we review what we know about LSTM networks and explore their new promising development – xLSTM
we discuss the limitations of RAG with a long-context window and explore the intuition behind the LongRAG framework to address these limitations, along with a list of resources for further learning
we discuss how Kolmogorov-Arnold Networks (KANs) are redefining neural network architectures and their advantages over traditional multilayer perceptrons
we discuss the breakthroughs in GPU optimization techniques, exploring how YaFSDP surpasses FSDP in enhancing efficiency and scalability for large language models.
we discuss the Joint Embedding Predictive Architecture (JEPA), how it differs from transformers and provide you with list of models based on JEPA
we discuss the limitations of RAG and explore the benefits of Graph RAG approach, as well as clarify terms and provide a list of resources
We discuss sequence modeling, the drawbacks of transformers, and what Mamba brings into play
we discuss the origins of MoE, why is it better than one neural network, Sparsely-Gated MoE, and sudden hype. Enjoy the collection of helpful links
