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Found 68 papers, 21 papers with code
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation
We thus consider a more realistic \textit{Few-Shot Video-based Domain Adaptation} (FSVDA) scenario where we adapt video models with only a few target video samples.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
MetaGrad: Adaptive Gradient Quantization with Hypernetworks
A popular track of network compression approach is Quantization aware Training (QAT), which accelerates the forward pass during the neural network training and inference.
Convex Bounds on the Softmax Function with Applications to Robustness Verification
The softmax function is a ubiquitous component at the output of neural networks and increasingly in intermediate layers as well.
Data-Centric AI: Deep Generative Differentiable Feature Selection via Discrete Subsetting as Continuous Embedding Space Optimization
We generalize this problem into a deep differentiable feature selection task and propose a new perspective: discrete feature subsetting as continuous embedding space optimization.
Label-efficient Time Series Representation Learning: A Review
The scarcity of labeled data is one of the main challenges of applying deep learning models on time series data in the real world.
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
Lastly, compared to other methods, GraphST’s cell type deconvolution achieved higher accuracy on simulated data and better captured spatial niches such as the germinal centers of the lymph node in experimentally acquired data.
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents
Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML).
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach
To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound.
CoTMix: Contrastive Domain Adaptation for Time-Series via Temporal Mixup
Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains.
VeriX: Towards Verified Explainability of Deep Neural Networks
We present VeriX, a system for producing optimal robust explanations and generating counterfactuals along decision boundaries of machine learning models.
Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation
The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC).
Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals
In response to this question, we propose a hierarchical mixup multiple-label classification framework, which we called H-MixUp.
Self-Optimizing Feature Transformation
Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features.
SwiftPruner: Reinforced Evolutionary Pruning for Efficient Ad Relevance
Remarkably, under our latency requirement of 1900us on CPU, SwiftPruner achieves a 0. 86% higher AUC than the state-of-the-art uniform sparse baseline for BERT-Mini on a large scale real-world dataset.
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module.
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models.
A Generalized Probabilistic Monitoring Model with Both Random and Sequential Data
Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades.
Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders
We also perform a feature selection stability analysis on our models and notice that there is a power-law relationship with features which are commonly associated with survival.
Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease
Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands.
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community.
Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction
A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions.
RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System
Speech enhancement and separation have been a long-standing problem, especially with the recent advances using a single microphone.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis.
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data in addition to the recent methods specifically developed for time series data.
Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath).
Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e. g. via Dual-energy X-ray Absorptiometry (DXA).
Motif Graph Neural Network
However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing \emph{high-order} graph structures as opposed to \emph{low-order} structures.
Self-supervised Autoregressive Domain Adaptation for Time Series Data
Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependency of both source and target features during domain alignment.
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data
Our evaluation includes adaptations of state-of-the-art visual domain adaptation methods to time series data in addition to recent methods specifically developed for time series data.
Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.
A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO2 Monitoring Using Smartphone Cameras
Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1. 26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
Remote Blood Oxygen Estimation From Videos Using Neural Networks
Blood oxygen saturation (SpO$_2$) is an essential indicator of respiratory functionality and is receiving increasing attention during the COVID-19 pandemic.
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.
Time-Series Representation Learning via Temporal and Contextual Contrasting
In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.
Ranked #1 on
Recognizing And Localizing Human Actions
on HAR
Automatic Sleep Stage Classification
Contrastive Learning
+8
Feasibility Study on Intra-Grid Location Estimation Using Power ENF Signals
In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces.
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.
Ranked #1 on
Automatic Sleep Stage Classification
on Sleep-EDF
Automatic Sleep Stage Classification
Electroencephalogram (EEG)
+1
Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray
In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations.
Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis
Energy landscape analysis is a method that can be used to quantify these dynamics.
FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data
Despite the faster HECNN inference, the mainstream packing schemes Dense Packing (DensePack) and Convolution Packing (ConvPack) introduce expensive rotation overhead, which prolongs the inference latency of HECNN for deeper and wider CNN architectures.
Cross-domain Joint Dictionary Learning for ECG Inference from PPG
The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring.
Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization
This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification.
Inferring ECG from PPG for Continuous Cardiac Monitoring Using Lightweight Neural Network
It is also infeasible to expect persistently active user participation for long-term continuous cardiac monitoring in order to capture these and other types of abnormalities of the heart.
Assessing Robustness of Text Classification through Maximal Safe Radius Computation
Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction.
Attention Sequence to Sequence Model for Machine Remaining Useful Life Prediction
Accurate estimation of remaining useful life (RUL) of industrial equipment can enable advanced maintenance schedules, increase equipment availability and reduce operational costs.
Recent Advances in Network-based Methods for Disease Gene Prediction
Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases.
Transparency Tools for Fairness in AI (Luskin)
The three tools are: - A new definition of fairness called "controlled fairness" with respect to choices of protected features and filters.
Towards Threshold Invariant Fair Classification
This paper introduces the notion of threshold invariant fairness, which enforces equitable performances across different groups independent of the decision threshold.
Multi-View Collaborative Network Embedding
Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes.
Adaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications
In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals.
Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation.
Respiratory Rate Estimation from Face Videos
Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos.
Robustness Guarantees for Deep Neural Networks on Videos
The widespread adoption of deep learning models places demands on their robustness.
A Deep Framework for Bone Age Assessment based on Finger Joint Localization
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation
Unlike most existing approaches which employ a generator to deal with domain difference, MMEN focuses on learning the categorical information from unlabeled target samples with the help of labeled source samples.
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks.
SL$^2$MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization
Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations.
Concolic Testing for Deep Neural Networks
Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program.
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm
In this paper we focus on the $L_0$ norm and aim to compute, for a trained DNN and an input, the maximal radius of a safe norm ball around the input within which there are no adversarial examples.
Adaptive Cost-sensitive Online Classification
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost.
Safety Verification of Deep Neural Networks
Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations.
Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events.
Sampling Requirements for Stable Autoregressive Estimation
Assuming that the parameters are compressible, we analyze the performance of the $\ell_1$-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime.
Robust Estimation of Self-Exciting Generalized Linear Models with Application to Neuronal Modeling
We consider the problem of estimating self-exciting generalized linear models from limited binary observations, where the history of the process serves as the covariate.
Multi-label ensemble based on variable pairwise constraint projection
For the boosting-like strategy, we employ both the variable pairwise constraints and the bootstrap steps to diversify the base classifiers.
Sparse Norm Filtering
Optimization-based filtering smoothes an image by minimizing a fidelity function and simultaneously preserves edges by exploiting a sparse norm penalty over gradients.
