Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Human-level control through deep reinforcement learning
201517.2k citationsVolodymyr Mnih, Koray Kavukcuoglu et al.Natureprofile →
Speech recognition with deep recurrent neural networks
This map shows the geographic impact of Alex Graves'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 Alex Graves with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alex Graves more than expected).
This network shows the impact of papers produced by Alex Graves. 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 Alex Graves. The network helps show where Alex Graves may publish in the future.
Co-authorship network of co-authors of Alex Graves
This figure shows the co-authorship network connecting the top 25 collaborators of Alex Graves.
A scholar is included among the top collaborators of Alex Graves 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 Alex Graves. Alex Graves is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Menick, Jacob, Erich Elsen, Utku Evci, et al.. (2021). Practical Real Time Recurrent Learning with a Sparse Approximation. International Conference on Learning Representations.3 indexed citations
Graves, Alex, Jacob Menick, & Aäron van den Oord. (2018). Associative Compression Networks. arXiv (Cornell University).2 indexed citations
4.
Kalchbrenner, Nal, Ivo Danihelka, & Alex Graves. (2016). Grid Long Short-Term Memory. arXiv (Cornell University).106 indexed citations
5.
Vezhnevets, Alexander Sasha, Volodymyr Mnih, Simon Osindero, et al.. (2016). Strategic Attentive Writer for Learning Macro-Actions. Neural Information Processing Systems. 29. 3486–3494.17 indexed citations
6.
Graves, Alex, Greg Wayne, Malcolm Reynolds, et al.. (2016). Hybrid computing using a neural network with dynamic external memory. Nature. 538(7626). 471–476.683 indexed citations breakdown →
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, et al.. (2015). Human-level control through deep reinforcement learning. Nature. 518(7540). 529–533.17153 indexed citations breakdown →
Wöllmer, Martin, Florian Eyben, Alex Graves, Björn W. Schuller, & Gerhard Rigoll. (2009). A Tandem BLSTM-DBN Architecture for Keyword Spotting with Enhanced Context Modeling.12 indexed citations
Liwicki, Marcus, Alex Graves, Horst Bunke, & Jürgen Schmidhuber. (2007). A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. Bern Open Repository and Information System (University of Bern).123 indexed citations
15.
Förster, Alexander, Alex Graves, & Jürgen Schmidhuber. (2007). RNN-based Learning of Compact Maps for Efficient Robot Localization. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 537–542.8 indexed citations
16.
Graves, Alex, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber, & Santiago Fernández. (2007). Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks. Bern Open Repository and Information System (University of Bern). 20. 577–584.123 indexed citations
17.
Graves, Alex, Santiago Fernández, Faustino Gomez, & Jürgen Schmidhuber. (2006). Connectionist temporal classification. 369–376.2921 indexed citations breakdown →
18.
Graves, Alex & Jürgen Schmidhuber. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks. 18(5-6). 602–610.3634 indexed citations breakdown →
19.
Graves, Alex & Jürgen Schmidhuber. (2005). Framewise phoneme classification with bidirectional lstm and other neural network architectures.64 indexed citations
20.
Graves, Alex, et al.. (2004). A Comparison Between Spiking and Differentiable Recurrent Neural Networks on Spoken Digit Recognition. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 164–168.5 indexed citations
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.