Learning Distinct Features Helps, Provably

Author:

Laakom FirasORCID,Raitoharju JenniORCID,Iosifidis AlexandrosORCID,Gabbouj MoncefORCID

Publisher

Springer Nature Switzerland

Reference65 articles.

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2. Arora, S., Du, S., Kakade, S., Luo, Y., Saunshi, N.: Provable representation learning for imitation learning via bi-level optimization. In: International Conference on Machine Learning. PMLR (2020)

3. Arpit, D., et al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning, pp. 233–242. PMLR (2017)

4. Bao, Y., Jiang, H., Dai, L., Liu, C.: Incoherent training of deep neural networks to de-correlate bottleneck features for speech recognition. In: International Conference on Acoustics, Speech and Signal Processing, pp. 6980–6984 (2013)

5. Barron, A.R.: Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Inf. Theor. 39(3), 930–945 (1993)

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