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Found 54 papers, 13 papers with code
Sparse Structure Search for Parameter-Efficient Tuning
The searched structures preserve more than 99\% fine-tuning performance with 0. 01\% trainable parameters.
RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring
Accordingly, we for the first time propose to model the biased credit scoring data with Multi-Task Learning (MTL).
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
MolMiner: You only look once for chemical structure recognition
Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR).
Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems
In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems.
Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump
As the pump-and-dump schemes (P&Ds) proliferate in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance, to inform potentially susceptible investors before they become victims.
Enhanced Contour Tracking: a Time-Varying Internal Model Principle-Based Approach
The important features of the TV-IMCC lie in: 1) it is of an asymptotical stability for irregular contour tracking compared to the cross coupled control (CCC); 2) it does not lead to system nonlinearity and hence is well-suited for tracking multi-axis contours compared to the task coordinate frame (TCF) control; 3) it reduces the tracking error and extends the class of contours can be tracked compared to the position domain control (PDC).
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks
The dynamics of systems biological processes are usually modeled by a system of ordinary differential equations (ODEs) with many unknown parameters that need to be inferred from noisy and sparse measurements.
SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems
Solving high-dimensional optimal control problems in real-time is an important but challenging problem, with applications to multi-agent path planning problems, which have drawn increased attention given the growing popularity of drones in recent years.
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems
We propose the GENERIC formalism informed neural networks (GFINNs) that obey the symmetric degeneracy conditions of the GENERIC formalism.
Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction
We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly.
Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data
Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data.
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation
Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc.
Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction
We propose a novel method for automatic separation of pulmonary arteries and veins from chest CT images.
Adaptive Optimizers with Sparse Group Lasso
We develop a novel framework that adds the regularizers to a family of adaptive optimizers in deep learning, such as MOMENTUM, ADAGRAD, ADAM, AMSGRAD, ADAHESSIAN, and create a new class of optimizers, which are named GROUP MOMENTUM, GROUP ADAGRAD, GROUP ADAM, GROUP AMSGRAD and GROUP ADAHESSIAN, etc., accordingly.
Market Impact in Trader-Agents: Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems
We demonstrate that the new imbalance-sensitive trader-agents introduced here do exhibit market impact effects, and hence are better-suited to operating in markets where impact is a factor of concern or interest, but do not suffer the weaknesses of the methods used by Church & Cliff.
Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks
We propose the Poisson neural networks (PNNs) to learn Poisson systems and trajectories of autonomous systems from data.
Factor Graph Neural Networks
Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks.
Cyclic Label Propagation for Graph Semi-supervised Learning
To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.
Deep Reinforcement Learning of Transition States
Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL$^\ddag$) to automatically unravel chemical reaction mechanisms.
CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints
In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem.
Robotics Multiagent Systems
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint
Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images.
Kagome quantum anomalous Hall effect with high Chern number and large band gap
For the bilayer compound Co6Sn5Se4, it becomes a half-metal, with a relatively flat plateau in its anomalous Hall conductivity corresponding to |C| = 3 near the Fermi level.
Materials Science
Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images
The PRSN is designed to segment pathological region based on the result of ASSN, in which a fusion block based on channel attention is proposed to better aggregate multi-modality information from multi-modality CMR images.
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making.
FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing
We quantify the strength of our proposed framework in different cases and compare it to the existing data-driven approaches.
Is Network the Bottleneck of Distributed Training?
As such, we advocate that the real challenge of distributed training is for the network community to develop high-performance network transport to fully utilize the network capacity and achieve linear scale-out.
Time-Stretched Femtosecond Lidar Using Microwave Photonic Signal Processing
After that, the frequency variation of the microwave pulse is uploaded to the first order sidebands.
A Perspective on Deep Learning for Molecular Modeling and Simulations
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems.
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
The shared sub-structures between training classes and test classes are essential in few-shot graph classification.
Multiplicative Gaussian Particle Filter
We propose a new sampling-based approach for approximate inference in filtering problems.
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules.
KerGM: Kernelized Graph Matching
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics.
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
In particular, RGE is shown to achieve \emph{(quasi-)linear scalability} with respect to the number and the size of the graphs.
Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition
We address the problem of Visual Relationship Detection (VRD) which aims to describe the relationships between pairs of objects in the form of triplets of (subject, predicate, object).
Hierarchical Graph Pooling with Structure Learning
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on
Graph Classification
on PROTEINS
Deep Graphical Feature Learning for the Feature Matching Problem
Rich local representation is a key part of efficient feature matching methods.
Learning Clustered Representation for Complex Free Energy Landscapes
In this paper we first analyzed the inductive bias underlying the data scattered across complex free energy landscapes (FEL), and exploited it to train deep neural networks which yield reduced and clustered representation for the FEL.
Factor Graph Neural Network
Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks.
phq: a Fortran code to compute phonon quasiparticle properties and dispersions
We here introduce a Fortran code that computes anharmonic free energy of solids from first-principles based on our phonon quasiparticle approach.
Materials Science
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks.
Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation
Domain shift, which occurs when there is a mismatch between the distributions of training (source) and testing (target) datasets, usually results in poor performance of the trained model on the target domain.
Convolutional Sequence to Sequence Model for Human Dynamics
Human motion modeling is a classic problem in computer vision and graphics.
OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery
We developed OmicsMapNet approach to take advantage of existing deep leaning frameworks to analyze high-dimensional omics data as 2-dimensional images.
Learning Deep Gradient Descent Optimization for Image Deconvolution
Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.
Depth and Image Restoration From Light Field in a Scattering Medium
First, we present a new single image restoration algorithm which removes backscatter and attenuation from images better than existing methods, and apply it to each view in the light field.
Pairwise Matching Through Max-Weight Bipartite Belief Propagation
Feature matching is a key problem in computer vision and pattern recognition.
Joint Probabilistic Matching Using m-Best Solutions
Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score.
Joint Probabilistic Data Association Revisited
In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program.
Learning Graph Structure for Multi-Label Image Classification via Clique Generation
Exploiting label dependency for multi-label image classification can significantly improve classification performance.
Managing Multi-Granular Linguistic Distribution Assessments in Large-Scale Multi-Attribute Group Decision Making
Linguistic large-scale group decision making (LGDM) problems are more and more common nowadays.
Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations
We show that using Marginal Polytope Diagrams allows the number of constraints to be reduced without loosening the LP relaxations.
A feasible roadmap for unsupervised deconvolution of two-source mixed gene expressions
Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling.
