Investigating Graph Embedding Methods for Cross-Platform Binary Code Similarity Detection

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The dynamical cavity method and its backtracking version provide a powerful approach to studying the properties of dynamical processes on large random graphs. This work extends these methods to hypergraphs, enabling the analysis of interactions involving m ...

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Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

Group testing is a technique that can reduce the number of tests needed to identify infected members in a population, by pooling together multiple diagnostic samples. Despite the variety and importance of prior results, traditional work on group testing ha ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Interactive oracle proofs (IOPs) are a proof system model that combines features of interactive proofs (IPs) and probabilistically checkable proofs (PCPs). IOPs have prominent applications in complexity theory and cryptography, most notably to constructing ...

Assessing the traversability of rugged terrain is a difficult challenge for legged robots, especially when they implement multiple, distinct gaits. We tackle this problem on the k-rock2 amphibious, sprawling gait robot by training a gait-dependent traversa ...

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory f ...

We present a new family of active surfaces for the semiautomatic segmentation of volumetric objects in 3D biomedical images. We represent our deformable model by a subdivision surface encoded by a small set of control points and generated through a geometr ...

Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks. We here prove that feed-forward ...

Investigating Graph Embedding Methods for Cross-Platform Binary Code Similarity Detection

Chattez avec Graph Search

Dynamical cavity method for hypergraphs and its application to quenches in the k-XOR-SAT problem

Source-Free Domain-Invariant Performance Prediction

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Community-Aware Group Testing

Bayes-optimal Learning of Deep Random Networks of Extensive-width

A deep learning-approach to infer somatic alterations in glioma using digital tissue slides

A Toolbox for Barriers on Interactive Oracle Proofs

Gait-dependent Traversability Estimation on the k-rock2 Robot

Learning of physical systems: from inference to control

Robotic automation and unsupervised cluster assisted modeling for solving the forward and reverse design problem of paper airplanes

Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

Contrastive Learning Inverts the Data Generating Process