Matthai Philipose
About
In The Last Decade
Matthai Philipose
85 papers receiving 5.0k citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Computer Vision and Pattern Recognition 2.9k
- Computer Networks and Communications 1.7k
- Electrical and Electronic Engineering 1.6k
- Artificial Intelligence 1.1k
- Information Systems 494
Countries citing papers authored by Matthai Philipose
This map shows the geographic impact of Matthai Philipose's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthai Philipose with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthai Philipose more than expected).
Fields of papers citing papers by Matthai Philipose
This network shows the impact of papers produced by Matthai Philipose. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthai Philipose. The network helps show where Matthai Philipose may publish in the future.
Co-authorship network of co-authors of Matthai Philipose
This figure shows the co-authorship network connecting the top 25 collaborators of Matthai Philipose. A scholar is included among the top collaborators of Matthai Philipose based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthai Philipose. Matthai Philipose is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size | 6 |
| 2 | Riptide: Fast End-to-End Binarized Neural Networks | 8 |
| 3 | Heterogeneous Bitwidth Binarization in Convolutional Neural Networks | 10 |
| 4 | Live video analytics at scale with approximation and delay-tolerance | 189 |
| 5 | Compressing LSTMs into CNNs. | 3 |
| 6 | MCDNN: An Execution Framework for Deep Neural Networks on Resource-Constrained Devices | 8 |
| 7 | The case for onloading continuous high-datarate perception to the phone | 8 |
| 8 | Nlify: Third-party programming support for spoken natural language interfaces | 1 |
| 9 | Relational learning for collective classification of entities in images | 6 |
| 10 | Structure learning on large scale common sense statistical models of human state | 8 |
| 11 | 23 | |
| 12 | Learning large scale common sense models of everyday life | 20 |
| 13 | Sensor-based understanding of daily life via large-scale use of common sense | 49 |
| 14 | Towards Activity Databases: Using Sensors and Statistical Models to Summarize People's Lives. | 46 |
| 15 | Unsupervised activity recognition using automatically mined common sense | 148 |
| 16 | Maximum a posteriori path estimation with input trace perturbation: algorithms and application to credible rating of human routines | 7 |
| 17 | Contextual computer support for human activity | 3 |
| 18 | 100 | |
| 19 | An evaluation of staged run-time optimizations in DyC (with retrospective) | 6 |
| 20 | Stochastic Modeling for Drought Analysis | 1 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.