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Found 7 papers, 0 papers with code
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
We introduce Seed1. 5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks.
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains.
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
AIBench: An Industry Standard Internet Service AI Benchmark Suite
On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.
Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms
The dataset is available online, which contains eight types of modulated signals.
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.
