We examine several aspects of explicability of a classification system built from neural networks. The first aspect is the pairwise explicability, which is the ability to provide the most accurate prediction when the range of possibilities is narrowed to just two. Next we consider explicability in development, which means ability to make incremental improvement in prediction accuracy based on observed deficiency of the system. Inherent stochasticity of neural network based classifiers can be interpreted using likelihood randomness explicability. Finally, sureness explicability indicates confidence of the classifying system to make any prediction at all. These concepts are examined in the framework of pairwise coupling, which is a non-trainable metamodel that originated during development of support vector machines. Several methodologies are evaluated, of which the key one is shown to be the choice of the pairwise coupling method. We compare two methods: the established Wu-Lin-Weng method with the recently proposed Bayes covariant method. Our experiments indicate that the Wu-Lin-Weng method gives more weight to a single pairwise classifier, whereas the latter tries to balance information from the whole matrix of pairwise likelihoods. This translates into higher accuracy, and better sureness predictions for the Bayes covariant method. Pairwise coupling methodology has its costs, especially in terms of the number of parameters (but not necessarily in terms of training costs). However, when additional explicability aspects beyond accuracy are desired in an application, the pairwise coupling models are a promising alternative to the established methodology.

Lipschitz constraints under L2 norm on deep neural networks are useful for provable adversarial robustness bounds, stable training, and Wasserstein distance estimation. While heuristic approaches such as the gradient penalty have seen much practical success, it is challenging to achieve similar practical performance while provably enforcing a Lipschitz constraint. In principle, one can design Lipschitz constrained architectures using the composition property of Lipschitz functions, but Anil et al. recently identified a key obstacle to this approach: gradient norm attenuation. They showed how to circumvent this problem in the case of fully connected networks by designing each layer to be gradient norm preserving. We extend their approach to train scalable, expressive, provably Lipschitz convolutional networks. In particular, we present the Block Convolution Orthogonal Parameterization (BCOP), an expressive parameterization of orthogonal convolution operations. We show that even though the space of orthogonal convolutions is disconnected, the largest connected component of BCOP with 2n channels can represent arbitrary BCOP convolutions over n channels. Our BCOP parameterization allows us to train large convolutional networks with provable Lipschitz bounds. Empirically, we find that it is competitive with existing approaches to provable adversarial robustness and Wasserstein distance estimation.

We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images. By multi-tasking on such a broad large-scale set of data, we hope to both move towards and measure progress in producing a single unified agent that can perceive, reason and converse with humans in an open-domain setting. We show that such multi-tasking improves over a BERT pre-trained baseline, largely due to multi-tasking with very large dialogue datasets in a similar domain, and that the multi-tasking in general provides gains to both text and image-based tasks using several metrics in both the fine-tune and task transfer settings. We obtain state-of-the-art results on many of the tasks, providing a strong baseline for this challenge.

Every natural text is written in some style. The style is formed by a complex combination of different stylistic factors, including formality markers, emotions, metaphors, etc. Some factors implicitly reflect the author's personality, while others are explicitly controlled by the author's choices in order to achieve some personal or social goal. One cannot form a complete understanding of a text and its author without considering these factors. The factors combine and co-vary in complex ways to form styles. Studying the nature of the covarying combinations sheds light on stylistic language in general, sometimes called cross-style language understanding. This paper provides a benchmark corpus (xSLUE) with an online platform (http://xslue.com) for cross-style language understanding and evaluation. The benchmark contains text in 15 different styles and 23 classification tasks. For each task, we provide the fine-tuned classifier for further analysis. Our analysis shows that some styles are highly dependent on each other (e.g., impoliteness and offense), and some domains (e.g., tweets, political debates) are stylistically more diverse than others (e.g., academic manuscripts). We discuss the technical challenges of cross-style understanding and potential directions for future research: cross-style modeling which shares the internal representation for low-resource or low-performance styles and other applications such as cross-style generation.

