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Found 67 papers, 28 papers with code
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification
Hierarchical Text Classification (HTC), which aims to predict text labels organized in hierarchical space, is a significant task lacking in investigation in natural language processing.
CASP-Net: Rethinking Video Saliency Prediction from an Audio-VisualConsistency Perceptual Perspective
Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain.
Generic-to-Specific Distillation of Masked Autoencoders
Lightweight ViT models limited by the model capacity, however, benefit little from those pre-training mechanisms.
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition
Our DMMG consists of a viewpoint variation min-max game and an edge perturbation min-max game.
Communication under Mixed Gaussian-Impulsive Channel: An End-to-End Framework
Under MGIN channel, classical communication signal schemes and corresponding detection methods usually can not achieve desirable performance as they are optimized with respect to WGN.
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective
TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy.
Can Single-Pass Contrastive Learning Work for Both Homophilic and Heterophilic Graph?
To answer this, we analyze the concentration property of features obtained by neighborhood aggregation on both homophilic and heterophilic graphs, introduce the single-pass graph contrastive learning loss based on the property, and provide performance guarantees of the minimizer of the loss on downstream tasks.
ViT-LSLA: Vision Transformer with Light Self-Limited-Attention
Consequently, this paper presents a light self-limited-attention (LSLA) consisting of a light self-attention mechanism (LSA) to save the computation cost and the number of parameters, and a self-limited-attention mechanism (SLA) to improve the performance.
Variance reduced Shapley value estimation for trustworthy data valuation
Data valuation, especially quantifying data value in algorithmic prediction and decision-making, is a fundamental problem in data trading scenarios.
Anisotropic multiresolution analyses for deepfake detection
We employ the fully separable wavelet transform and multiwavelets to obtain the anisotropic features to feed to standard CNN classifiers.
Joint Microstrip Selection and Beamforming Design for MmWave Systems with Dynamic Metasurface Antennas
Dynamic metasurface antennas (DMAs) provide a new paradigm to realize large-scale antenna arrays for future wireless systems.
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations
This paper derives a sufficient condition for the identifiability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory.
Solving combinational optimization problems with evolutionary single-pixel imaging
Single-pixel imaging (SPI) is a novel optical imaging technique by replacing the pixelated sensor array in a conventional camera with a single-pixel detector.
VLSNR:Vision-Linguistics Coordination Time Sequence-aware News Recommendation
In our work, we propose a vision-linguistics coordinate time sequence news recommendation.
On the Sparse DAG Structure Learning Based on Adaptive Lasso
Learning the underlying Bayesian Networks (BNs), represented by directed acyclic graphs (DAGs), of the concerned events from purely-observational data is a crucial part of evidential reasoning.
Parameter Estimation of Mixed Gaussian-Impulsive Noise: An U-net++ Based Method
In many scenarios, the communication system suffers from both Gaussian white noise and non-Gaussian impulsive noise.
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Interpretability of Deep Learning (DL) models is arguably the barrier in front of trustworthy AI.
HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
A key idea of efficient implementation is to discard the masked image patches (or tokens) throughout the target network (encoder), which requires the encoder to be a plain vision transformer (e. g., ViT), albeit hierarchical vision transformers (e. g., Swin Transformer) have potentially better properties in formulating vision inputs.
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
In this paper, we develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations.
Hierarchical Distribution-Aware Testing of Deep Learning
In this paper, we propose a new robustness testing approach for detecting AEs that considers both the input distribution and the perceptual quality of inputs.
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis
Advanced deep neural networks (DNNs), designed by either human or AutoML algorithms, are growing increasingly complex.
Playing Tic-Tac-Toe Games with Intelligent Single-pixel Imaging
Single-pixel imaging (SPI) is a novel optical imaging technique by replacing a two-dimensional pixelated sensor with a single-pixel detector and pattern illuminations.
Augmentation-Free Graph Contrastive Learning with Performance Guarantee
Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data.
Enhancing Adversarial Training with Second-Order Statistics of Weights
In this paper, we show that treating model weights as random variables allows for enhancing adversarial training through \textbf{S}econd-Order \textbf{S}tatistics \textbf{O}ptimization (S$^2$O) with respect to the weights.
An Audio-Visual Attention Based Multimodal Network for Fake Talking Face Videos Detection
DeepFake based digital facial forgery is threatening the public media security, especially when lip manipulation has been used in talking face generation, the difficulty of fake video detection is further improved.
Attention-Based Lip Audio-Visual Synthesis for Talking Face Generation in the Wild
Rather than focusing on the unimportant regions of the face image, the proposed AttnWav2Lip model is able to pay more attention on the lip region reconstruction.
Audio-visual speech separation based on joint feature representation with cross-modal attention
Multi-modal based speech separation has exhibited a specific advantage on isolating the target character in multi-talker noisy environments.
Look\&Listen: Multi-Modal Correlation Learning for Active Speaker Detection and Speech Enhancement
Active speaker detection and speech enhancement have become two increasingly attractive topics in audio-visual scenario understanding.
Auto-scaling Vision Transformers without Training
The motivation comes from two pain spots: 1) the lack of efficient and principled methods for designing and scaling ViTs; 2) the tremendous computational cost of training ViT that is much heavier than its convolution counterpart.
Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning
In addition to being a pure generalization bound analysis tool, PAC-Bayesian bound can also be incorporated into an objective function to train a probabilistic neural network, making them a powerful and relevant framework that can numerically provide a tight generalization bound for supervised learning.
