Perform classifications using a network already created and trained. Perform classifications using a network already created and trained. Import folders of images and make them usable with a given network. Import folders of images and make them usable with a given network. Modify a pretrained network to classify images into specified classes.

A top priority in any business is a constant need to increase revenue and profitability. Within the retail industry, the main source of revenue is based on the purchases of customers. For this reason, companies need to focus on customer retention. When a customer leaves or churns from a business, the opportunity for potential sales or cross selling is lost. When a customer leaves the business without any form of explanation or notice, the company may find it hard to respond and take corrective action.

The future took another step forward. But it was stopped right in its tracks by its inventor, OpenAI. According to its website, OpenAI is a non-profit AI research company, discovering and enacting the path to safe artificial general intelligence. Elon Musk is one of the backers. In a recent blog post, OpenAI's technology, called GPT2, was shown to craft written passages that mimic the style and content of a given sample.

This is the Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning

Humans have schematic knowledge of how certain types of events unfold (e.g. coffeeshop visits) that can readily be generalized to new instances of those events. Schematic knowledge allows humans to perform role-filler binding, the task of associating schematic roles (e.g. "barista") with specific fillers (e.g. "Bob"). Here we examined whether and how recurrent neural networks learn to do this. We procedurally generated stories from an underlying generative graph, and trained networks on role-filler binding question-answering tasks. We tested whether networks can learn to maintain filler information on their own, and whether they can generalize to fillers that they have not seen before. We studied networks by analyzing their behavior and decoding their memory states. We found that a network's success in learning role-filler binding depends on both the breadth of roles introduced during training, and the network's memory architecture. In our decoding analyses, we observed a close relationship between the information we could decode from various parts of network architecture, and the information the network could recall.

In chronic pain physical rehabilitation, physiotherapists adapt movement to current performance of patients especially based on the expression of protective behavior, gradually exposing them to feared but harmless and essential everyday movements. As physical rehabilitation moves outside the clinic, physical rehabilitation technology needs to automatically detect such behaviors so as to provide similar personalized support. In this paper, we investigate the use of a Long Short-Term Memory (LSTM) network, which we call Protect-LSTM, to detect events of protective behavior, based on motion capture and electromyography data of healthy people and people with chronic low back pain engaged in five everyday movements. Differently from previous work on the same dataset, we aim to continuously detect protective behavior within a movement rather than overall estimate the presence of such behavior. The Protect-LSTM reaches best average F1 score of 0.815 with leave-one-subject-out (LOSO) validation, using low level features, better than other algorithms. Performances increase for some movements when modelled separately (mean F1 scores: bending=0.77, standing on one leg=0.81, sit-to-stand=0.72, stand-to-sit=0.83, reaching forward=0.67). These results reach excellent level of agreement with the average ratings of physiotherapists. As such, the results show clear potential for in-home technology supported affect-based personalized physical rehabilitation.

To improve how neural networks function it is crucial to understand their learning process. The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task. However, empirical evidence for this theory is conflicting, as compression was only observed when networks used saturating activation functions. In contrast, networks with non-saturating activation functions achieved comparable levels of task performance but did not show compression. In this paper we developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions. Using these adaptive estimation techniques, we explored compression in networks with a range of different activation functions. With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions. We also find that there is a large amount of variation in compression between different network initializations. Secondary, we see that L2 regularization leads to significantly increased compression, while preventing overfitting. Finally, we show that only compression of the last layer is positively correlated with generalization.

Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the individual learning rates. Differently from existing methods, we make use of the most recent first moment of gradients to compute the individual learning rates per iteration. The motivation behind it is that the dynamic variation of the first moment of gradients may provide useful information to obtain the learning rates. We refer to the new method as the rapidly adapting moment estimation (RAME). The theoretical convergence of deterministic RAME is studied by using an analysis similar to the one used in [1] for Adam. Experimental results for training a number of DNNs show promising performance of RAME w.r.t. the convergence speed and generalization performance compared to the stochastic heavy-ball (SHB) method, Adam, and RMSprop.

The other answer here just posted text from an article on Medium. It goes over the idea of Bayesian deep networks, and lists three ways of implementing a Bayesian approach to network parameters. The first is to use Monte Carlo -- which means you have to first sample the network parameters (weights and biases), and then sample the network outputs from the inputs. That will never work at scale; you can't train anything practical that way, too slow. The second approach is to use variational inference to approximately find the right weights; but you still have to sample the weights and average in order to get the mean and variance for the network outputs, which still slows down inference, without mentioning that variational inference is approximate and often very computationally expensive. The third approach is the one that was actually proposed, that is, to use DropOut, which is hardly Bayesian in the traditional sense, whatever theoretical justification may be offered.

From ransomware, to spyware and banking trojans, the types of malware threats are many. Yet the one threat that seems to be posing the greatest challenge to organizations and their cybersecurity solutions is the unknown malware. This is because the means that have most typically been used to counter cyber threats to date remain insufficient when it comes to unknowns: Signature-based: traditional antivirus solutions usually lean on such technique. With this technique, key data from given files is signed, and so for next times files are matched with the same signature, those will be classified as malicious. The key data is usually strings, or other byte sequences of code piece.