At first look, it may not be obvious that Dell Technologies is using AI to pioneer new ways to support our customers. From cutting edge Dell EMC Ready Solutions for AI to software platforms like World Wide Herd for federating learning in genome research, Dell Technologies is silently building the foundational underpinnings that businesses around the globe will need to become successful in this new age. Within the walls of Dell Technologies, advanced engineering teams are filing patents and blazing new trails in support of the next digital revolution. The innovation mindset runs deep, and data science teams in our Dell Technologies Services group are leading by example. The focus on "drinking our own champagne" -- as Patricia Florissi, Dell EMC's global CTO for sales, likes to say -- helps demonstrate how Dell's hardware, software and deep learning solutions are both viable and transferable into many verticals, like predicting quality issues in medical devices or detecting fraud.

Asker, Norway-based Tomra Sorting Recycling has launched Gain, a deep learning-based sorting technology to further enhance the performance of its sensor-based sorting machines, according to a Tomra news release. The gain technology will be made available as an add-on option for the company's autosort machines. By classifying objects from sensor data, gain enables the sorting of objects, which could previously not be seperated with high levels of purity and without compromising the throughput speed of the autosort. "By bringing deep learning to our sorting technologies, Tomra is adding further sophistication and effectiveness to its market-leading autosort sorting machines," says Alessandro Granziera, sales manager for Tomra Sorting Recycling in Italy. "The gain technology will also help sorting machines adapt to new waste streams, which will be increasingly important as we move towards a circular economy."

Since our task is detection and not segmentation, correctly predicting only a sufficient amount of voxels around the vertebra centroid is needed to detect normal or fractured vertebrae in an image. We leverage this observation to construct 3D label images for our training database in a semi-automated fashion. First, radiologist S.R. created a text file with annotations for every vertebra present in the field of view as described in section 2. Next, J.N. enriched these labels with 3D centroid coordinates by manually localizing every vertebra centroid in the image using MeVisLab [8]. This step required an average of less than two minutes per image in our dataset. Finally, we extended the method described by Glocker et al. [6] to automatically generate 3D label images from these sparse annotations. The resulting label images contain ellipsoids (flattened along the longitudinal axis for fractured vertebrae) around each vertebra centroid annotated with the ground truth class label provided by the radiologist (combining mild, moderate and severe fractures into one fracture class because of the low number of examples per class, see Figure 1).

I've managed to get the model running generation on my PC. One thing needed to point out is that the checkpoint can NOT be loaded exactly as the GPT-2 model checkpoint from Huggingface pytorch-transformer repository. You'll also need to manually define the config, e.g. The generation works just fine by a nucleus sampling approach, and once in a while an E-O-T will be given to indicate end of one post. Bot: they're having an open gym soon in June... dont think they'll be up there this time though Bot: So what's this gym called?

Such misuses would require the public to become more critical about the text they consume, which could have been generated by artificial intelligence, they said. The researcher wrote: "These findings, combined with earlier results on synthetic imagery, audio, and video, imply that technologies are reducing the cost of generating fake content and waging disinformation campaigns. "The public at large will need to become more skeptical of text they find online, just as the'deep fakes' phenomenon calls for more skepticism about images."

DarwinAI's Generative Synthesis technology – the byproduct of years of scholarship from the University of Waterloo – uses AI itself to observe a neural network as it is being trained and then generates a number of new and highly optimized versions of said network. The result are new and entirely unique networks that are not only significantly smaller than the original, but infer faster without sacrificing functional accuracy. Moreover, the understanding garnered by the engine enables explainable deep learning, whereby the platform can illuminate how a network is reaching its conclusions across an entire dataset or a singular inference. The benefits of DarwinAI's Generative Synthesis platform are threefold: In addition, the Darwin platform is complementary to low-level hardware optimization techniques found in toolkits such as Movidius, making it is essential for deploying deep learning applications to Intel chipsets. DarwinAI's proprietary IP is the product of years of scholarship from the University of Waterloo.