian Schulz’s Post

Researchers from Carnegie Mellon University and Princeton University introduced the Mamba architecture, an approach that challenges traditional transformers in processing efficiency and memory usage. In tests, Mamba exceeded the performance of similar-sized transformers in both speed and accuracy across tasks like text generation and DNA sequence prediction. Read our summary of the paper in #TheBatch: https://hubs.la/Q02sWR050

Could this new architecture (Mamba) replace transformers? "A relatively small Mamba produced tokens five times faster and achieved better accuracy than a vanilla transformer of similar size." #AI #ML #deeplearning #transformers #mamba

DeepLearning.AI, It's truly intriguing to see the paradigm shift in AI architecture brought about by Mamba. The fact that it outperforms similar-sized transformers in both processing speed and accuracy is a testament to the innovative minds at Carnegie Mellon University and Princeton University. This could signal a significant leap forward not just for text generation, but also for fields such as genomics, where DNA sequence prediction is crucial. I eagerly look forward to more insights from TheBatch on how this development might impact various industries.

A new alternative(Mamba) to the transformer approach is gaining momentum. Mamba provides an alternative architecture that can accommodate very long input sequences while processing them more efficiently. #mamba #transformers #genAI #generativeAI #llm #deeplearning

“Mamba” architecture, challenging Transformers, is gaining momentum. This is a great read to understand why is Mamba interesting and what the benefits are over the more traditional Transformer architecture. Highly recommended.

It’s extremely validating when DeepLearning.AI corroborates a conclusion that you made not long after the paper came out nearly 4 months ago. I’m glad people are really starting to see the benefits of Mamba and other “RNN-like” architectures when compared to Transformers. Hybrid model architectures that combine the two are next.

Research in AI is just started and in the last year made unbelievable progress. Adoption from small businesses is what is driving this pace and researchers find increasingly better and more efficient paradigms. Trasformers are not yet obsolete, but we can't really predict what will be released next year.

I guess many of you heard about the UltraFastBERT paper. But I want to emphasize an integral part of the main approach and the authors' earlier work: Fast Feedforward Networks https://lnkd.in/dFvq9if7 The authors propose a binary tree where each decision criterion is modeled by a tiny neural network + sigmoid. The essence: During training, error from each leaf is integrated through sigmoid weights, throughout the training sigmoids are hardened, and during inference a single path is selected based on hard sigmoids. So error propogation goes through each leaf during training and this helps training. And during inference only one child is selected which makes the inference ultra fast. Neat idea and gives one further thoughts about how to utilize hybrid tree and neural network architectures. #decisiontrees #neuralnetworks #llm #transformers #artificialintelligence #ai

Fast Feedforward Networks (FFF) can be 6x quicker than Mixture-of-Experts networks ⚡️ 🌳 FFF involves a binary tree with smaller neural networks as leaves. Non-leaf nodes host compact neural networks to find the input-dependent path. ⚠️ The FFF model's main challenge is its overly deep tree, causing fragmentation. This undermines learning as individual leaves are rarely used. 👀 Yet, despite obstacles, this approach is highly promising for scenarios that require fast inference and encoding minor details. 🔍 FFF networks can increase speed by maintaining 94.2% of predictive performance with only 1% layer neurons during inference, offering potential for vision transformers and tasks requiring fast inference and compact encoding. —————————————————— Want to stay at the forefront of Generative AI developments? Follow NLPlanet for daily insights into the most relevant news, guides, and research! 🚀

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