Analogies play a crucial role in commonsense reasoning. The ability to recognize analogies like "eye is to seeing what ear is to hearing," sometimes referred to as analogical proportions, shape how humans structure knowledge and understand language. In a new study that looks at whether AI models can understand analogies, researchers at Cardiff University used benchmarks from education as well as more common datasets. They found that while off-the-shelf models can identify some analogies, they sometimes struggle with complex relationships, raising questions about to what extent models capture knowledge. Large language models learn to write humanlike text by internalizing billions of examples from the public web.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To develop a framework to generate synthetic noncontrast CT acute stroke lesions with exact labels and adjustable levels of attenuation to test the efficacy of lesion segmentation deep learning algorithms. The signal intensity on the NCCT images was depressed by 4 HU (a 13% drop) in the region of the diffusion-weighted lesion.

However, not much is known In this work, we take the first step for studying a rather about the mathematical and statistical properties of fundamental problem in the study of adaptive gradients; we this family of methods. This work aims at providing propose to study the distributional properties of the update a series of theoretical analyses of its statistical in the adaptive gradient method. The most closely related properties justified by experiments. In particular, previous work is [Liu et al., 2019]. The difference is that this we show that when the underlying gradient obeys work goes much deeper into the detail in the theoretical analysis a normal distribution, the variance of the magnitude and contradicts the results in [Liu et al., 2019]. The main of the update is an increasing and bounded contributions of this work are the following: (1) We prove function of time and does not diverge. This work that the variance of the adaptive gradient method is always finite suggests that the divergence of variance is not the (Proposition 1), which contradicts the result in Liu et al. cause of the need for warm up of the Adam optimizer, [2019]; this proof does not make any assumption regarding contrary to what is believed in the current the distribution of the gradient.

Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. However, the labeled data often does not align with the target distribution and introduces harmful bias to the trained model. In this paper, we aim to understand the effect of a biased anomaly set on anomaly detection. Concretely, we view anomaly detection as a supervised learning task where the objective is to optimize the recall at a given false positive rate. We formally study the relative scoring bias of an anomaly detector, defined as the difference in performance with respect to a baseline anomaly detector. We establish the first finite sample rates for estimating the relative scoring bias for deep anomaly detection, and empirically validate our theoretical results on both synthetic and real-world datasets. We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes. Our study demonstrates scenarios in which the biased anomaly set can be useful or problematic, and provides a solid benchmark for future research.

Experiencing images with suitable music can greatly enrich the overall user experience. The proposed image analysis method treats an artwork image differently from a photograph image. Automatic image classification is performed using deep-learning based models. An illustrative analysis showcasing the ability of our deep-models to inherently learn and utilize perceptually relevant features when classifying artworks is also presented. The Mean Opinion Score (MOS) obtained from subjective assessments of the respective image and recommended music pairs supports the effectiveness of our approach.

Nowadays, shallow and deep Neural Networks (NNs) have vast applications including biomedical engineering, image processing, computer vision, and speech recognition. Many researchers have developed hardware accelerators including field-programmable gate arrays (FPGAs) for implementing high-performance and energy efficient NNs. Apparently, the hardware architecture design process is specific and time-consuming for each NN. Therefore, a systematic way to design, implement and optimize NNs is highly demanded. The paper presents a systematic approach to implement state-space models in register transfer level (RTL), with special interest for NN implementation. The proposed design flow is based on the iterative nature of state-space models and the analogy between state-space formulations and finite-state machines. The method can be used in linear/nonlinear and time-varying/time-invariant systems. It can also be used to implement either intrinsically iterative systems (widely used in various domains such as signal processing, numerical analysis, computer arithmetic, and control engineering), or systems that could be rewritten in equivalent iterative forms. The implementation of recurrent NNs such as long short-term memory (LSTM) NNs, which have intrinsic state-space forms, are another major applications for this framework. As a case study, it is shown that state-space systems can be used for the systematic implementation and optimization of NNs (as nonlinear and time-varying dynamic systems). An RTL code generating software is also provided online, which simplifies the automatic generation of NNs of arbitrary size.

Neural Cellular Automata (NCA) have shown a remarkable ability to learn the required rules to "grow" images, classify morphologies, segment images, as well as to do general computation such as path-finding. We believe the inductive prior they introduce lends itself to the generation of textures. Textures in the natural world are often generated by variants of locally interacting reaction-diffusion systems. Human-made textures are likewise often generated in a local manner (textile weaving, for instance) or using rules with local dependencies (regular grids or geometric patterns). We demonstrate learning a texture generator from a single template image, with the generation method being embarrassingly parallel, exhibiting quick convergence and high fidelity of output, and requiring only some minimal assumptions around the underlying state manifold. Furthermore, we investigate properties of the learned models that are both useful and interesting, such as non-stationary dynamics and an inherent robustness to damage. Finally, we make qualitative claims that the behaviour exhibited by the NCA model is a learned, distributed, local algorithm to generate a texture, setting our method apart from existing work on texture generation. We discuss the advantages of such a paradigm.

The existing human pose estimation methods are confronted with inaccurate long-distance regression or high computational cost due to the complex learning objectives. This work proposes a novel deep learning framework for human pose estimation called composite localization to divide the complex learning objective into two simpler ones: a sparse heatmap to find the keypoint's approximate location and two short-distance offsetmaps to obtain its final precise coordinates. To realize the framework, we construct two types of composite localization networks: CLNet-ResNet and CLNet-Hourglass. We evaluate the networks on three benchmark datasets, including the Leeds Sports Pose dataset, the MPII Human Pose dataset, and the COCO keypoints detection dataset. The experimental results show that our CLNet-ResNet50 outperforms SimpleBaseline by 1.14% with about 1/2 GFLOPs. Our CLNet-Hourglass outperforms the original stacked-hourglass by 4.45% on COCO.

In the meantime, a wide variety of terminologies, motivations, approaches and evaluation criteria have been developed within the scope of research on explainable artificial intelligence (XAI). Many taxonomies can be found in the literature, each with a different focus, but also showing many points of overlap. In this paper, we summarize the most cited and current taxonomies in a meta-analysis in order to highlight the essential aspects of the state-of-the-art in XAI. We also present and add terminologies as well as concepts from a large number of survey articles on the topic. Last but not least, we illustrate concepts from the higher-level taxonomy with more than 50 example methods, which we categorize accordingly, thus providing a wide-ranging overview of aspects of XAI and paving the way for use case-appropriate as well as context-specific subsequent research.

As the title suggests, in this blog we are going to learn about text to speech (TTS) synthesis. What is the first bell which rings in your mind when you listen to text to speech? For me, it's Alexa, Google Home, Siri, and many other conversational bots that are on an exponential rise currently. Advances in deep learning research have helped us to generate human-like voices, so let's see how we can use that. I'll start with a few definitions, but if you want to understand these more then read this blog first.