京东 11.11 红包
AI/ML+Physics Part 2: Curating Training Data
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Andrew Ng，数据工程对于data-centric AI的重要性
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Computer Architecture 2024，Flash Memory and SSD 15
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目标驱动AI: Towards AI systems that can learn, remember, reason, and plan
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[SIGCOMM2023] : Data Center Programming
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Harvard Efficient ML Seminar，Machine Unlearning
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[SIGCOMM2023] ： Offloading
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[RLC 2024] Andy Barto， In the Beginning ML was RL
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Stanford CS234 Reinforcement Learning，Value Alignment2
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[HOTI2023] Leveraging SmartNICs for HPC and Data Center Applications 2
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Ray Summit 2024，The State of vLLM
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Computer Architecture 2024，Memory Latency II 9
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Terence Tao，AI在科学和数学潜力
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Scaling Ray to 10K NPUs： Huawei's Hyperscale Journey
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Enhancing Python Data Loading in the Cloud for AI⧸ML
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Supermic，现代数据中心液冷 AI 机架集群方案
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Jim Fan 谈 Nvidia 的 Embodied AI Lab 以及老黄关于所有机器人都将实现自主预测
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Stanford CS234 Reinforcement Learning，RLHF&DPO
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Real World Robotics Tutorial7：Bringing Robots to the World
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RISC-V Microprocessors
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视听自监督baby learning，从AI、心理学和神经科学的角度理解低级智力
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Robotics in the Age of Generative AI
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Stanford EE259: Lidar测距和DOA估计
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Seminar in Computer Architecture 2024，计算机架构的未来 II 和基因组分析 3
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可扩展且高效的大模型系统
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Stanford CS149 并行计算，Hardware Specialization 18
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Stanford CS234 Reinforcement Learning，Multi-Agent Game Playing
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Stanford EE364A：Convex Optimization lecture14
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Slaying OOMs with PyTorch FSDP and torchao
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NVIDIA Spectrum-X Network Platform Architecture
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Stanford CS149 并行计算，Transactional Memory 17
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Voxel51：Make Visual AI a Reality
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[CoNEXT2023] Datacenter
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Ray Summit 2024，Apple弹性GPU资源管理
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Stanford EE364A：Convex Optimization lecture1
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Stanford CS149 并行计算，Memory Consistency 12
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Google，Data Centers of the Future
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AMA，istio service mesh圆桌讨论
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[DLAI] AI for Good_ A discussion on addressing real-world problems with AI
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GoogleCloudNext2023: Building your ML Ops strategy for generative AI
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Fast, Flexible, and Scalable Data Loading for ML Training with Ray Data