Matthew G. Karlaftis
Building and Construction · 2003–2019
A researcher in the Science corpus: 11 papers; trusts 40 researchers and is trusted by 53 researchers here, as inferred from the citation graph.
Subfields
- Building and Construction — 41 citations into it; #44 of 2,909 by trust here, #220 by citations
- Economics and Econometrics — 39 citations into it; #10,795 of 23,481 by trust here, #14,996 by citations
- Artificial Intelligence — 36 citations into it
- Transportation — 32 citations into it; #51 of 2,062 by trust here, #88 by citations
- Computer Vision and Pattern Recognition — 24 citations into it
- Control and Systems Engineering — 21 citations into it; #555 of 8,257 by trust here, #644 by citations
- Statistics and Probability — 17 citations into it
- Strategy and Management — 16 citations into it
Whom Matthew G. Karlaftis trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald B. Rubin
- Diederik P. Kingma
- Geoffrey E. Hinton
- Billy M. Williams
- Baher Abdulhai
- Hojjat Adeli
- Yoshua Bengio
- Tim Salimans
- Volker Nitsch
- K. Kalirajan
Who trusts Matthew G. Karlaftis overall
- Zhiyong Cui
- Breno Sampaio
- David Alexander Tedjopurnomo
- Eleni I. Vlahogianni
- Ruimin Ke
- Yinhai Wang
- Cathy Wu
- Yongxue Tian
- Yu Wei
- Ibai Lana
Papers from Matthew G. Karlaftis's perspective
Ordered by how strongly the researchers Matthew G. Karlaftis trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Panel Data Analysis (2003) — 266 citations
- A DEA approach for evaluating the efficiency and effectiveness of urban transit systems (2004) — 308 citations
- Latent Variable Models (2019)
- Short-term traffic forecasting: Where we are and where we’re going (2014) — 1,130 citations
- Efficiency Measurement in Public Transport: Are Findings Specification Sensitive? (2012) — 143 citations
- Statistical methods versus neural networks in transportation research: Differences, similarities and some insights (2011) — 841 citations
- The Gravity Model Specification for Modeling International Trade Flows and Free Trade Agreement Effects: A 10-Year Review of Empirical Studies (2010) — 259 citations
- Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach (2005) — 631 citations
- Short‐term traffic forecasting: Overview of objectives and methods (2004) — 582 citations
- Statistical and econometric methods for transportation data analysis (2003) — 110 citations
- A multivariate state space approach for urban traffic flow modeling and prediction (2003) — 538 citations