Alex Graves

85.4k citations
40 papers · 36.5k indexed · 10 hit papers · h-index 26

Impact in

    • Reinforcement Learning in Robotics
    • Speech Recognition and Synthesis
    • Natural Language Processing Techniques
    • Topic Modeling
    • Anomaly Detection Techniques and Applications
    • Music and Audio Processing
    • Speech and Audio Processing

Papers in

    • Music and Audio Processing 13
    • Speech and Audio Processing 6
    • Speech Recognition and Synthesis 17
    • Natural Language Processing Techniques 10
    • Topic Modeling 8
    • Neural Networks and Applications 5
    • Reinforcement Learning in Robotics 3

Alex Graves

39 papers receiving 34.6k citations

Hit Papers

Hybrid computing using a neural network with dynamic external memory 2016 · 683 citations
68320052026201220195.0k10.0k15.0k

Peers

Alex Graves
Comparison fields: 5 of 220
  • Artificial Intelligence 18.2k
  • Signal Processing 5.1k
  • Computer Vision and Pattern Recognition 7.8k
  • Control and Systems Engineering 4.2k
  • Automotive Engineering 2.2k
Replace Qiang Yang with:
Qiang Yang Hong Kong
Sepp Hochreiter Austria
Ruslan Salakhutdinov United States
Alex Smola United States
Koray Kavukcuoglu United States
Andrew Y. Ng United States
Léon Bottou United States
Alex Krizhevsky Canada
Chih‐Jen Lin Taiwan
Guang-Bin Huang Singapore
Alex Graves relative to Qiang Yang Hong Kong Qiang Yang's profile →
Citations per field
00.5×1.5×2.4×
Qiang Yang · 1×
Citations per year

Countries citing papers authored by Alex Graves

Since Specialization
Citations

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).

Fields of papers citing papers by Alex Graves

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

The 25 scholars most cited alongside Alex Graves, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Alex Graves Line = papers co-authored together Alex Graves links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2
Practical Real Time Recurrent Learning with a Sparse Approximation
20213
3
Associative Compression Networks
20182
4
Strategic Attentive Writer for Learning Macro-Actions
201617
5
Grid Long Short-Term Memory
2016106
6
Hybrid computing using a neural network with dynamic external memory
Hit paper breakdown →
2016683
7
DRAW: A Recurrent Neural Network For Image Generation
Hit paper breakdown →
2015478
8
Human-level control through deep reinforcement learning
Hit paper breakdown →
201517153
9
Towards End-To-End Speech Recognition with Recurrent Neural Networks
Hit paper breakdown →
20141084
10 201412
11
Hybrid speech recognition with Deep Bidirectional LSTM
Hit paper breakdown →
20131138
12
Practical Variational Inference for Neural Networks
Hit paper breakdown →
2011475
13
A Tandem BLSTM-DBN Architecture for Keyword Spotting with Enhanced Context Modeling
200912
14 2009136
15
A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks
2007123
16
RNN-based Learning of Compact Maps for Efficient Robot Localization
20078
17
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks
2007123
18
Framewise phoneme classification with bidirectional lstm and other neural network architectures
200564
19
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Hit paper breakdown →
20053634
20
A Comparison Between Spiking and Differentiable Recurrent Neural Networks on Spoken Digit Recognition
20045

About Alex Graves

Alex Graves is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Rehabilitation and Geriatrics and Gerontology, having authored 40 papers that have together received 36.5k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (17 papers), Music and Audio Processing (13 papers), Natural Language Processing Techniques (10 papers), Topic Modeling (8 papers), Speech and Audio Processing (6 papers), Neural Networks and Applications (5 papers), Advanced Memory and Neural Computing (3 papers) and Reinforcement Learning in Robotics (3 papers). The work is most often cited by research in Artificial Intelligence (18.2k citations), Signal Processing (5.1k citations), Computer Vision and Pattern Recognition (7.8k citations), Control and Systems Engineering (4.2k citations) and Automotive Engineering (2.2k citations). Alex Graves has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Jürgen Schmidhuber, Abdelrahman Mohamed, Geoffrey E. Hinton, Daan Wierstra, Koray Kavukcuoglu, Demis Hassabis, Georg Ostrovski, Navdeep Jaitly, Volodymyr Mnih and Charles Beattie. Their work appears in journals such as Nature, Neural Networks, International Journal on Document Analysis and Recognition (IJDAR), Nature Communications and Cognitive Computation.

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.

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