His primary scientific interests are in Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Image. His Artificial intelligence study deals with Natural language processing intersecting with Translation. Machine learning is closely attributed to Embedding in his work.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Pixel and Computer vision. Marc'Aurelio Ranzato combines subjects such as Language model and Feature learning with his study of Image. His MNIST database research is multidisciplinary, relying on both Supervised learning, Feature extraction, Filter and Word error rate.
Marc'Aurelio Ranzato mainly focuses on Artificial intelligence, Machine learning, Pattern recognition, Natural language processing and Language model. His research ties Computer vision and Artificial intelligence together. His work on Stochastic gradient descent and Feature as part of general Machine learning research is often related to Sequence, thus linking different fields of science.
Many of his research projects under Pattern recognition are closely connected to Invariant with Invariant, tying the diverse disciplines of science together. His work on BLEU as part of general Natural language processing research is frequently linked to Quality, thereby connecting diverse disciplines of science. His studies in Language model integrate themes in fields like Recurrent neural network, Word and Natural language.
His primary areas of investigation include Artificial intelligence, Language model, Perplexity, Machine learning and Natural language processing. His work on Benchmark is typically connected to Sequence as part of general Artificial intelligence study, connecting several disciplines of science. His Perplexity research incorporates elements of Theoretical computer science, Natural language and Transformer.
Marc'Aurelio Ranzato interconnects Task, Automatic summarization and Machine translation in the investigation of issues within Machine learning. Marc'Aurelio Ranzato has researched Natural language processing in several fields, including Training set and Generative grammar. His research in Training set intersects with topics in Energy based and Residual energy.
Marc'Aurelio Ranzato mostly deals with Artificial intelligence, Language model, Machine translation, Automatic summarization and Machine learning. His research in the fields of Perplexity overlaps with other disciplines such as Fluency. As part of his studies on Perplexity, he often connects relevant subjects like Leverage.
Fluency is intertwined with Evaluation of machine translation, BLEU, Natural, Matching and Natural language processing in his research. Borrowing concepts from Quality, Marc'Aurelio Ranzato weaves in ideas under Evaluation of machine translation. His Dropout study incorporates themes from Margin, Noise, Principle of compositionality and Semi-supervised learning.
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DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Yaniv Taigman;Ming Yang;Marc'Aurelio Ranzato;Lior Wolf.
computer vision and pattern recognition (2014)
Large Scale Distributed Deep Networks
Jeffrey Dean;Greg Corrado;Rajat Monga;Kai Chen.
neural information processing systems (2012)
Building high-level features using large scale unsupervised learning
Marc'aurelio Ranzato;Rajat Monga;Matthieu Devin;Kai Chen.
international conference on machine learning (2012)
What is the best multi-stage architecture for object recognition?
Kevin Jarrett;Koray Kavukcuoglu;Marc'Aurelio Ranzato;Yann LeCun.
international conference on computer vision (2009)
DeViSE: A Deep Visual-Semantic Embedding Model
Andrea Frome;Greg S Corrado;Jon Shlens;Samy Bengio.
neural information processing systems (2013)
Efficient Learning of Sparse Representations with an Energy-Based Model
Marc'aurelio Ranzato;Christopher Poultney;Sumit Chopra;Yann L. Cun.
neural information processing systems (2006)
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
M.A. Ranzato;Fu Jie Huang;Y.-L. Boureau;Yann LeCun.
computer vision and pattern recognition (2007)
Word translation without parallel data
Guillaume Lample;Alexis Conneau;Marc'Aurelio Ranzato;Ludovic Denoyer.
international conference on learning representations (2018)
Sparse Feature Learning for Deep Belief Networks
Marc'aurelio Ranzato;Y-lan Boureau;Yann L. Cun.
neural information processing systems (2007)
Gradient Episodic Memory for Continual Learning
David Lopez-Paz;Marc'Aurelio Ranzato.
neural information processing systems (2017)
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