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

We discuss sequence modeling, the drawbacks of transformers, and what Mamba brings into play

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 the Joint Embedding Predictive Architecture (JEPA), how it differs from transformers and provide you with list of models based on JEPA

we discuss the breakthroughs in GPU optimization techniques, exploring how YaFSDP surpasses FSDP in enhancing efficiency and scalability for large language mode

we discuss how Kolmogorov-Arnold Networks (KANs) are redefining neural network architectures and their advantages over traditional multilayer perceptrons

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 review what we know about LSTM networks and explore their new promising development – xLSTM

We explore the recent Speculative RAG idea, highlighting where it excels, and discuss the limitations of other RAG systems. Bonus: Resources

We discuss the innovation suggested by the DeepSeek team, how it improves the models' performance, and dive into the architectures and implementation

we compare three distinct approaches, all called Chain of Knowledge, and suggest how they can be combined for better reasoning

we discuss the innovative combination of VectorRAG and GraphRAG in HybridRAG, its impact on financial document analysis and other areas of implementation, and clarify related terms for better understanding

Explore how open-source idea enhances MoE architecture

we compare three fine-tuning methods – DoRA, QLoRA, and QDoRA – each designed to improve model performance and memory efficiency in different ways.

Explore how OpenAI made their automatic speech recognition (ASR) model multilingual and multitasking

Explore how MLLMs can visually "think" step-by-step

we trace Mistral's strategic roadmap and and unpack the unique performance of les Ministraux (Ministral)

Explore how transformers can select different depths of processing and reduce compute needs

Let's explore a smarter Vision-Language Model (VLM) that thinks step-by-step

Explore the key concepts of Flow Matching, its relation to diffusion models, and how it can enhance the training of generative models

Explore how RL can be blended with natural language