Marco Tagliasacchi
Artificial Intelligence · 2007–2024
A researcher in the Science corpus: 17 papers; trusts 374 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 315 citations into it; #4,677 of 35,345 by trust here, #2,594 by citations
- Signal Processing — 244 citations into it; #849 of 7,572 by trust here, #279 by citations
- Computer Vision and Pattern Recognition — 198 citations into it
- Speech & Audio — 187 citations into it; #216 of 1,076 by trust here, #117 by citations
- Representation & Self-Supervised Learning — 84 citations into it
- Generative Models — 71 citations into it
- Natural Language Processing — 64 citations into it
- Evaluation & Benchmarks — 54 citations into it
Whom Marco Tagliasacchi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Jimmy Ba
- Ilya Sutskever
- Andrew L. Maas
- Yann LeCun
- Alex Graves
Who trusts Marco Tagliasacchi overall
- Zhihao Du
- Zalán Borsos
- Neil Zeghidour
- Pavel Andreev
- Aaqib Saeed
- Rui Xue
- Yuma Koizumi
- Yunpeng Li
- Yanqing Liu
- Shiliang Zhang
Papers from Marco Tagliasacchi's perspective
Ordered by how strongly the researchers Marco Tagliasacchi 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- AudioLM: A Language Modeling Approach to Audio Generation (2022) — 990 citations
- MusicLM: Generating Music From Text (2023) — 722 citations
- SoundStream: An End-to-End Neural Audio Codec (2021) — 1,407 citations
- Towards Learning a Universal Non-Semantic Representation of Speech (2020) — 174 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- AudioPaLM: A Large Language Model That Can Speak and Listen (2023) — 486 citations
- Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision (2023) — 291 citations
- SoundStorm: Efficient Parallel Audio Generation (2023) — 162 citations
- LMCodec: A Low Bitrate Speech Codec with Causal Transformer Models (2023) — 31 citations
- TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition (2023) — 18 citations
- SpeechPainter: Text-conditioned Speech Inpainting (2022) — 36 citations
- Disentangling speech from surroundings in a neural audio codec (2022) — 7 citations
- Self-Supervised Learning from Automatically Separated Sound Scenes (2021) — 13 citations
- Real-Time Speech Frequency Bandwidth Extension (2020) — 71 citations
- SEANet: A Multi-modal Speech Enhancement Network (2020) — 117 citations
- Self-supervised audio representation learning for mobile devices (2019) — 48 citations
- Scream and gunshot detection and localization for audio-surveillance systems (2007) — 390 citations