We provide both theoretical analysis and experimental results to validate the effectiveness of our proposed algorithm.

Autoencoder is a powerful unsupervised learning framework to learn latent representations by minimizing reconstruction loss of the input data [1]. Autoencoders have been widely used in unsupervised learning tasks such as representation learning [1] [2], denoising [3], and generative model [4][5]. Most autoencoders are under-complete autoencoders, for which the latent space is smaller than the input data [2]. Over-complete autoencoders have latent space larger than input data.

Define = log (M2H / ).

Then, we consider sufficient assumptions under which learning good policies requires polynomial number of episodes.

It has several appealing benefits that prior arts donothave.

Corollary 3.Consider Algorithm 1 withinitialpointx0, parameters satisfying 1.1 = O( 1)and 00, and -MSoracleOaMSN (with LAZY= Trueinallbutthefirstiteration) with 2 (0.01,0.99).

It has achieved great success on different vision-language downstream tasks suchasImage-TextRetrieval,Image-Captioning,andVisualQuestionAnswering.

We tackle this by introducing two novel value-based algorithms.