Search Results for author:
Found 30 papers, 5 papers with code
Ethos: Rectifying Language Models in Orthogonal Parameter Space
Evaluations show Ethos is more effective in removing undesired knowledge and maintaining the overall model performance compared to current task arithmetic methods.
Interpretability of Machine Learning: Recent Advances and Future Prospects
The proliferation of machine learning (ML) has drawn unprecedented interest in the study of various multimedia contents such as text, image, audio and video, among others.
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training
However, we show empirically that this method can lead to a substantial drop in training accuracy as well as a slower convergence rate.
Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform
Likewise, the proposed method achieved a maximum per-subject classification accuracy of 81. 53% with 43 DTCWT features and 3 clinical features using the balanced dataset and 64. 97% with 13 DTCWT features and 3 clinical features using the unbalanced dataset.
A GPU-accelerated Algorithm for Distinct Discriminant Canonical Correlation Network
In this paper, a GPU-based accelerated algorithm is proposed to further optimize the DDCCANet algorithm.
Vision Transformer with Convolutions Architecture Search
The high-performance backbone network searched by VTCAS introduces the desirable features of convolutional neural networks into the Transformer architecture while maintaining the benefits of the multi-head attention mechanism.
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities
In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications.
ODMTCNet: An Interpretable Multi-view Deep Neural Network Architecture for Image Feature Representation
This work proposes an interpretable multi-view deep neural network architecture, namely optimal discriminant multi-view tensor convolutional network (ODMTCNet), by integrating statistical machine learning (SML) principles with the deep neural network (DNN) architecture.
Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects
Visual sensation and perception refers to the process of sensing, organizing, identifying, and interpreting visual information in environmental awareness and understanding.
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
In this paper, to further push forward this direction with a comprehensive study in both algorithm and system design, we build FedIoT platform that contains FedDetect algorithm for on-device anomaly data detection and a system design for realistic evaluation of federated learning on IoT devices.
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction
Specifically, we divide the AOPE task into aspect term extraction (ATE) and aspect-specified opinion extraction (ASOE) subtasks; we first extract all the candidate aspect terms and then the corresponding opinion words given the aspect term.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Enhanced Gradient for Differentiable Architecture Search
At the stage of block-level search, a relaxation method based on the gradient is proposed, using an enhanced gradient to design high-performance and low-complexity blocks.
A Discriminative Vectorial Framework for Multi-modal Feature Representation
Due to the rapid advancements of sensory and computing technology, multi-modal data sources that represent the same pattern or phenomenon have attracted growing attention.
A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis
This letter shows that the introduction of the complete discriminatory tensor representation strategy provides an effective vehicle for revealing, and extracting the discriminant representations across the 2D data sets, leading to improved results.
The Property of Frequency Shift in 2D-FRFT Domain with Application to Image Encryption
The Fractional Fourier Transform (FRFT) has been playing a unique and increasingly important role in signal and image processing.
Discriminative Multiple Canonical Correlation Analysis for Information Fusion
In this paper, we propose the Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information analysis and fusion.
The Labeled Multiple Canonical Correlation Analysis for Information Fusion
The objective of multimodal information fusion is to mathematically analyze information carried in different sources and create a new representation which will be more effectively utilized in pattern recognition and other multimedia information processing tasks.
Online Behavioral Analysis with Application to Emotion State Identification
In this paper, we propose a novel discriminative model for online behavioral analysis with application to emotion state identification.
MLMA-Net: multi-level multi-attentional learning for multi-label object detection in textile defect images
For the sake of recognizing and classifying textile defects, deep learning-based methods have been proposed and achieved remarkable success in single-label textile images.
Optimizing Deep Neural Networks through Neuroevolution with Stochastic Gradient Descent
Deep neural networks (DNNs) have achieved remarkable success in computer vision; however, training DNNs for satisfactory performance remains challenging and suffers from sensitivity to empirical selections of an optimization algorithm for training.
A hybrid deep-learning approach for complex biochemical named entity recognition
In this approach, the MHATT mechanism aims to improve the recognition accuracy of abbreviations to efficiently deal with the problem of inconsistency in full-text labels.
Early systems change necessary for catalyzing long-term sustainability in a post-2030 agenda
Progress to-date towards the Sustainable Development Goals (SDGs) has fallen short of expectations and is unlikely to fully meet 2030 targets.
Predicting Geographic Information with Neural Cellular Automata
This paper presents a novel framework using neural cellular automata (NCA) to regenerate and predict geographic information.
Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search
LNS consists of a destroy operator and a repair operator that specify a way to carry out the neighborhood search to solve the Combinatorial Optimization problems.
Learn to Design the Heuristics for Vehicle Routing Problem
This paper presents an approach to learn the local-search heuristics that iteratively improves the solution of Vehicle Routing Problem (VRP).
Superpixel-Based Background Recovery from Multiple Images
Background candidate images are obtained from input raw images with the masks.
Field of View Extension in Computed Tomography Using Deep Learning Prior
In computed tomography (CT), data truncation is a common problem.
Modeling Document-level Causal Structures for Event Causal Relation Identification
We aim to comprehensively identify all the event causal relations in a document, both within a sentence and across sentences, which is important for reconstructing pivotal event structures.
Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach
In the wake of a polarizing election, social media is laden with hateful content.
Detecting Online Hate Speech Using Context Aware Models
In the wake of a polarizing election, the cyber world is laden with hate speech.
