MOE News
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2025
Jan / 15
- MiniMax Unveils MiniMax-01 Series, a New Family of Foundation Models Built to Handle Ultra-Long Contexts and Enhance AI Agent Development
- Jan 15, 2025 at 07:46 am
- MiniMax is perhaps today best known here in the U.S. as the Singaporean company behind Hailuo, a realistic, high-resolution generative AI video model that competes with Runway, OpenAI's Sora and Luma AI's Dream Machine.
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2025
Jan / 14 -
2025
Jan / 12
- n computer science, has experience in machine learning and distributed systems.
- Jan 12, 2025 at 08:45 am
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2025
Jan / 05 -
2025
Jan / 03
- Spotlight on Seven Leading AI-Powered Crypto Projects Shaping a New Digital Landscape
- Jan 03, 2025 at 09:41 pm
- The fusion of blockchain technology with artificial intelligence (AI) is heralding a revolutionary phase in digital innovation. This convergence has spurred the development of AI-powered crypto projects that offer autonomous decision-making and continuous service.
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2024
Dec / 29
- ReMoE: ReLU-based Mixture-of-Experts Architecture for Scalable and Efficient Training
- Dec 29, 2024 at 04:05 pm
- The development of Transformer models has significantly advanced artificial intelligence, delivering remarkable performance across diverse tasks. However, these advancements often come with steep computational requirements, presenting challenges in scalability and efficiency. Sparsely activated Mixture-of-Experts (MoE) architectures provide a promising solution, enabling increased model capacity without proportional computational costs. Yet, traditional TopK+Softmax routing in MoE models faces notable limitations. The discrete and non-differentiable nature of TopK routing hampers scalability and optimization, while ensuring balanced expert utilization remains a persistent issue, leading to inefficiencies and suboptimal performance.
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2024
Dec / 27
- DeepSeek-V3: A 671B Mixture-of-Experts Language Model From DeepSeek-AI
- Dec 27, 2024 at 12:32 pm
- The field of Natural Language Processing (NLP) has made significant strides with the development of large-scale language models (LLMs). However, this progress has brought its own set of challenges. Training and inference require substantial computational resources, the availability of diverse, high-quality datasets is critical, and achieving balanced utilization in Mixture-of-Experts (MoE) architectures remains complex. These factors contribute to inefficiencies and increased costs, posing obstacles to scaling open-source models to match proprietary counterparts. Moreover, ensuring robustness and stability during training is an ongoing issue, as even minor instabilities can disrupt performance and necessitate costly interventions.
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2024
Dec / 26 -
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Communtity feeds
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- Twitter source
- Christiaan Feb 25, 2025 at 03:16 pm
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- Mario Nawfal’s Roundtable Feb 25, 2025 at 03:11 pm
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- Ki Young Ju Feb 25, 2025 at 02:09 pm
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- Lookonchain Feb 25, 2025 at 01:40 pm
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- Crypto Rover Feb 25, 2025 at 01:28 pm
- $BTC We are there at that dotted vertical pink line give or take a few bars. February 2017 Stick with me. Don't you dare get shaken out now
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- Miles Deutscher Feb 25, 2025 at 12:36 pm
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