Baidu

By S. Yeo. 2019

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Furthermore, these design philosophies are realized with two alternative approaches: modular [20], [43], [47]–[54] or end-to-end designs [55]–[63].”
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks (2019, background): “[29, 30] and then each learner updates the whole model locally.”
A Survey on Multimodal Large Language Models for Autonomous Driving (2023, methodology): “Autonomous driving platform-wise, various open source and commercialized software solutions are introduced, such as Tesla Autopilot [118], NVIDIA DRIVE, Autoware.AI [73,74], Baidu Apollo [8], and PonyAlpha [135].”
A Vademecum on Blockchain Technologies: When, Which, and How (2019, unknown): “Baidu [184] Proprietary technology.”
Gradient Compression Supercharged High-Performance Data Parallel DNN Training (2021, methodology): “…compression, there are other approaches aiming at addressing the communication bottleneck in data parallel training, such as using RDMA [80], adopting Ring-allreduce [7, 8, 29], co-designing gradient synchronization with the physical topology [35, 38], and priority-based scheduling [25, 28, 56].”
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications (2018, background): “Finally, we point out that standard benchmarks, like Deep-Bench [6], typically give more emphasis on batch sizes larger than what is encountered in our use cases.”
BlueConnect: Decomposing all-reduce for deep learning on heterogeneous network hierarchy (2019, methodology): “60 Ring Single-level ring-based all-reduce algorithm as in (Baidu, 2017), designed for deep learning.”