SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudoku puzzles visually from examples of puzzle digit images, and was heralded as an impressive achievement towards the longstanding AI goal of combining pattern recognition with logical reasoning. In this paper, we clarify SATNet's capabilities by showing that in the absence of intermediate labels that identify individual Sudoku digit images with their logical representations, SATNet completely fails at visual Sudoku (0% test accuracy). More generally, the failure can be pinpointed to its inability to learn to assign symbols to perceptual phenomena, also known as the symbol grounding problem, which has long been thought to be a prerequisite for intelligent agents to perform real-world logical reasoning. We propose an MNIST based test as an easy instance of the symbol grounding problem that can serve as a sanity check for differentiable symbolic solvers in general.

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing valuable datasets obsolete. However, recreating similar datasets with newer sensor technologies is both tedious and time-consuming. Therefore, it is crucial to adapt these existing datasets for use with new setups and modalities. In response, we introduce ACROSS, a novel framework for translating data between tactile sensors by exploiting sensor deformation information. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of first converting the input signals into 3D deformation meshes. We then transition from the 3D deformation mesh of one sensor to the mesh of another, and finally convert the generated 3D deformation mesh into the corresponding output space. We demonstrate our approach to the most challenging problem of going from a low-dimensional tactile representation to a high-dimensional one. In particular, we transfer the tactile signals of a BioTac sensor to DIGIT tactile images. Our approach enables the continued use of valuable datasets and the exchange of data between groups with different setups.

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing datasets obsolete. This article introduces a novel method for translating data between tactile sensors by exploiting sensor deformation information rather than output signals. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of three steps: first, converting signal data into corresponding 3D deformation meshes; second, translating these 3D deformation meshes from one sensor to another; and third, generating output images using the converted meshes. Our approach enables the continued use of valuable datasets.

Deep learning has been extensively used in various fields, such as phase imaging, 3D imag ing reconstruction, phase unwrapping, and laser speckle reduction, particularly for complex problems that lack analytic models. Its data - driven nature allows for implicit construction of mathematical relationships within the network through training with abun dant data. However, a critical challenge in practical applications is the generalization issue, where a network trained on one dataset struggles to recognize an unknown target from a different dataset. In this study, we investigate imaging through scatteri ng media and discover that the mathematical relationship learned by the network is an approximation dependent on the training dataset, rather than the true mapping relationship of the model. W e demonstrate that enhancing the diversity of the training datas et can improve this approximation, thereby achieving generalization across different datasets, as the mapping relationship of a linear physical model is independent of inputs. This study elucidates the nature of generalization across different datasets and provides insights into the design of training datasets to ultimately address the generalization issue in various deep learning - based applications . Introduction The study of imaging through scattering media is a challenging and cutting - edge field. Scattering media are ubiquitous in everyday life, such as rough surfaces, clouds, fog, dust, water, and biological tissues. Image reconstruction through these media is p articularly important in areas such as transportation, military, and biomedicine .

SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudoku puzzles visually from examples of puzzle digit images, and was heralded as an impressive achievement towards the longstanding AI goal of combining pattern recognition with logical reasoning. In this paper, we clarify SATNet's capabilities by showing that in the absence of intermediate labels that identify individual Sudoku digit images with their logical representations, SATNet completely fails at visual Sudoku (0% test accuracy). More generally, the failure can be pinpointed to its inability to learn to assign symbols to perceptual phenomena, also known as the symbol grounding problem, which has long been thought to be a prerequisite for intelligent agents to perform real-world logical reasoning. We propose an MNIST based test as an easy instance of the symbol grounding problem that can serve as a sanity check for differentiable symbolic solvers in general. We report on the causes of SATNet's failure and how to prevent them.

