Cristina Garbacea
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 6 papers; trusts 75 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 214 citations into it; #22,620 of 35,345 by trust here, #6,517 by citations
- Natural Language Processing — 194 citations into it
- Evaluation & Benchmarks — 113 citations into it; #2,618 of 3,147 by trust here, #417 by citations
- Computer Vision and Pattern Recognition — 86 citations into it; #16,562 of 21,575 by trust here, #11,950 by citations
- Generative Models — 32 citations into it; #1,574 of 1,956 by trust here, #1,315 by citations
- Signal Processing — 14 citations into it; #5,686 of 7,572 by trust here, #2,990 by citations
- Model Architectures — 9 citations into it
- Speech & Audio — 8 citations into it; #845 of 1,076 by trust here, #680 by citations
Whom Cristina Garbacea trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Diederik P. Kingma
- Geoffrey E. Hinton
- Oriol Vinyals
- Sepp Hochreiter
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Richard S. Sutton
- Tomas Mikolov
Who trusts Cristina Garbacea overall
- Jan Skoglund
- Jean-Marc Valin
- Jian Guan
- Elizabeth Clark
- Zhichao Huang
- Jie Gui
- Neil Zeghidour
- Alexandre D'efossez
- Felicia S. C. Lim
- Roy Fejgin
Papers from Cristina Garbacea's perspective
Ordered by how strongly the researchers Cristina Garbacea trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021) — 329 citations
- GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
- Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation (2019) — 34 citations
- Low Bit-rate Speech Coding with VQ-VAE and a WaveNet Decoder (2019) — 140 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations