Limitations of Deep Learning for Vision, and How We Might Fix Them History of Deep Learning We are witnessing the third rise of deep learning. The first two waves — 1950s–1960s and 1980s–1990s — generated considerable excitement but slowly ran out of steam, since these neural networks neither achieved their promised performance gains nor aided our understanding of biological vision systems. The th
In this essay, we are going to address the limitations of one of the core fields of AI. In the process, we will encounter a fun allegory, a set of methods of incorporating prior knowledge and instruction into deep learning, and a radical conclusion.[1] The first part, which you're reading right now, will set up what RL is and why it (or at least a particular version of it we shall name 'pure RL' a
Big changes are underway in the world of Natural Language Processing (NLP). The long reign of word vectors as NLP’s core representation technique has seen an exciting new line of challengers emerge: ELMo[1], ULMFiT[2], and the OpenAI transformer[3]. These works made headlines by demonstrating that pretrained language models can be used to achieve state-of-the-art results on a wide range of NLP tas
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