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The Illustrated GPT-2 (Visualizing Transformer Language Models)


This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn't a particularly novel architecture – it's architecture is very similar to the decoder-only transformer. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we'll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. My goal here is to also supplement my earlier post, The Illustrated Transformer, with more visuals explaining the inner-workings of transformers, and how they've evolved since the original paper. My hope is that this visual language will hopefully make it easier to explain later Transformer-based models as their inner-workings continue to evolve.

Injecting Hierarchy with U-Net Transformers Machine Learning

The Transformer architecture has become increasingly popular over the past couple of years, owing to its impressive performance on a number of natural language processing (NLP) tasks. However, it may be argued that the Transformer architecture lacks an explicit hierarchical representation, as all computations occur on word-level representations alone, and therefore, learning structure poses a challenge for Transformer models. In the present work, we introduce hierarchical processing into the Transformer model, taking inspiration from the U-Net architecture, popular in computer vision for its hierarchical view of natural images. We propose a novel architecture that combines ideas from Transformer and U-Net models to incorporate hierarchy at multiple levels of abstraction. We empirically demonstrate that the proposed architecture outperforms the vanilla Transformer and strong baselines in the chit-chat dialogue and machine translation domains.

Natural Language Processing: the age of Transformers


This article is the first installment of a two-post series on Building a machine reading comprehension system using the latest advances in deep learning for NLP. Stay tuned for the second part, where we'll introduce a pre-trained model called BERT that will take your NLP projects to the next level! In the recent past, if you specialized in natural language processing (NLP), there may have been times when you felt a little jealous of your colleagues working in computer vision. It seemed as if they had all the fun: the annual ImageNet classification challenge, Neural Style Transfer, Generative Adversarial Networks, to name a few. At last, the dry spell is over, and the NLP revolution is well underway!

Copy this Sentence Machine Learning

Attention is an operation that selects some largest element from some set, where the notion of largest is defined elsewhere. Applying this operation to sequence to sequence mapping results in significant improvements to the task at hand. In this paper we provide the mathematical definition of attention and examine its application to sequence to sequence models. We highlight the exact correspondences between machine learning implementations of attention and our mathematical definition. We provide clear evidence of effectiveness of attention mechanisms evaluating models with varying degrees of attention on a very simple task: copying a sentence. We find that models that make greater use of attention perform much better on sequence to sequence mapping tasks, converge faster and are more stable.

NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting Machine Learning

Although Transformer has made breakthrough success in widespread domains especially in Natural Language Processing (NLP), applying it to time series forecasting is still a great challenge. In time series forecasting, the autoregressive decoding of canonical Transformer models could introduce huge accumulative errors inevitably. Besides, utilizing Transformer to deal with spatial-temporal dependencies in the problem still faces tough difficulties.~To tackle these limitations, this work is the first attempt to propose a Non-Autoregressive Transformer architecture for time series forecasting, aiming at overcoming the time delay and accumulative error issues in the canonical Transformer. Moreover, we present a novel spatial-temporal attention mechanism, building a bridge by a learned temporal influence map to fill the gaps between the spatial and temporal attention, so that spatial and temporal dependencies can be processed integrally. Empirically, we evaluate our model on diversified ego-centric future localization datasets and demonstrate state-of-the-art performance on both real-time and accuracy.