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Found 25 papers, 9 papers with code
Transfer Learning with Reconstruction Loss
In most applications of utilizing neural networks for mathematical optimization, a dedicated model is trained for each specific optimization objective.
Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization
Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix.
Variational Quantum Circuits Enhanced Generative Adversarial Network
Generative adversarial network (GAN) is one of the widely-adopted machine-learning frameworks for a wide range of applications such as generating high-quality images, video, and audio contents.
Quantum-Inspired Machine Learning for Molecular Docking
Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking.
Accelerating In-Browser Deep Learning Inference on Diverse Edge Clients through Just-in-Time Kernel Optimizations
Web applications are increasingly becoming the primary platform for AI service delivery, making in-browser deep learning (DL) inference more prominent.
Reinforcement Learning with Non-Cumulative Objective
In this paper, we recognize the prevalence of non-cumulative objectives in various problems, and propose a modification to existing algorithms for optimizing such objectives.
Uncertainty Injection: A Deep Learning Method for Robust Optimization
This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems.
Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
Generalized Complete Intersection Calabi-Yau Manifold (gCICY) is a new construction of Calabi-Yau manifolds established recently.
AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization
The AirFi is a novel domain generalization framework that learns the critical part of CSI regardless of different environments and generalizes the model to unseen scenarios, which does not require collecting any data for adaptation to the new environment.
Tutel: Adaptive Mixture-of-Experts at Scale
On efficiency, Flex accelerates SwinV2-MoE, achieving up to 1. 55x and 2. 11x speedup in training and inference over Fairseq, respectively.
Efficient Quantum Feature Extraction for CNN-based Learning
Recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as general function approximators.
Active Sensing for Communications by Learning
This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a predetermined number of time frames to gather information in order to perform a sensing or actuation task for maximizing some utility function.
Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models
In this paper, we present non-asymptotic optimization guarantees of gradient descent methods for estimating structured transition matrices in high-dimensional vector autoregressive (VAR) models.
HUBS: A dedicated hot circumgalactic medium explorer
The latter (missing baryons) are thought to be hiding in low-density warm-hot ionized medium (WHIM), based on results from theoretical studies and recent observations, and be distributed in the vicinity of galaxies (i. e., circum-galactic medium) and between galaxies (i. e., intergalactic medium).
Instrumentation and Methods for Astrophysics
Phase Transitions in Recovery of Structured Signals from Corrupted Measurements
Our theoretical results show that these phase transitions are determined by some geometric measures of structure, e. g., the spherical Gaussian width of a tangent cone and the Gaussian (squared) distance to a scaled subdifferential.
Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer
This paper proposes a novel scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks.
Low-Power Wireless Wearable ECG Monitoring Chestbelt Based on Ferroelectric Microprocessor
The ECG monitoring device, abbreviated as ECGM, is designed based on ferroelectric microprocessor which provides ultra-low power consumption and contains four parts-MCU, BLE, Sensors and Power.
Algorithmic Decision Making with Conditional Fairness
In practice, there frequently exist a certain set of variables we term as fair variables, which are pre-decision covariates such as users' choices.
Data-based wind disaster climate identification algorithm and extreme wind speed prediction
Based on classification results, the extreme wind speeds calculated based on mixed wind hazard types is compared with those obtained from conventional methods, and the effects on structural design for different return periods are discussed.
ECG Identification under Exercise and Rest Situations via Various Learning Methods
By applying various existing learning methods to our ECG dataset, we find that current methods which can well support the identification of individuals under rests, do not suffice to present satisfying ECGID performance under exercise situations, therefore exposing the deficiency of existing ECG identification methods.
Spatial Deep Learning for Wireless Scheduling
The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task.
Identifying the Mislabeled Training Samples of ECG Signals using Machine Learning
The classification accuracy of electrocardiogram signal is often affected by diverse factors in which mislabeled training samples issue is one of the most influential problems.
Optical Mapping Near-eye Three-dimensional Display with Correct Focus Cues
We present an optical mapping near-eye (OMNI) three-dimensional display method for wearable devices.
Longest-path attacks on complex networks
We propose two algorithms, the random augmenting approach (RPA) and the Hamilton-path based approach (HPA), for finding the approximately longest simple path in a network.
Data Analysis, Statistics and Probability
LSST: from Science Drivers to Reference Design and Anticipated Data Products
About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18, 000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27. 5$.
