Mamba
PulseAugur coverage of Mamba — every cluster mentioning Mamba across labs, papers, and developer communities, ranked by signal.
- instance of State Space Model 90%
- instance of State Space Models 90%
- competes with State space models: Univariate representation of a multivariate model, partial interpolation and periodic convergence 70%
- used by State Space Model 70%
- competes with CNN 70%
- instance of State space models: Univariate representation of a multivariate model, partial interpolation and periodic convergence 60%
- instance of long short-term memory 60%
- affiliated with State Space Models 50%
8 天有情绪数据
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Deep learning models show promise in pavement, aero-engine, and affect recognition tasks
Researchers are exploring deep learning models for predictive maintenance and performance analysis across various domains. One study utilizes CNN and LSTM networks with extensive pavement condition data from Texas to mo…
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COMMA network enhances 3D dispersed vessel segmentation with coordinate awareness
Researchers have developed a new network architecture called COMMA for segmenting 3D dispersed blood vessels in medical imaging. This Coordinate-aware Modulated Mamba Network utilizes both global and local branches to m…
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Mamba 主干驱动新高效神经组合优化框架
研究人员开发了 ECO，一个利用 Mamba 主干的高效神经组合优化框架。该方法将轨迹生成与梯度更新分离，采用监督预热阶段，然后对批量候选集进行迭代式直接偏好优化。该框架包含一个混合 Mamba 编码器-解码器来管理内存增长并提高硬件效率，以及一个局部搜索引导的引导策略来稳定训练。与现有的神经基线相比，ECO 在旅行商问题和有容量车辆路径问题基准测试中展现出卓越的性能、内存效率和吞吐量。
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AdaMamba framework integrates adaptive frequency analysis for improved time series forecasting
Researchers have introduced AdaMamba, a new framework designed for long-term time series forecasting. This model addresses the challenge of cross-domain heterogeneity in real-world data by adaptively integrating frequen…
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New Mamba model variant enhances memory retention and bilinear computation
Researchers have introduced Bilinear Input Modulation (BIM) to enhance Selective State Space Models (SSMs), specifically Mamba, by incorporating state-input products. This augmentation allows for improved memory retenti…
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序列模型预测心力衰竭患者的不稳定性和死亡率
研究人员开发了序列模型，利用电子健康记录来预测心力衰竭患者一年内的临床不稳定性和死亡率。该研究对瑞典一个由超过42,000名患者组成的队列进行，采用了一种将结构化电子健康记录数据转化为患者序列的框架。Llama等模型表现出强大的预测能力，优于传统方法，并且即使在临床概念或训练数据有限的情况下也显示出稳健性。
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NVIDIA 发布 Nemotron 3 Nano Omni，统一多模态 AI 以提高效率
NVIDIA 发布了 Nemotron 3 Nano Omni，这是一个开放的多模态模型，能够处理文本、图像、音频和视频。该模型旨在将这些模态统一到单一架构中，从而提高效率并实现更复杂的人工智能智能体。Nemotron 3 Nano Omni 在文档智能、音频理解和视频分析的基准测试中表现出色，与之前的模型和替代方案相比，在吞吐量和推理速度方面均有显著提升。
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MambaCSP model offers hardware-efficient CSI prediction with hybrid attention
Researchers have developed MambaCSP, a new AI model designed for efficient channel state prediction in wireless networks. This model utilizes a hybrid-attention state space architecture, combining the linear-time effici…
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Apple enables parallel RNN training, challenging transformer dominance
Apple researchers have developed ParaRNN, a new framework that enables parallel training of nonlinear Recurrent Neural Networks (RNNs). This advancement overcomes the historical sequential bottleneck in RNN training, ac…
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New AI models enhance image and video super-resolution with diffusion and efficient architectures
Researchers are developing new methods for image and video super-resolution using advanced AI techniques. Several papers explore diffusion models for joint spatiotemporal super-resolution, enabling adaptation across dif…
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Apple researchers unveil parallel RNN training and enhanced SSMs at ICLR 2026
Apple researchers are presenting new work at ICLR 2026, focusing on advancements in recurrent neural networks (RNNs) and state space models (SSMs). Their paper "ParaRNN" introduces a parallelized training framework that…
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Memristor-based AI systems show promise for efficient learning and neuromorphic computing
Researchers are exploring Self-Organising Memristive Networks (SOMNs) as a physical alternative to conventional hardware for artificial intelligence, aiming for energy-efficient, brain-like continual learning. These net…
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Together AI 发布 NVIDIA 的多模态和 1M 上下文 Nemotron 3 模型
Together AI 已在其平台上发布了 NVIDIA 的 Nemotron 3 模型，包括多模态的 Nano Omni 和大上下文的 Super。Nemotron 3 Nano Omni 是一个 30B 参数模型，擅长同时处理视频、图像、音频和语言的推理，非常适合代理应用。Nemotron 3 Super 是一个 120B 参数模型，拥有 100 万个 token 的上下文窗口和多 token 预测，可高效处理复杂的推理和长上下文…
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Mamba model offers Transformer-level performance with faster inference and longer context
Mamba, a new State Space Model (SSM), presents an alternative to the dominant Transformer architecture in AI. It aims to match Transformer performance and scaling laws while efficiently handling extremely long sequences…