Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continu(cid:173) ous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learn(cid:173) ing progresses. DOLCE consists of a standard recurrent neural net trained by gradient descent and an adaptive clustering technique that quantizes the state space. DOLCE is based on the assumption that a finite set of discrete internal states is required for the task, and that the actual network state belongs to this set but has been corrupted by noise due to inaccuracy in the weights. DOLCE learns to recover the discrete state with maximum a posteriori probabil(cid:173) ity from the noisy state.

In the missing data' approach to improving the robustness of automatic speech recognition to added noise, an initial process identifies spectral- temporal regions which are dominated by the speech source. The remaining regions are considered to bemissing'. In this paper we develop a connectionist approach to the problem of adapting speech recognition to the missing data case, using Recurrent Neural Networks. In contrast to methods based on Hidden Markov Models, RNNs allow us to make use of long-term time constraints and to make the problems of classification with incomplete data and imputing missing values interact. We report encouraging results on an isolated digit recognition task. 1. Introduction Automatic Speech Recognition systems perform reasonably well in controlled and matched training and recognition conditions.

Chest X-Ray imaging is one of the most common radiological tools for detection of various pathologies related to the chest area and lung function. In a clinical setting, automated assessment of chest radiographs has the potential of assisting physicians in their decision making process and optimize clinical workflows, for example by prioritizing emergency patients. Most work analyzing the potential of machine learning models to classify chest X-ray images focuses on vision methods processing and predicting pathologies for one image at a time. However, many patients undergo such a procedure multiple times during course of a treatment or during a single hospital stay. The patient history, that is previous images and especially the corresponding diagnosis contain useful information that can aid a classification system in its prediction. In this study, we analyze how information about diagnosis can improve CNN-based image classification models by constructing a novel dataset from the well studied CheXpert dataset of chest X-rays. We show that a model trained on additional patient history information outperforms a model trained without the information by a significant margin. We provide code to replicate the dataset creation and model training.

Even if you're not actively following recent trends in AI and machine learning, you may have come across articles by a researcher who experiments with training neural nets to generate interesting things such as: Add creamed meat and another deep mixture. What's being used is something called a Recurrent Neural Net to generate text in a specific style. It's trained with input data which it analyzes to recognizes patterns in the text, constructing a model of that data. It can then generate new text following the same patterns, sometimes with rather curious and amusing results. A commonly referred-to article on this topic is by Andrej Karpathy titled The Unreasonable Effectiveness of Recurrent Neural Networks.

Summary: Recurrent Neural Nets (RNNs) are at the core of the most common AI applications in use today but we are rapidly recognizing broad time series problem types where they don't fit well. Several alternatives are already in use and one that's just been introduced, ODE net is a radical departure from our way of thinking about the solution. Recurrent Neural Nets (RNNs) and their cousins LSTMs are at the very core of the most common applications of AI, natural language processing (NLP). There are far more real world applications of RNN-NLP than any other form of AI, including image recognition and processing with Convolutional Neural Nets (CNNs). In a sense, the army of data scientists has split off into two groups, each pursuing the separate applications that might be developed from these two techniques. In application there is essentially no overlap since image processing is about processing data that is static (even if only for a second) while RNN-NLP has always interpreted speech and text as time series data.

Summary: Convolutional Neural Nets are getting all the press but it's Recurrent Neural Nets that are the real workhorse of this generation of AI. We've paid a lot of attention lately to Convolutional Neural Nets (CNNs) as the cornerstone of 2nd gen NNs and spent some time on Spiking Neural Nets (SNNs) as the most likely path forward to 3rd gen, but we'd really be remiss if we didn't stop to recognize Recurrent Neural Nets (RNNs). Because RNNs are solid performers in the 2nd gen NN world and perform many tasks much better than CNNs. These include speech-to-text, language translation, and even automated captioning for images. By count, there are probably more applications for RNNs than for CNNs. On one scale RNNs have much more in common with the larger family of NNs than do CNNs which have very unique architecture.

This video was posted on Youtube by Sirajology. He explains the basics of recurrent neural networks. Then you code your own RNN in 80 lines of python (plus white-space) that predicts the sum of two binary numbers after training. To watch this video, click here.

This video was posted on Youtube by Sirajology. He explains the basics of recurrent neural networks. Then you code your own RNN in 80 lines of python (plus white-space) that predicts the sum of two binary numbers after training.

Summary: Convolutional Neural Nets are getting all the press but it's Recurrent Neural Nets that are the real workhorse of this generation of AI. We've paid a lot of attention lately to Convolutional Neural Nets (CNNs) as the cornerstone of 2nd gen NNs and spent some time on Spiking Neural Nets (SNNs) as the most likely path forward to 3rd gen, but we'd really be remiss if we didn't stop to recognize Recurrent Neural Nets (RNNs). Because RNNs are solid performers in the 2nd gen NN world and perform many tasks much better than CNNs. These include speech-to-text, language translation, and even automated captioning for images. By count, there are probably more applications for RNNs than for CNNs. On one scale RNNs have much more in common with the larger family of NNs than do CNNs which have very unique architecture.

Our Head of Research, Alec Radford, recently led a workshop on general sequence learning using recurrent neural networks at Next.ML in San Francisco. Next.ML was created to teach the latest actionable machine learning techniques that you can use right out of the workshop.The upcoming Next.ML workshop will be in Cambridge, MA at the Microsoft NERD Center on April 27. Recurrent Neural Networks hold great promise as general sequence learning algorithms. As such, they are a very promising tool for text analysis. However, outside of very specific use cases such as handwriting recognition and recently, machine translation, they have not seen widespread use.