Juho Kanniainen

Artificial Intelligence · 2017

A researcher in the Science corpus: 6 papers; trusts 89 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it
• Management Science and Operations Research — 35 citations into it; #4,539 of 11,946 by trust here, #5,316 by citations
• Finance — 34 citations into it; #4,170 of 10,518 by trust here, #6,960 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Economics and Econometrics — 23 citations into it; #15,345 of 23,481 by trust here, #18,597 by citations
• Model Architectures — 15 citations into it
• Accounting — 14 citations into it; #4,591 of 8,970 by trust here, #8,191 by citations
• Electrical and Electronic Engineering — 9 citations into it

Whom Juho Kanniainen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alec N. Kercheval
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Justin A. Sirignano
7. Paul J. Werbos
8. Jimmy Ba
9. Alex Graves
10. Sepp Hochreiter

Who trusts Juho Kanniainen overall

1. Weiwei Jiang
2. Avraam Tsantekidis
3. Nikolaos Passalis
4. A. Ozbayoglu
5. Omer Berat Sezer
6. Dat Thanh Tran
7. M. Ugur Gudelek
8. Zihao Zhang
9. Anastasios Tefas
10. Moncef Gabbouj

Papers from Juho Kanniainen's perspective

Ordered by how strongly the researchers Juho Kanniainen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Using deep learning to detect price change indications in financial markets (2017) — 145 citations
• Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017) — 329 citations
• Tensor representation in high-frequency financial data for price change prediction (2017) — 63 citations
• Time-series classification using neural Bag-of-Features (2017) — 39 citations
• Limit Order Books and Liquidity around Scheduled and Non-Scheduled Announcements: Empirical Evidence from NASDAQ Nordic (2017) — 26 citations
• Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017) — 31 citations