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Oscar Chang
Oscar Chang
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Cited by
Year
Gemini: A family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
4882023
Principled weight initialization for hypernetworks
O Chang, L Flokas, H Lipson
arXiv preprint arXiv:2312.08399, 2020
702020
Pepcvae: Semi-supervised targeted design of antimicrobial peptide sequences
P Das, K Wadhawan, O Chang, T Sercu, CD Santos, M Riemer, ...
arXiv preprint arXiv:1810.07743, 2018
592018
Gemma: Open Models Based on Gemini Research and Technology
G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ...
arXiv preprint arXiv:2403.08295, 2024
362024
Neural network quine
O Chang, H Lipson
Artificial Life Conference Proceedings, 234-241, 2018
242018
Assessing SATNet's ability to solve the symbol grounding problem
O Chang, L Flokas, H Lipson, M Spranger
Advances in Neural Information Processing Systems 33, 1428-1439, 2020
182020
Conformers are All You Need for Visual Speech Recognition
O Chang, H Liao, D Serdyuk, A Shah, O Siohan
arXiv preprint arXiv:2302.10915, 2023
132023
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
112024
Agent Embeddings: A Latent Representation for Pole-Balancing Networks
O Chang, R Kwiatkowski, S Chen, H Lipson
arXiv preprint arXiv:1811.04516, 2019
112019
Single-Channel Speech Enhancement Using Learnable Loss Mixup
O Chang, DN Tran, K Koishida
Proc. Interspeech 2021, 2696-2700, 2021
92021
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network
O Chang, Y Yao, D Williams-King, H Lipson
arXiv preprint arXiv:1905.09453, 2019
82019
On Robustness to Missing Video for Audiovisual Speech Recognition
O Chang, O Braga, H Liao, D Serdyuk, O Siohan
Transactions on Machine Learning Research, 2022
52022
Revisiting the Entropy Semiring for Neural Speech Recognition
O Chang, D Hwang, O Siohan
International Conference on Learning Representations, 2023
12023
Autogenerative Networks
O Chang
Columbia University, 2021
12021
Seven Myths in Machine Learning Research
O Chang, H Lipson
arXiv preprint arXiv:1902.06789, 2019
12019
Balanced and deterministic weight-sharing helps network performance
O Chang, H Lipson
International Conference on Artificial Neural Networks, 41-50, 2018
12018
Holistic Safety and Responsibility Evaluations of Advanced AI Models
L Weidinger, J Barnhart, J Brennan, C Butterfield, S Young, W Hawkins, ...
arXiv preprint arXiv:2404.14068, 2024
2024
Cascaded encoders for fine-tuning ASR models on overlapped speech
R Rose, O Chang, O Siohan
arXiv preprint arXiv:2306.16398, 2023
2023
Accelerating Meta-Learning by Sharing Gradients
O Chang, H Lipson
ICLR 2020 workshop: Beyond tabula rasa in RL, 2020
2020
Gradient Normalization & Depth Based Decay For Deep Learning
R Kwiatkowski, O Chang
arXiv, arXiv: 1712.03607, 2017
2017
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