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1Open-endedness: The last grand challenge you’ve never heard of
While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself.
2 years ago by @jascal_panetzky
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1scDiffusion: conditional generation of high-quality single-cell data using diffusion model
E. Luo, M. Hao, L. Wei, and X. Zhang. (2024)
4 months ago by @martinr
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3Diffusion-lm improves controllable text generation
X. Li, J. Thickstun, I. Gulrajani, P. Liang, and T. Hashimoto. Advances in Neural Information Processing Systems, (2022)
9 months ago by @martinr
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2Large Language Models as Optimizers
C. Yang, X. Wang, Y. Lu, H. Liu, Q. Le, D. Zhou, and X. Chen. (2023)
10 months ago by @martinr
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3Automatic Prompt Optimization with ``Gradient Descent'' and Beam Search
R. Pryzant, D. Iter, J. Li, Y. Lee, C. Zhu, and M. Zeng. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, page 7957--7968. Singapore, Association for Computational Linguistics, (December 2023)
10 months ago by @martinr
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2Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel and 2 other author(s). Advances in Neural Information Processing Systems, 33, page 9459--9474. Curran Associates, Inc., (2020)
12 months ago by @martinr
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2Neural Networks and the Chomsky Hierarchy
G. Deletang, A. Ruoss, J. Grau-Moya, T. Genewein, L. Wenliang, E. Catt, C. Cundy, M. Hutter, S. Legg, J. Veness and 1 other author(s). The Eleventh International Conference on Learning Representations, (2023)
a year ago by @martinr
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2Neural Networks and the Chomsky Hierarchy
G. Deletang, A. Ruoss, J. Grau-Moya, T. Genewein, L. Wenliang, E. Catt, C. Cundy, M. Hutter, S. Legg, J. Veness and 1 other author(s). The Eleventh International Conference on Learning Representations, (2023)
a year ago by @sew03dg
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2End-to-end Symbolic Regression with Transformers
P. Kamienny, S. dAscoli, G. Lample, and F. Charton. Advances in Neural Information Processing Systems, 35, page 10269--10281. Curran Associates, Inc., (2022)
a year ago by @martinr
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2End-to-end Symbolic Regression with Transformers
P. Kamienny, S. dAscoli, G. Lample, and F. Charton. Advances in Neural Information Processing Systems, 35, page 10269--10281. Curran Associates, Inc., (2022)
a year ago by @sew03dg
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3PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
Z. Long, Y. Lu, and B. Dong. (2018)cite arxiv:1812.04426Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:1710.09668.
a year ago by @martinr
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3Catch-a-waveform: Learning to generate audio from a single short example
G. Greshler, T. Shaham, and T. Michaeli. Advances in Neural Information Processing Systems, (2021)
a year ago by @jascal_panetzky
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2Gene2vec: distributed representation of genes based on co-expression
J. Du, P. Jia, Y. Dai, C. Tao, Z. Zhao, and D. Zhi. BMC Genomics, 20 (1): 82-- (2019)
a year ago by @martinr
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5AudioLM: a Language Modeling Approach to Audio Generation
Z. Borsos, R. Marinier, D. Vincent, E. Kharitonov, O. Pietquin, M. Sharifi, O. Teboul, D. Grangier, M. Tagliasacchi, and N. Zeghidour. (2022)cite arxiv:2209.03143.
a year ago by @martinr
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2scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data
F. Yang, W. Wang, F. Wang, Y. Fang, D. Tang, J. Huang, H. Lu, and J. Yao. Nature Machine Intelligence, (October 2022)
a year ago by @martinr
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3Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
J. Lee, Y. Lee, J. Kim, A. Kosiorek, S. Choi, and Y. Teh. ICML, volume 97 of Proceedings of Machine Learning Research, page 3744-3753. PMLR, (2019)
a year ago by @martinr
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2What is my math transformer doing? -- Three results on interpretability and generalization
F. Charton. (2022)
a year ago by @martinr
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2What is my math transformer doing? -- Three results on interpretability and generalization
F. Charton. (2022)
2 years ago by @sew03dg
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2Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
A. Baevski, A. Babu, W. Hsu, and M. Auli. https://ai.facebook.com/blog/ai-self-supervised-learning-data2vec/, (2022)cite arxiv:2212.07525.
2 years ago by @martinr
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1Soundstream: An end-to-end neural audio codec
N. Zeghidour, A. Luebs, A. Omran, J. Skoglund, and M. Tagliasacchi. IEEE/ACM Transactions on Audio, Speech, and Language Processing, (2021)
2 years ago by @jascal_panetzky
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2A review of vector quantization techniques
A. Vasuki, and P. Vanathi. IEEE Potentials, 25 (4): 39--47 (2006)
2 years ago by @martinr
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1Open-endedness: The last grand challenge you’ve never heard of

publications (hide)41
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1scDiffusion: conditional generation of high-quality single-cell data using diffusion model

3Diffusion-lm improves controllable text generation

2Large Language Models as Optimizers

3Automatic Prompt Optimization with ``Gradient Descent'' and Beam Search

2Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

2Neural Networks and the Chomsky Hierarchy

2Neural Networks and the Chomsky Hierarchy

2End-to-end Symbolic Regression with Transformers

2End-to-end Symbolic Regression with Transformers

3PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

3Catch-a-waveform: Learning to generate audio from a single short example

2Gene2vec: distributed representation of genes based on co-expression

5AudioLM: a Language Modeling Approach to Audio Generation

2scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data

3Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

2What is my math transformer doing? -- Three results on interpretability and generalization

2What is my math transformer doing? -- Three results on interpretability and generalization

2Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language

1Soundstream: An end-to-end neural audio codec

2A review of vector quantization techniques

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