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.
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.
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
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.
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Theano: A Python framework for fast computation of mathematical expressions
Rami Al-Rfou;Guillaume Alain;Amjad Almahairi.
arXiv: Symbolic Computation (2016)
Brain tumor segmentation with Deep Neural Networks
Mohammad Havaei;Axel Davy;David Warde-Farley;Antoine Biard.
Medical Image Analysis (2017)
Learning Conditional Random Fields for Stereo
D. Scharstein;C. Pal.
computer vision and pattern recognition (2007)
Describing Videos by Exploiting Temporal Structure
Li Yao;Atousa Torabi;Kyunghyun Cho;Nicolas Ballas.
international conference on computer vision (2015)
Activity recognition using the velocity histories of tracked keypoints
Ross Messing;Chris Pal;Henry Kautz.
international conference on computer vision (2009)
Deep Learning: A Primer for Radiologists
Gabriel Chartrand;Phillip M Cheng;Eugene Vorontsov;Michal Drozdzal.
The Importance of Skip Connections in Biomedical Image Segmentation
Michal Drozdzal;Eugene Vorontsov;Gabriel Chartrand;Samuel Kadoury.
LABELS/[email protected] (2016)
Real-time preview for panoramic images
Chris Pal;Matthew Uyttendaele;Eric Rudolph;Patrick Baudisch.
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)
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)
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