京东 11.11 红包
BioML：Protein Design，Lecture 8
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【搬运】BindCraft: one-shot design of functional protein binders
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BioML：RNA折叠问题
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Princeton机器人Lecture10：动态约束下的规划
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Optimal Control (CMU 16-745) 2024 Lecture 19：卡尔曼滤波
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ICML2024 Tutorial，Machine Learning on Function spaces
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[SIGCOMM2023] ： Offloading
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MIT机器人操作Lecture 2
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ICML 2024 Tutorial，Graph Learning
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编译器的黄金时代
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Noam Brown，通过self-play学习合作和竞争
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[RLC 2024] David Silver，Towards Superhuman Intelligence
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TPU V4 and Trends in Accelerator Hardware
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Stanford EE364A：Convex Optimization lecture11
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Dynamics on Neural Networks
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Stanford EE364A：Convex Optimization lecture 10
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Stanford EE364A: Convex Optimization lecture 4
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[Cuda mode] Lecture 14： Practitioners Guide to Triton
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Ray Summit 2024，The State of vLLM
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Slaying OOMs with PyTorch FSDP and torchao
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[RLC 2024] Sergey Levine，Reinforcement Learning in the Age of Foundation Models
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3DV 2024，下一代reconstruction
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Stanford EE364A: Convex Optimization lecture13
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[Cuda mode] Lecture 7: Advanced Quantization
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Zero Bubble (Almost) Pipeline Parallelism
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[a16z] Democratizing Design with Figmas Dylan Field
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Stanford EE364A：Convex Optimization lecture12
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Geometric Regularizations for 3D Shape Generation
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机器人空间感知基础
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[RLC 2024] Peter Stone，实用强化学习：30 年研究的经验教训
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目标驱动AI: Towards AI systems that can learn, remember, reason, and plan
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Stanford CS149 并行计算，Memory Consistency 12
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AI Hardware
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Optimal Control (CMU 16-745) 2024 Lecture 23： 使用变分法和物理特性作为最优控制
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[RLC 2024] Andy Barto， In the Beginning ML was RL
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Stanford CS25: Robotics and Imitation Learning
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Stanford CS149 并行计算，Cache Coherence 11
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Computer Architecture 2024，Memory Latency II 9
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凸集图及在最优控制和运动规划中应用
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Optimal Control (CMU 16-745) 2024 Lecture 22：自动驾驶和博弈论
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Stanford CS234 Reinforcement Learning，RLHF&DPO