In multi-stage processes, decisions occur in an ordered sequence of stages. Early stages usually have more observations with general information (easier/cheaper to collect), while later stages have fewer observations but more specific data. This situation can be represented by a dual funnel structure, in which the sample size decreases from one stage to the other while the information increases. Training classifiers in this scenario is challenging since information in the early stages may not contain distinct patterns to learn (underfitting). In contrast, the small sample size in later stages can cause overfitting. We address both cases by introducing a framework that combines adversarial autoencoders (AAE), multi-task learning (MTL), and multi-label semi-supervised learning (MLSSL). We improve the decoder of the AAE with an MTL component so it can jointly reconstruct the original input and use feature nets to predict the features for the next stages. We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions. Using real-world data from different domains (selection process, medical diagnosis), we show that our approach outperforms other state-of-the-art methods.

Modelling uncertainty arising from input-dependent label noise is an increasingly important problem. A state-of-the-art approach for classification [Kendall and Gal, 2017] places a normal distribution over the softmax logits, where the mean and variance of this distribution are learned functions of the inputs. This approach achieves impressive empirical performance but lacks theoretical justification. We show that this model is a special case of a well known and theoretically understood model studied in econometrics. Under this view the softmax over the logit distribution is a smooth approximation to an argmax, where the approximation is exact in the zero temperature limit. We further illustrate that the softmax temperature controls a bias-variance trade-off and the optimal point on this trade-off is not always found at 1.0. By tuning the softmax temperature, we achieve improved performance on well known image classification benchmarks with controlled label noise. For image segmentation, where input-dependent label noise naturally arises, we show that tuning the temperature increases the mean IoU on the PASCAL VOC and Cityscapes datasets by more than 1% over the state-of-the-art model and a strong baseline that does not model this noise source.

Machine-learning (ML) models are increasingly used today in making decisions that affect real people's lives, and, because of that, there is a huge need to ensure that the models and their decisions are interpretable by their human users. Motivated by this need, there has bee a lot of interest recently in the ML community in studying Interpretable models [18]. There is currently no consensus on what interpretability means, and no benchmarks for evaluating interpretability [5, 10]. The only consensus is that simpler models such as linear regression or decision trees are considered more interpretable than complex models like, say, deep neural nets. However, two general principles for approaching interpretability have emerged in the literature that are relevant to our paper.

Aspect-based opinion mining is the task of identifying sentiment at the aspect level in opinionated text, which consists of two subtasks: aspect category extraction and sentiment polarity classification. While aspect category extraction aims to detect and categorize opinion targets such as product features, sentiment polarity classification assigns a sentiment label, i.e. positive, negative, or neutral, to each identified aspect. Supervised learning methods have been shown to deliver better accuracy for this task but they require labeled data, which is costly to obtain, especially for resource-poor languages like Vietnamese. To address this problem, we present a supervised aspect-based opinion mining method that utilizes labeled data from a foreign language (English in this case), which is translated to Vietnamese by an automated translation tool (Google Translate). Because aspects and opinions in different languages may be expressed by different words, we propose using word embeddings, in addition to other features, to reduce the vocabulary difference between the original and translated texts, thus improving the effectiveness of aspect category extraction and sentiment polarity classification processes. We also introduce an annotated corpus of aspect categories and sentiment polarities extracted from restaurant reviews in Vietnamese, and conduct a series of experiments on the corpus. Experimental results demonstrate the effectiveness of the proposed approach.

In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning. Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning. Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays. But still, there is a big misconception among many people about the meaning of these terms. In the worst case, one may think that these terms describe the same thing -- which is simply false.

TensorFlow is an open source framework developed by Google researchers to run machine learning, deep learning and other statistical and predictive analytics workloads. Like similar platforms, it's designed to streamline the process of developing and executing advanced analytics applications for users such as data scientists, statisticians and predictive modelers. TensorFlow is an open source framework developed by Google researchers to run machine learning, deep learning and other statistical and predictive analytics workloads. Like similar platforms, it's designed to streamline the process of developing and executing advanced analytics applications for users such as data scientists, statisticians and predictive modelers. The TensorFlow software handles data sets that are arrayed as computational nodes in graph form. The edges that connect the nodes in a graph can represent multidimensional vectors or matrices, creating what are known as tensors.

Battlesnake is an AI competition based on the traditional snake game in which multiple AI-powered snakes compete to be the last snake surviving. Battlesnake attracts a community of developers at all levels. Hundreds of snakes compete and rise up in the ranks in the online Battlesnake global arena. Battlesnake also hosts several offline events that are attended by more than a thousand developers and non-developers alike and are streamed on Twitch. Teams of developers build snakes for the competition and learn new tech skills, learn to collaborate, and have fun. Teams can build snakes by using a variety of strategies ranging from state-of-the-art deep reinforcement learning (RL) algorithms to unique heuristics-based strategies. This post shows how to use Amazon SageMaker to build an RL-based snake.

This algorithm consists of 2 parts: a LSTM block and a FCN part with 3 convolution layers. Long short-term memory recurrent neural networks are an improvement over the general recurrent neural networks,which possess a vanishing gradient problem. LSTM RNNs address the vanishing gradient problem commonly found in ordinary recurrent neural networks by incorporating gating functions into their state dynamics. For more information about LSTM networks, you can read this great article by Christopher Olah. In addition to the LSTM block, this part also includes a dimension shuffle.

This tutorial is a free preview from The course titled Practical Deep Learning with Keras and Python, and it is available in The Complete Data Science Course Bundle on my website. In this course you will learn how to apply machine learning techniques to real world problems, including how to build a complete pipeline using Keras and Python. If you enjoy this video and would like to continue learning with us, feel free to check out this 6 course bundle which will take you from beginner to advanced in data science, machine learning and neural networks. I will include timestamps and links to additional resources in the description of this video. Without further ado, let's get started!

One hundred years ago, the state of the art in finding antibiotics was epitomized by the playful explorations of Alexander Fleming, the Scotsman who discovered penicillin. "I play with microbes," Fleming is quoted as having said. "It is very pleasant to break the rules and to be able to find something nobody had thought of." Today's research in antibiotics is conducted somewhat more mechanically, perhaps, but it's still important to break the rules sometimes, to look where one might not otherwise. Scientists at the Massachusetts Institute of Technology and Harvard last month described in the scholarly journal Cell how they used a deep learning neural network to identify a molecular compound that's different from most antibiotics.