What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
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.
His most cited work include:
- DeepFace: Closing the Gap to Human-Level Performance in Face Verification (4026 citations)
- Large Scale Distributed Deep Networks (2339 citations)
- What is the best multi-stage architecture for object recognition? (1556 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (85.47%)
- Machine learning (41.03%)
- Pattern recognition (23.93%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (85.47%)
- Language model (16.24%)
- Perplexity (5.13%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Revisiting Self-Training for Neural Sequence Generation (55 citations)
- On The Evaluation of Machine Translation Systems Trained With Back-Translation (24 citations)
- Residual Energy-Based Models for Text Generation (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
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.
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.
