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Found 39 papers, 8 papers with code
Debiasing Machine Unlearning with Counterfactual Examples
This bias emerges from two main sources: (1) data-level bias, characterized by uneven data removal, and (2) algorithm-level bias, which leads to the contamination of the remaining dataset, thereby degrading model accuracy.
First Mapping the Canopy Height of Primeval Forests in the Tallest Tree Area of Asia
We proposed a method to map the canopy height of the primeval forest within the world-level giant tree distribution area by using a spaceborne LiDAR fusion satellite imagery (Global Ecosystem Dynamics Investigation (GEDI), ICESat-2, and Sentinel-2) driven deep learning modeling.
Document Set Expansion with Positive-Unlabelled Learning Using Intractable Density Estimation
The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents.
SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification
In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains.
Document Set Expansion with Positive-Unlabeled Learning: A Density Estimation-based Approach
Previous work shows that PU learning is a promising method for this task.
AttentionLut: Attention Fusion-based Canonical Polyadic LUT for Real-time Image Enhancement
Subsequently, the attention fusion module integrates the image feature with the priori attention feature obtained during training to generate image-adaptive canonical polyadic tensors.
Fast data-driven iterative learning control for linear system with output disturbance
This paper studies data-driven iterative learning control (ILC) for linear time-invariant (LTI) systems with unknown dynamics, output disturbances and input box-constraints.
Dynamic Fair Federated Learning Based on Reinforcement Learning
Federated learning enables a collaborative training and optimization of global models among a group of devices without sharing local data samples.
RAWIW: RAW Image Watermarking Robust to ISP Pipeline
To achieve end-to-end training of the framework, we integrate a neural network that simulates the ISP pipeline to handle the RAW-to-RGB conversion process.
Emulating Reader Behaviors for Fake News Detection
To this end, we propose an approach of Emulating the behaviors of readers (Ember) for fake news detection on social media, incorporating readers' reading and verificating process to model news from the component perspective thoroughly.
Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease
Validation analysis revealed that the AUCs of DLRP were 0. 91 for GEV (95% CI 0. 90 to 0. 93, p < 0. 05) and 0. 88 for HRV (95% CI 0. 86 to 0. 89, p < 0. 01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM.
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
By reversing the learning process of the recommendation model, we thus develop a proficient greedy algorithm to generate fabricated user profiles and their associated interaction records for the aforementioned surrogate model.
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis
A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels.
Masked Autoencoders as Image Processors
Recently, masked autoencoders (MAE) for feature pre-training have further unleashed the potential of Transformers, leading to state-of-the-art performances on various high-level vision tasks.
Ranked #4 on
Image Defocus Deblurring
on DPD (Dual-view)
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry.
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the list of suggestions for a given user.
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach
One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters.
Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon
In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the SCCL model based on capsule network and sentiment lexicon.
Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements
It is a challenging task to predict financial markets.
A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment
To improve the IVF success rate, we propose a knowledge-based decision support system that can provide medical advice on the treatment protocol and medication adjustment for each patient visit during IVF treatment cycle.
Bounds all around: training energy-based models with bidirectional bounds
Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train.
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets.
Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos
A lightweight panoramic annular semantic segmentation neural network model is designed to achieve high-accuracy and real-time scene parsing.
Ranked #69 on
Semantic Segmentation
on Cityscapes val
An Improved Discriminative Optimization for 3D Rigid Point Cloud Registration
The Discriminative Optimization (DO) algorithm has been proved much successful in 3D point cloud registration.
CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets
Financial markets are a complex dynamical system.
Financial Markets Prediction with Deep Learning
Financial markets are difficult to predict due to its complex systems dynamics.
Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning
In FL, however, the model update is an indirect multi-terminal source coding problem, also called as the CEO problem where each edge device cannot observe directly the gradient that is to be reconstructed at the decoder, but is rather provided only with a noisy version.
KNN-enhanced Deep Learning Against Noisy Labels
Inspired by the robustness of K-Nearest Neighbors (KNN) against data noise, in this work, we propose to apply deep KNN for label cleanup.
A Learning-based Optimization Algorithm:Image Registration Optimizer Network
As the search space of registration is usually non-convex, the optimization algorithm, which aims to search the best transformation parameters, is a challenging step.
Generative Model without Prior Distribution Matching
Variational Autoencoder (VAE) and its variations are classic generative models by learning a low-dimensional latent representation to satisfy some prior distribution (e. g., Gaussian distribution).
Uniform Interpolation Constrained Geodesic Learning on Data Manifold
Based on this defined Riemannian metric, we introduce a constant speed loss and a minimizing geodesic loss to regularize the interpolation network to generate uniform interpolation along the learned geodesic on the manifold.
Alleviation of Gradient Exploding in GANs: Fake Can Be Real
In order to alleviate the notorious mode collapse phenomenon in generative adversarial networks (GANs), we propose a novel training method of GANs in which certain fake samples are considered as real ones during the training process.
Efficient training and design of photonic neural network through neuroevolution
Recently, optical neural networks (ONNs) integrated in photonic chips has received extensive attention because they are expected to implement the same pattern recognition tasks in the electronic platforms with high efficiency and low power consumption.
Understanding VAEs in Fisher-Shannon Plane
In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables.
Joint Topic-Semantic-aware Social Recommendation for Online Voting
JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization.
Topological Recurrent Neural Network for Diffusion Prediction
As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure.
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.
Ranked #1 on
Node Classification
on Wikipedia
Unsupervised Triplet Hashing for Fast Image Retrieval
Considering the difficulty in obtaining labeled datasets for image retrieval task in large scale, we propose a novel CNN-based unsupervised hashing method, namely Unsupervised Triplet Hashing (UTH).
Truss Decomposition in Massive Networks
We first improve the existing in-memory algorithm for computing k-truss in networks of moderate size.
Databases
