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Found 75 papers, 25 papers with code
Uncertainty Awareness in Wireless Communications, Sensing, and Learning
Wireless communications and sensing (WCS) establish the backbone of modern information exchange and environment perception.
Unsupervised Anomaly Detection for Tabular Data Using Noise Evaluation
The neural network can learn a reliable decision boundary between normal data and anomalous data when the diversity of the generated noisy data is sufficiently high so that the hard abnormal samples lie in the noisy region.
Robust Beamforming with Application in High-Resolution Sensing
As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest while mitigating interference and noise.
EEG-DIF: Early Warning of Epileptic Seizures through Generative Diffusion Model-based Multi-channel EEG Signals Forecasting
Furthermore, the early warning accuracy for epilepsy seizures based on the generated EEG data reaches 0. 89.
Advancing Academic Knowledge Retrieval via LLM-enhanced Representation Similarity Fusion
In an era marked by robust technological growth and swift information renewal, furnishing researchers and the populace with top-tier, avant-garde academic insights spanning various domains has become an urgent necessity.
SoccerNet 2024 Challenges Results
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
State-dependent Filtering of the Ring Model
In this paper, we probe how orientation-selective neurons organized on a 1-D ring network respond to perturbations in the hope of gaining some insights on the robustness of visual system in brain.
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment.
Stationary and Sparse Denoising Approach for Corticomuscular Causality Estimation
Objective: Cortico-muscular communication patterns are instrumental in understanding movement control.
Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning
To gain insights into the neural unit dynamics, we theoretically analyze the model's convergence property via a universal approximation theorem on learning sequential mappings, which is under-explored in the CIL community.
A Low-rank Projected Proximal Gradient Method for Spectral Compressed Sensing
This paper presents a new approach to the recovery of a spectrally sparse signal (SSS) from partially observed entries, focusing on challenges posed by large-scale data and heavy noise environments.
Distributionally Robust Receive Beamforming
This article investigates signal estimation in wireless transmission (i. e., receive beamforming) from the perspective of statistical machine learning, where the transmit signals may be from an integrated sensing and communication system; that is, 1) signals may be not only discrete constellation points but also arbitrary complex values; 2) signals may be spatially correlated.
LightGCNet: A Lightweight Geometric Constructive Neural Network for Data-Driven Soft sensors
Herein, guided by spatial geometric, a lightweight geometric constructive neural network, namely LightGCNet, is proposed, which utilizes compact angle constraint to assign the hidden parameters from dynamic intervals.
ASI: Accuracy-Stability Index for Evaluating Deep Learning Models
In the context of deep learning research, where model introductions continually occur, the need for effective and efficient evaluation remains paramount.
Robust Waveform Design for Integrated Sensing and Communication
Simulation results show that by solving the robust waveform design problems, the lower bound of the true but unknown Pareto frontier, which characterizes the sensing-communication performance trade-off under communication channel uncertainty, can be obtained.
Multi-scale MRI reconstruction via dilated ensemble networks
As aliasing artefacts are highly structural and non-local, many MRI reconstruction networks use pooling to enlarge filter coverage and incorporate global context.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation
Given the prevalence of rolling bearing fault diagnosis as a practical issue across various working conditions, the limited availability of samples compounds the challenge.
DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations
In e-commerce platforms, the relevant recommendation is a unique scenario providing related items for a trigger item that users are interested in.
Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks
Concretely, a cloud feature extraction method is first developed by using a backward cloud generator of normal cloud model to mine the uncertainty of fault information.
Interpretable Neural Networks with Random Constructive Algorithm
This paper introduces an Interpretable Neural Network (INN) incorporating spatial information to tackle the opaque parameterization process of random weighted neural networks.
A Mask Free Neural Network for Monaural Speech Enhancement
In speech enhancement, the lack of clear structural characteristics in the target speech phase requires the use of conservative and cumbersome network frameworks.
Transformer-Based Hierarchical Clustering for Brain Network Analysis
Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions.
Explainable Semantic Medical Image Segmentation with Style
The discriminator is generalised to small domain shifts as much as permissible by the training data, and the generator automatically diversifies the training samples using a manifold of input features learnt during segmentation.
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
To achieve this, prior attacks explicitly inject the insecure code payload into the training data, making the poison data detectable by static analysis tools that can remove such malicious data from the training set.
Planting and Mitigating Memorized Content in Predictive-Text Language Models
Language models are widely deployed to provide automatic text completion services in user products.
Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets
After quantitatively analyzing experimental results, we report the limitations of the two IQAs with these noised CIFAR-10 and MNIST image sets.
Brain Network Transformer
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders.
Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume
The low representation ability of FC leads to poor performance in clinical practice, especially when dealing with multimodal medical data involving multiple types of visual signals and textual records for brain diseases.
