What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Artificial intelligence
His primary scientific interests are in Parallel computing, Cache, Locality of reference, CUDA and General-purpose computing on graphics processing units.
His studies in Parallel computing integrate themes in fields like Data flow diagram, Algorithm design and Software.
The various areas that Xipeng Shen examines in his Cache study include Scalability, Thread, Distributed computing and Shared memory.
His Locality of reference research is multidisciplinary, incorporating perspectives in Program optimization and Cache miss.
His Data mining research includes elements of Machine learning and Artificial intelligence.
His Feature vector study, which is part of a larger body of work in Machine learning, is frequently linked to Jaccard index, bridging the gap between disciplines.
His most cited work include:
- Learning multi-label scene classification (1442 citations)
- Locality phase prediction (217 citations)
- Software behavior oriented parallelization (163 citations)
What are the main themes of his work throughout his whole career to date?
Xipeng Shen focuses on Parallel computing, Compiler, Artificial intelligence, Speedup and Distributed computing.
He has researched Parallel computing in several fields, including Computer architecture and General-purpose computing on graphics processing units.
His Compiler research is multidisciplinary, relying on both Software, Set, Profiling and Code.
His research in Artificial intelligence intersects with topics in Machine learning, Block and Natural language processing.
Xipeng Shen focuses mostly in the field of Speedup, narrowing it down to topics relating to Theoretical computer science and, in certain cases, Heuristics.
His research integrates issues of Schedule and Scheduling in his study of Distributed computing.
He most often published in these fields:
- Parallel computing (35.03%)
- Compiler (21.83%)
- Artificial intelligence (18.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (18.27%)
- Set (10.15%)
- Machine learning (9.64%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Set, Machine learning, Computer engineering and Speedup are his primary areas of study.
His Artificial intelligence study integrates concerns from other disciplines, such as Program synthesis and Code.
Xipeng Shen interconnects Software analytics and Data set in the investigation of issues within Machine learning.
His Speedup study combines topics from a wide range of disciplines, such as Optimizing compiler, Program optimization, Compiler and Kernel.
His Optimizing compiler study incorporates themes from Field-programmable gate array, High-level synthesis and Parallel computing.
The Compiler study combines topics in areas such as Theoretical computer science and Programming paradigm.
Between 2018 and 2021, his most popular works were:
- How to "DODGE" Complex Software Analytics? (26 citations)
- Adaptive Deep Reuse: Accelerating CNN Training on the Fly (7 citations)
- In-Place Zero-Space Memory Protection for CNN (5 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Artificial intelligence
His primary areas of investigation include Artificial intelligence, Computation, Speedup, Hyperparameter optimization and Machine learning.
His study brings together the fields of Theoretical computer science and Artificial intelligence.
His Computation research incorporates elements of Backpropagation, On the fly and Relaxation.
His Speedup research includes themes of Inference, Set, Data mining and Cluster analysis.
His Set study which covers Deep learning that intersects with Convolutional neural network.
His studies in Convolutional neural network integrate themes in fields like Compiler, Computer engineering, Pruning, Field and Composability.
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