SwiGLU
PulseAugur coverage of SwiGLU — every cluster mentioning SwiGLU across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New QG-MIL architecture enhances medical imaging analysis accuracy
Researchers have developed QG-MIL, a novel gated transformer aggregator designed to improve the stability and accuracy of multiple instance learning (MIL) in medical imaging. This new architecture addresses issues of ov…
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xFormers library enables memory-efficient Transformer models on GPUs
This tutorial demonstrates how to build memory-efficient Transformer models using the xFormers library on GPUs. It covers implementing and comparing memory-efficient attention with standard attention, analyzing techniqu…
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New neural network architectures tackle complex scientific computing problems · 8 sources tracked
Researchers are developing novel neural network architectures to solve complex partial differential equations (PDEs) and model dynamical systems. These include structure-oriented randomized neural networks (SO-RaNN) for…
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AI research requires discipline, foundational knowledge, and a beginner's mindset
Becoming a successful AI researcher requires a blend of consistent effort and hands-on building, akin to a meditative practice where dedication is key even without immediate insights. Focusing on fundamental concepts ra…
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New MoA FFN Design Enhances LLM Expressivity and Scaling
Researchers have introduced a novel feedforward network (FFN) design called Mixture of Activations (MoA) for large language models (LLMs). MoA utilizes token-adaptive activation mixing, allowing different activation fun…
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Transformer LLM Architectures Converge on Standard Stack
A recent analysis of 53 large language models from 2017 to 2025 reveals a significant convergence in transformer architectures. Key elements of this de facto standard include pre-normalization (RMSNorm), Rotary Position…
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IBM releases Granite 4.1 LLMs with 512K context and Apache 2.0 license
IBM has released the Granite 4.1 family of large language models, comprising 3B, 8B, and 30B parameter versions. These models were trained on approximately 15 trillion tokens through a five-stage pre-training process th…
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MLP skip connections can't be absorbed into residual-free models
Researchers have investigated whether a skip connection around a single-hidden-layer MLP can be absorbed into a residual-free MLP of the same width. They found that for certain activation functions like ReLU^2 and ReGLU…
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Research: Removing LayerNorm in LLMs acts as implicit regularizer, impacting performance based on training data size.
Researchers have investigated the impact of removing Layer Normalization (LayerNorm) from neural network architectures, particularly in models like GPT-2 and Llama. Their findings indicate that replacing LayerNorm with …