H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 50 Citations 16,225 163 World Ranking 2887 National Ranking 114

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary scientific interests are in Artificial intelligence, Pattern recognition, Convolutional neural network, Machine learning and Deep learning. His study in Computer vision extends to Artificial intelligence with its themes. His work on Discriminative model as part of general Pattern recognition study is frequently linked to Action recognition, therefore connecting diverse disciplines of science.

His study in Convolutional neural network is interdisciplinary in nature, drawing from both Exploit, Speech recognition, Brain tumor segmentation and Layer. His Machine learning research is multidisciplinary, incorporating perspectives in Lesion segmentation, Multispectral image and Complex network. His Deep learning research incorporates elements of Field, Transcription, Software, Key and TIMIT.

His most cited work include:

  • Brain tumor segmentation with Deep Neural Networks (1373 citations)
  • Theano: A Python framework for fast computation of mathematical expressions (1242 citations)
  • Describing Videos by Exploiting Temporal Structure (615 citations)

What are the main themes of his work throughout his whole career to date?

Chris Pal mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Reinforcement learning. His Artificial intelligence study typically links adjacent topics like Natural language processing. His research in the fields of Semi-supervised learning, Feature learning and Recurrent neural network overlaps with other disciplines such as Context.

His research in Recurrent neural network intersects with topics in State and Motion capture. His Pattern recognition study frequently draws connections between related disciplines such as Image. His work in Computer vision addresses subjects such as Computer graphics, which are connected to disciplines such as Panorama.

He most often published in these fields:

  • Artificial intelligence (78.28%)
  • Machine learning (38.93%)
  • Pattern recognition (20.49%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (78.28%)
  • Machine learning (38.93%)
  • Reinforcement learning (9.02%)

In recent papers he was focusing on the following fields of study:

Chris Pal mainly focuses on Artificial intelligence, Machine learning, Reinforcement learning, Transformer and Generalization. Adversarial system is the focus of his Artificial intelligence research. His Machine learning study combines topics in areas such as Initialization and Word error rate.

His Reinforcement learning research incorporates themes from Variety, Image, Control and Algorithm. His study looks at the intersection of Algorithm and topics like Artificial neural network with Deep learning. His Transformer study also includes fields such as

  • Language model together with Forward chaining, Logical reasoning and Natural language,
  • Automatic summarization together with Sentence and Ranking.

Between 2019 and 2021, his most popular works were:

  • A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms (25 citations)
  • Robust motion in-betweening (10 citations)
  • Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning (9 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Programming language

Chris Pal mainly investigates Artificial intelligence, Generalization, Theoretical computer science, Transformer and Natural language processing. Artificial intelligence is closely attributed to Machine learning in his study. His Theoretical computer science research is multidisciplinary, incorporating elements of Property and Modularity.

His biological study spans a wide range of topics, including Language model, Overfitting, Learning interference and Multi-task learning. As part of one scientific family, Chris Pal deals mainly with the area of Language model, narrowing it down to issues related to the Natural language, and often Inference. His Natural language processing research includes themes of Representation, Key and Transfer of learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Theano: A Python framework for fast computation of mathematical expressions

Rami Al-Rfou;Guillaume Alain;Amjad Almahairi.
arXiv: Symbolic Computation (2016)

1962 Citations

Brain tumor segmentation with Deep Neural Networks

Mohammad Havaei;Axel Davy;David Warde-Farley;Antoine Biard.
Medical Image Analysis (2017)

1945 Citations

Learning Conditional Random Fields for Stereo

D. Scharstein;C. Pal.
computer vision and pattern recognition (2007)

905 Citations

Describing Videos by Exploiting Temporal Structure

Li Yao;Atousa Torabi;Kyunghyun Cho;Nicolas Ballas.
international conference on computer vision (2015)

710 Citations

Activity recognition using the velocity histories of tracked keypoints

Ross Messing;Chris Pal;Henry Kautz.
international conference on computer vision (2009)

648 Citations

Deep Learning: A Primer for Radiologists

Gabriel Chartrand;Phillip M Cheng;Eugene Vorontsov;Michal Drozdzal.
Radiographics (2017)

405 Citations

The Importance of Skip Connections in Biomedical Image Segmentation

Michal Drozdzal;Eugene Vorontsov;Gabriel Chartrand;Samuel Kadoury.
LABELS/[email protected] (2016)

378 Citations

Real-time preview for panoramic images

Chris Pal;Matthew Uyttendaele;Eric Rudolph;Patrick Baudisch.
(2005)

356 Citations

EmoNets: Multimodal deep learning approaches for emotion recognition in video

Samira Ebrahimi Kahou;Xavier Bouthillier;Pascal Lamblin;Çaglar Gülçehre.
Journal on Multimodal User Interfaces (2016)

332 Citations

Combining modality specific deep neural networks for emotion recognition in video

Samira Ebrahimi Kahou;Christopher Pal;Xavier Bouthillier;Pierre Froumenty.
international conference on multimodal interfaces (2013)

305 Citations

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