The Dawn of High-throughput and Genome-scale Kinetic Modeling: Recent Advances and Future Directions

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We consider the problem of computing mixed Nash equilibria of two-player zero-sum games with continuous sets of pure strategies and with first-order access to the payoff function. This problem arises for example in game-theory-inspired machine learning app ...

MapPool is a dataset of 75 million potential maps and textual captions. It has been derived from CommonPool, a dataset consisting of 12 billion text-image pairs from the Internet. The images have been encoded by a vision transformer and classified into map ...

Evaluating the visual quality of distorted images is a crucial task in the field of image compression, as artifacts may significantly impair the appeal and fidelity of images, therefore reducing the user experience. As assessing the visual quality of image ...

Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulatio ...

In this paper, we propose a novel adaptive feedforward super-twisting sliding mode control algorithm to resolve the tracking control problem of parallel manipulators. The proposed control scheme includes three main terms, (i) the standard super-twisting al ...

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for co ...

One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially-chosen perturbations of data. Despite the promi ...

Preserving privacy is a growing concern in our society where cameras are ubiquitous. In this work, we propose a trainable image acquisition method that removes the sensitive information in the optical domain before it reaches the image sensor. The method b ...

In the past decade, optical diffraction tomography has gained a lot of attention for its ability to create label-free three-dimensional (3D) images of the refractive index distribution of biological samples using scattered fields measured through holograph ...

Neural-network quantum states (NQS) have been shown to be a suitable variational ansatz to simulate out-of-equilibrium dynamics in two-dimensional systems using timedependent variational Monte Carlo (t-VMC). In particular, stable and accurate time propagat ...

Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to misl ...

The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (AT ...

The Dawn of High-throughput and Genome-scale Kinetic Modeling: Recent Advances and Future Directions

Chat with Graph Search

An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games

MapPool -Bubbling up an extremely large corpus of maps for AI

Benchmarking conventional and learning-based objective image quality metrics on compression artifacts

Quantifying the biomimicry gap in biohybrid robot-fish pairs

Adaptive Feedforward Super-Twisting Sliding Mode Control of Parallel Kinematic Manipulators With Real-Time Experiments

Machine Learning Security in Industry: A Quantitative Survey

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Adversarial Training Should Be Cast As a Non-Zero-Sum Game

Privacy-Preserving Image Acquisition for Neural Vision Systems

Exploring Novel Modalities for Optical Diffraction Tomography

Role of stochastic noise and generalization error in the time propagation of neural-network quantum states

Natural-Looking Adversarial Examples From Freehand Sketches

Auto-tuning deep forest for shear stiffness prediction of headed stud connectors