Search Results for author:
Found 43 papers, 11 papers with code
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.
HierAttn: Effectively Learn Representations from Stage Attention and Branch Attention for Skin Lesions Diagnosis
Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin conditions and disorders.
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.
Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis
In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner.
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.