The spatial photonic Ising machine (SPIM) [D. Pierangeli et al., Phys. Rev. Lett. 122, 213902 (2019)] is a promising optical architecture utilizing spatial light modulation for solving large-scale combinatorial optimization problems efficiently. The primitive version of the SPIM, however, can accommodate Ising problems with only rank-one interaction matrices. In this Letter, we propose a new computing model for the SPIM that can accommodate any Ising problem without changing its optical implementation. The proposed model is particularly efficient for Ising problems with low-rank interaction matrices, such as knapsack problems. Moreover, it acquires the learning ability of Boltzmann machines. We demonstrate that learning, classification, and sampling of the MNIST handwritten digit images are achieved efficiently using the model with low-rank interactions. Thus, the proposed model exhibits higher practical applicability to various problems of combinatorial optimization and statistical learning, without losing the scalability inherent in the SPIM architecture.

If you're working in deep learning, you've probably heard of GANs, or Generative Adversarial Networks (Goodfellow et al, 2014). In post we will explain what GANs are and discuss some use cases with examples. I am adding to this post a link to my GAN playground, called MP-GAN (Multi Purpose GAN). I prepared this playground in github as a research framework, and you are welcome to use it to train and explore GANs for yourselves. GANs are part of a family of generative deep learning architectures, whose goal is to generate synthetic data, instead of predicting features of data points (these are the more common discriminative models, such as classifiers and regressors.

A generative adversarial network is a subclass of machine learning frameworks in which when we give a training set, this technique learns to generate new data with the same statistics as the training set with the help of algorithmic architectures that uses two neural networks to generate new, synthetic instances of data that is very much similar to the real data and GANs were designed in 2014 by Ian Goodfellow and his colleagues. GANs are usually trained to generate images from random noises and a GAN has usually two parts in which it works namely the Generator that generates new samples of images and the second is a Discriminator that classifies images as real or fake for example we can train a GAN model to generate digit images that look like hand-written digit images from the MNIST dataset and apart from this GANs are widely used for voice generation, image generation or video generation. There are a variety of reasons why fans are so exciting and one of them is because GANs were the first generative algorithms to give convincingly good results also they have opened up many new directions for research and GANs themselves is considered to be the most prominent research in machine learning in the last several years, and since then GANs have started a revolution in deep learning and this revolution has produced some major technological breakthroughs in the history of computer science and artificial intelligence. From the perspective of AI researchers, this was a breakthrough. Generative adversarial networks (GANs) have been improved over the years and despite all the hurdles brought by this past decade of research, GANs have generated content that will become increasingly difficult to distinguish from real content and comparing image generation in 2014 to today, the quality was not expected to become that good and if the progress continues like this, GANs will remain a very important research project in future provided the acceptance of GANs and their applications by the research community.

In this paper, we disseminate a new handwritten digits-dataset, termed Kannada-MNIST, for the Kannada script, that can potentially serve as a direct drop-in replacement for the original MNIST dataset. In addition to this dataset, we disseminate an additional real world handwritten dataset (with $10k$ images), which we term as the Dig-MNIST dataset that can serve as an out-of-domain test dataset. We also duly open source all the code as well as the raw scanned images along with the scanner settings so that researchers who want to try out different signal processing pipelines can perform end-to-end comparisons. We provide high level morphological comparisons with the MNIST dataset and provide baselines accuracies for the dataset disseminated. The initial baselines obtained using an oft-used CNN architecture ($96.8\%$ for the main test-set and $76.1\%$ for the Dig-MNIST test-set) indicate that these datasets do provide a sterner challenge with regards to generalizability than MNIST or the KMNIST datasets. We also hope this dissemination will spur the creation of similar datasets for all the languages that use different symbols for the numeral digits.

Swift for TensorFlow was introduced by Chris Lattner at TensorFlow Dev Summit 2018. On April 27, 2018 Google team has made its first release to public community on their GitHub repository. But Swift for TensorFlow is still in its infancy stage. And it seems to be too early for developers/researchers to use it in their projects. If you are still interested in trying it out then install Swift for TensorFlow's snapshot from Swift Official website.