Achieving Efficient Distributed Machine Learning Using a Novel Non-Linear Class of Aggregation Functions
In this paper, we analyze properties of the WPM and rigorously prove convergence properties of our aggregation mechanism.
Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm
In this paper, we investigate the waveform design from a novel perspective and propose a new waveform design paradigm with the knowledge graph (KG)-based intelligent recommendation system.
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation
Next, two new experimental protocols, zero-shot and few-shot cross-dataset transfer, are designed.
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
The increasing use of Machine Learning (ML) components embedded in autonomous systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the pressing need to assure their functional safety.
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.
Ranked #56 on
Semantic Segmentation
on Cityscapes test
On the Equivalence between Neural Network and Support Vector Machine
Therefore, in this work, we propose to establish the equivalence between NN and SVM, and specifically, the infinitely wide NN trained by soft margin loss and the standard soft margin SVM with NTK trained by subgradient descent.
Deep Active Learning by Leveraging Training Dynamics
Active learning theories and methods have been extensively studied in classical statistical learning settings.
Long-tailed Distribution Adaptation
We propose to jointly optimize empirical risks of the unbalanced and balanced domains and approximate their domain divergence by intra-class and inter-class distances, with the aim to adapt models trained on the long-tailed distribution to general distributions in an interpretable way.
Hierarchy-Aware T5 with Path-Adaptive Mask Mechanism for Hierarchical Text Classification
Hierarchical Text Classification (HTC), which aims to predict text labels organized in hierarchical space, is a significant task lacking in investigation in natural language processing.
Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking
To solve this problem, we propose to distill representations of the TIR modality from the RGB modality with Cross-Modal Distillation (CMD) on a large amount of unlabeled paired RGB-TIR data.
Heterogeneous network-based drug repurposing for COVID-19
The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses (HCoVs), which spreads rapidly around the world.
Differentiable Architecture Search Meets Network Pruning at Initialization: A More Reliable, Efficient, and Flexible Framework
Although Differentiable ARchiTecture Search (DARTS) has become the mainstream paradigm in Neural Architecture Search (NAS) due to its simplicity and efficiency, more recent works found that the performance of the searched architecture barely increases with the optimization proceeding in DARTS, and the final magnitudes obtained by DARTS could hardly indicate the importance of operations.
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications.
Conformer: Local Features Coupling Global Representations for Visual Recognition
Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations.
Ranked #286 on
Image Classification
on ImageNet
PD-GAN: Probabilistic Diverse GAN for Image Inpainting
To this end, we propose spatially probabilistic diversity normalization (SPDNorm) inside the modulation to model the probability of generating a pixel conditioned on the context information.
Advanced Deep Networks for 3D Mitochondria Instance Segmentation
Mitochondria instance segmentation from electron microscopy (EM) images has seen notable progress since the introduction of deep learning methods.
Ranked #1 on
3D Instance Segmentation
on MitoEM
Detecting Operational Adversarial Examples for Reliable Deep Learning
The utilisation of Deep Learning (DL) raises new challenges regarding its dependability in critical applications.
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information
We propose a novel algorithm for multi-player multi-armed bandits without collision sensing information.
DeFLOCNet: Deep Image Editing via Flexible Low-level Controls
While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content.
Sequential Random Network for Fine-grained Image Classification
To address this issue, we use the proposed SRN which composed of BiLSTM and several Tanh-Dropout blocks (called BiLSTM-TDN), to further process DCNN one-dimensional features for highlighting the detail information of image.
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally.
A Unified Framework for Specification Tests of Continuous Treatment Effect Models
We propose a general framework for the specification testing of continuous treatment effect models.
Fairness and Accuracy in Federated Learning
An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed.
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research.
Implicit bias of deep linear networks in the large learning rate phase
We claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the catapult phase.
Embedding and Extraction of Knowledge in Tree Ensemble Classifiers
Whilst, as the increasing use of machine learning models in security-critical applications, the embedding and extraction of malicious knowledge are equivalent to the notorious backdoor attack and its defence, respectively.
Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations
We use CNN features from the deep and shallow layers of the encoder to represent structures and textures of an input image, respectively.
Achievable Rate Region of MISO Interference Channel Aided by Intelligent Reflecting Surface
This paper investigates the achievable rate region of the multiple-input single-output (MISO) interference channel aided by intelligent reflecting surfaces (IRSs).
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization
The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training.
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems
Our work is primarily inspired by the Gaussian Process Latent Variable Model (GPLVM), which was a non-linear dimensionality reduction method.
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply
In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success.
One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block
However, the two-stage architecture is time-consuming, the contextual information lack high-level semantics and ignores both the semantic relevance and distance information of hole's feature patches, these limitations result in blurry textures and distorted structures of final result.
Coverage Guided Testing for Recurrent Neural Networks
The test metrics and test case generation algorithm are implemented into a tool TestRNN, which is then evaluated on a set of LSTM benchmarks.
Transferability of Spectral Graph Convolutional Neural Networks
Transferability, which is a certain type of generalization capability, can be loosely defined as follows: if two graphs describe the same phenomenon, then a single filter or ConvNet should have similar repercussions on both graphs.
testRNN: Coverage-guided Testing on Recurrent Neural Networks
Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction.
Attributes-aided Part Detection and Refinement for Person Re-identification
Person attributes are often exploited as mid-level human semantic information to help promote the performance of person re-identification task.
Multiobjective Optimization Differential Evolution Enhanced with Principle Component Analysis for Constrained Optimization
PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimisation.