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild
To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions.
Ranked #12 on
Dynamic Facial Expression Recognition
on MAFW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Combing for Credentials: Active Pattern Extraction from Smart Reply
We show experimentally that it is possible for an adversary to extract sensitive user information present in the training data, even in realistic settings where all interactions with the model must go through a front-end that limits the types of queries.
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.
Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics
As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning.
Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning
In this study, a framework for power consumption modeling of eVTOL aircraft was established.
Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention
Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment.
Digging into Primary Financial Market: Challenges and Opportunities of Adopting Blockchain
Since the emergence of blockchain technology, its application in the financial market has always been an area of focus and exploration by all parties.
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
A New Learning Paradigm for Stochastic Configuration Network: SCN+
This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN.
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
We created comprehensive 69 benchmarking image sets, including a clean set, sets with single factor perturbations, and sets with two-factor perturbation conditions.
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.
CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation
Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of individual objects with complex geometries within a large volume under investigation.
Conflict-Free Four-Dimensional Path Planning for Urban Air Mobility Considering Airspace Occupations
This paper aims at tackling conflict-free path planning problem for UAM operation with a consideration of four-dimensional airspace management.
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.
Price change prediction of ultra high frequency financial data based on temporal convolutional network
Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper.
Deep Kernel Gaussian Process Based Financial Market Predictions
However, the deep kernel Gaussian Process has not been applied to forecast the conditional returns and volatility in financial market to the best of our knowledge.
VMAF And Variants: Towards A Unified VQA
We investigate variants of the popular VMAF video quality assessment algorithm for the FR case, using both support vector regression and feedforward neural networks.
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation
Also, we introduce a new four-quadrant statistical visualization tool, including minimum accuracy, maximum accuracy, mean accuracy, and coefficient of variation, for benchmarking robustness of DL classifiers.
Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism
In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.
Where is the Model Looking At?--Concentrate and Explain the Network Attention
We generate attribute-based textual explanations for the network and ground the attributes on the image to show visual explanations.
Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation
However, medical image datasets have diverse-sized images and features, and developing a model simultaneously for multiple datasets is challenging.
Learning Optimal Tree Models Under Beam Search
Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.
EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation
We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.
HEAX: An Architecture for Computing on Encrypted Data
Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data.
Charge-Based Prison Term Prediction with Deep Gating Network
Judgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction.
Benchmarking Contemporary Deep Learning Hardware and Frameworks:A Survey of Qualitative Metrics
This paper surveys benchmarking principles, machine learning devices including GPUs, FPGAs, and ASICs, and deep learning software frameworks.
Dictionary Learning with BLOTLESS Update
BLOTLESS updates a block of dictionary elements and the corresponding sparse coefficients simultaneously.
Improving Data Quality through Deep Learning and Statistical Models
Traditional data quality control methods are based on users experience or previously established business rules, and this limits performance in addition to being a very time consuming process with lower than desirable accuracy.
Toward Understanding the Impact of Staleness in Distributed Machine Learning
Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates.
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest.
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent.
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
Recent deep learning (DL) models have moved beyond static network architectures to dynamic ones, handling data where the network structure changes every example, such as sequences of variable lengths, trees, and graphs.
Dual Motion GAN for Future-Flow Embedded Video Prediction
To make both synthesized future frames and flows indistinguishable from reality, a dual adversarial training method is proposed to ensure that the future-flow prediction is able to help infer realistic future-frames, while the future-frame prediction in turn leads to realistic optical flows.
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
We show that Poseidon enables Caffe and TensorFlow to achieve 15. 5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification.
SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays
Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes.
A Comparison of deep learning methods for environmental sound
On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector.
Learning Filter Banks Using Deep Learning For Acoustic Signals
Based on the procedure of log Mel-filter banks, we design a filter bank learning layer.
Very Deep Convolutional Neural Networks for Raw Waveforms
Our CNNs, with up to 34 weight layers, are efficient to optimize over very long sequences (e. g., vector of size 32000), necessary for processing acoustic waveforms.
Strategies and Principles of Distributed Machine Learning on Big Data
Taking the view that Big ML systems can benefit greatly from ML-rooted statistical and algorithmic insights --- and that ML researchers should therefore not shy away from such systems design --- we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions.
LightLDA: Big Topic Models on Modest Compute Clusters
When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers.
High-Performance Distributed ML at Scale through Parameter Server Consistency Models
As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands.
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
We develop parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework.
Petuum: A New Platform for Distributed Machine Learning on Big Data
What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)?
Consistent Bounded-Asynchronous Parameter Servers for Distributed ML
Many ML algorithms fall into the category of \emph{iterative convergent algorithms} which start from a randomly chosen initial point and converge to optima by repeating iteratively a set of procedures.
