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Found 35 papers, 11 papers with code
Activating Wider Areas in Image Super-Resolution
The prevalence of convolution neural networks (CNNs) and vision transformers (ViTs) has markedly revolutionized the area of single-image super-resolution (SISR).
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation Learning
However, CRL is mainly used as a pre-training technique, leading to a complicated multi-stage training pipeline.
100 Gbps Indoor Access and 4.8 Gbps Outdoor Point-to-Point LiFi Transmission Systems using Laser-based Light Sources
In this paper, we demonstrate the communication capabilities of light-fidelity (LiFi) systems based on highbrightness and high-bandwidth integrated laser-based sources in a surface mount device (SMD) packaging platform.
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
Without bells and whistles, our approach outperforms the state-of-the-art online few-shot learning method by an average of 3. 6\% on eight image classification datasets with higher inference speed.
Skywork: A More Open Bilingual Foundation Model
In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3. 2 trillion tokens drawn from both English and Chinese texts.
Graph Propagation Transformer for Graph Representation Learning
The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks.
Ranked #2 on
Graph Regression
on PCQM4M-LSC
(Validation MAE metric)
Individual Fairness under Uncertainty
Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML.
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19
With COVID-19 now pervasive, identification of high-risk individuals is crucial.
Graph Fourier transform based on singular value decomposition of directed Laplacian
The proposed GFT is consistent with the conventional GFT in the undirected graph setting, and on directed circulant graphs, the proposed GFT is the classical discrete Fourier transform, up to some rotation, permutation and phase adjustment.
Wiener filters on graphs and distributed polynomial approximation algorithms
In this paper, we consider Wiener filters to reconstruct deterministic and (wide-band) stationary graph signals from their observations corrupted by random noises, and we propose distributed algorithms to implement Wiener filters and inverse filters on networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents.
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks
Specifically, in each training iteration and adaptively for each part of the network, the gradient of an auxiliary loss is carefully reduced or enlarged to have a closer magnitude to the gradient of the target loss, preventing auxiliary tasks from being so strong that dominate the target task or too weak to help the target task.
Learning to Adapt to Light
Light adaptation or brightness correction is a key step in improving the contrast and visual appeal of an image.
A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method
Data-driven methods for remaining useful life (RUL) prediction normally learn features from a fixed window size of a priori of degradation, which may lead to less accurate prediction results on different datasets because of the variance of local features.
OneFlow: Redesign the Distributed Deep Learning Framework from Scratch
Aiming at a simple, neat redesign of distributed deep learning frameworks for various parallelism paradigms, we present OneFlow, a novel distributed training framework based on an SBP (split, broadcast and partial-value) abstraction and the actor model.
Dictionary Learning Using Rank-One Atomic Decomposition (ROAD)
Dictionary learning aims at seeking a dictionary under which the training data can be sparsely represented.
Dictionary Learning with Convex Update (ROMD)
Typical methods for dictionary update focuses on refining both dictionary atoms and their corresponding sparse coefficients by using the sparsity patterns obtained from sparse coding stage, and hence it is a non-convex bilinear inverse problem.
Short-and-Sparse Deconvolution Via Rank-One Constrained Optimization (ROCO)
In the literature, formulations of blind deconvolution is either a convex programming via a matrix lifting of convolution, or a bilinear Lasso.
DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement
We propose a Degradation-Aware Module (DA Module) which can guide the training process of the decomposer and enable the decomposer to be a restorer during the training phase without additional computational cost in the test phase.
BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration
We propose a Noise and Color Bias Control module (NCBC Module) that contains a convolutional neural network and two loss functions (noise loss and color loss).
FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity
%on how to exploit strong convexity to further improve the convergence rate of AdaBelief.
Design Diversity for Improving Efficiency and Reducing Risk in Oil and Gas Well Stimulation under Uncertain Reservoir Conditions
Hydraulic fracturing stimulates fracture swarm in reservoir formation though pressurized injection fluid.
Pixel-Face: A Large-Scale, High-Resolution Benchmark for 3D Face Reconstruction
It is also a challenging task due to the lack of high-quality datasets that can fuel current deep learning-based methods.
Adaptive support driven Bayesian reweighted algorithm for sparse signal recovery
For example, sparse Bayesian learning (SBL) was proposed to learn major features from a dictionary of basis functions, which makes identified models interpretable.
Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning
Finally, the PEER score is provided in the form of a nomogram for direct calculation of patient risk, and can be used to highlight at-risk patients among critical care patients eligible for ECMO.
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
Ensemble emotion recognizing with multiple modal physiological signals
Multiple physiological signals fusing models, building the uniform classification model by means of consistent and complementary information from different emotions to improve recognition performance.
Knowledge Distillation in Document Retrieval
In this paper we show that knowledge distillation can be used to encourage a model that generates claim independent document encodings to mimic the behavior of a more complex model which generates claim dependent encodings.
Combining Machine Learning Models using combo Library
Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications.
Oceanic Eddy Identification Using an AI Scheme
Oceanic eddies play an important role in global energyand material transport, and contribute greatly to nutrient and phytoplankton distribution.
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series
This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases.
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis
However, for diverse working conditions in the industry, deep learning suffers two difficulties: one is that the well-defined (source domain) and new (target domain) datasets are with different feature distributions; another one is the fact that insufficient or no labelled data in target domain significantly reduce the accuracy of fault diagnosis.
A General End-to-end Diagnosis Framework for Manufacturing Systems
A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications.
A deep learning-based remaining useful life prediction approach for bearings
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs).
Measuring Territorial Control in Civil Wars Using Hidden Markov Models: A Data Informatics-Based Approach
Territorial control is a key aspect shaping the dynamics of civil war.
A Deep Regression Architecture With Two-Stage Re-Initialization for High Performance Facial Landmark Detection
At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation.
