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Found 23 papers, 5 papers with code
Computing committor functions for the study of rare events using deep learning with importance sampling
The committor function is a central object of study in understanding transitions between metastable states in complex systems.
Deep Learning Method for Computing Committor Functions with Adaptive Sampling
In this work, we propose a deep learning method with two novel adaptive sampling schemes (I and II).
Texture Classification Network Integrating Adaptive Wavelet Transform
Graves' disease is a common condition that is diagnosed clinically by determining the smoothness of the thyroid texture and its morphology in ultrasound images.
Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learning
In fact, computing the transition pathway is a challenging task for complex and high-dimensional systems.
Environment Reconstruction based on Multi-User Selection and Multi-Modal Fusion in ISAC
Integrated sensing and communications (ISAC) has been deemed as a key technology for the sixth generation (6G) wireless communications systems.
Moving Target Sensing for ISAC Systems in Clutter Environment
In this paper, we consider the moving target sensing problem for integrated sensing and communication (ISAC) systems in clutter environment.
Boosting Generalization with Adaptive Style Techniques for Fingerprint Liveness Detection
We introduce a high-performance fingerprint liveness feature extraction technique that secured first place in LivDet 2023 Fingerprint Representation Challenge.
AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing
Cycling stress assessment, which quantifies cyclists' perceived stress imposed by the built environment and motor traffics, increasingly informs cycling infrastructure planning and cycling route recommendation.
PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion
The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones.
A Generalized Semantic Communication System: from Sources to Channels
This article introduces a framework for generalized semantic communication system, which exploits the semantic information in both the multimodal source and the wireless channel environment.
SimpleDG: Simple Domain Generalization Baseline without Bells and Whistles
We present a simple domain generalization baseline, which wins second place in both the common context generalization track and the hybrid context generalization track respectively in NICO CHALLENGE 2022.
Machine Learning-Augmented Optimization of Large Bilevel and Two-stage Stochastic Programs: Application to Cycling Network Design
We prove bounds on the optimality gap of the generated leader decision as measured by the original objective that considers the full follower set.
Efficient One Pass Self-distillation with Zipf's Label Smoothing
This paper proposes an efficient self-distillation method named Zipf's Label Smoothing (Zipf's LS), which uses the on-the-fly prediction of a network to generate soft supervision that conforms to Zipf distribution without using any contrastive samples or auxiliary parameters.
Multi-Camera View Based Proactive BS Selection and Beam Switching for V2X
In this paper, we proposed a multi-camera view based proactive BS selection and beam switching that can predict the optimal BS of the user in the future frame and switch the corresponding beam pair.
Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning
The potential component of the decomposition gives the generalized potential.
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems
A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation.
A Data Driven Method for Computing Quasipotentials
The quasipotential is a natural generalization of the concept of energy functions to non-equilibrium systems.
Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows
The past decade has seen a rapid penetration of electric vehicles (EV) in the market, more and more logistics and transportation companies start to deploy EVs for service provision.
Bounding The Number of Linear Regions in Local Area for Neural Networks with ReLU Activations
In this paper, we present the first method to estimate the upper bound of the number of linear regions in any sphere in the input space of a given ReLU neural network.
Tropical Optimal Transport and Wasserstein Distances
We study the problem of optimal transport in tropical geometry and define the Wasserstein-$p$ distances in the continuous metric measure space setting of the tropical projective torus.
Optimization and Control Metric Geometry Statistics Theory Statistics Theory
Computing Committor Functions for the Study of Rare Events Using Deep Learning
The committor function is a central object of study in understanding transitions between metastable states in complex systems.
Linear and Rational Factorization of Tropical Polynomials
Already for bivariate tropical polynomials, factorization is an NP-Complete problem.
Combinatorics
Demand-Driven Incremental Object Queries
Object queries are essential in information seeking and decision making in vast areas of applications.
