First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper focuses on l_1 regularized multi-task feature RL by means of an integration between multi-task feature learning (MTFL) and Fitted Q-learning. Clarity: The paper is mostly well written. Regarding the format of this paper, the font size is not right. A suggestion on nuclear norm: The nuclear norm is usually represented as ||\cdot||_*, where in the paper it is notated as ||\cdot||_1. There is a mistake in Assumption 5. Judging from the context, I think line 291 is right and line 299 is mistakenly written, and thus the formulation in Equation (5, 6) are wrong, where U should be U^{-1}.

Weaknesses: Method: - Theorem 1 is not new, and similar results were already presented in previous work [Liang et al., 2019]. For the proof of Theorem 1, In L171, there are no bias terms that appeared in computing z_j. However, for ResNet and VGGNet they all have bias terms at each layer, so this theorem does not apply for them. For example, when the input data is very sparse and most of its dimensions are not correlated with the prediction, then you can probably prune a lot at the input layer. Therefore, I am not convinced with the motivation of data-independent pruning.

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Now in its 37th year, ICML (The International Conference on Machine Learning) is known for bringing cutting-edge research on all aspects of machine learning to the fore. This year, 1088 papers have been accepted from 4990 submissions. Here are a few interesting works to look at ICML 2020, which will be held between 13th and 18th of July. Meta-learning relies on deep networks, which makes batch normalization an essential component of meta-learning pipelines. However, there are several challenges that can render conventional batch normalization ineffective, giving rise to the need to rethink normalization in this setting.

Computer vision summit CVPR has just (virtually) taken place, and like other CV-focused conferences, there are quite a few interesting papers. More than I could possibly write up individually, in fact, so I've collected the most promising ones from major companies here. Facebook, Google, Amazon and Microsoft all shared papers at the conference -- and others too, I'm sure -- but I'm sticking to the big hitters for this column. Redmond has the most interesting papers this year, in my opinion, because they cover several nonobvious real-life needs. One is documenting that shoebox we or perhaps our parents filled with old 3x5s and other film photos.

Definitely does have "echos" of BERT and friends from the NLP side of things, though still has a while to go to reach a similarly large revolution in performance. However, the OP's title does not match the claims of the paper. With unsupervised pretraining on over 1M images 13 labels per class they get 64% top-5 accuracy, well below Alexnet's 82% accuracy. While the paper's investigation is pretty thorough, I don't think they mention either compute requirements (given it's deepmind, I would default to assuming it's gigantic) or how the approach scales with different amounts of unsupervised data. Like how does it perform if only training the CPC feature extractor on half of imagenet?