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Found 22 papers, 6 papers with code
Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference
Moreover, we designed a Multiscale Sigmoid Inference (MSI) module as a post processing step to further refine the change probability map from the trained student network.
Distilling Adversarial Robustness Using Heterogeneous Teachers
Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e. g., self-driving cars or medical imaging.
Heterogeneous Graph Sparsification for Efficient Representation Learning
Graph sparsification is a powerful tool to approximate an arbitrary graph and has been used in machine learning over homogeneous graphs.
Auto-Encoding Goodness of Fit
Goodness of fit (GoF) hypothesis tests provide a measure of statistical indistinguishability between the latent distribution and a target distribution class.
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm.
Identifying Interactions among Categorical Predictors with Monte-Carlo Tree Search
Identifying interpretable interactions among categorical predictors for predictive modeling is crucial in various research fields.
An Efficient Algorithm for Deep Stochastic Contextual Bandits
In this work, we formulate the SCB that uses a DNN reward function as a non-convex stochastic optimization problem, and design a stage-wise stochastic gradient descent algorithm to optimize the problem and determine the action policy.
Escaping Saddle Points with Stochastically Controlled Stochastic Gradient Methods
Stochastically controlled stochastic gradient (SCSG) methods have been proved to converge efficiently to first-order stationary points which, however, can be saddle points in nonconvex optimization.
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model.
Federated Nonconvex Sparse Learning
Nonconvex sparse learning plays an essential role in many areas, such as signal processing and deep network compression.
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data
Federated learning allows loads of edge computing devices to collaboratively learn a global model without data sharing.
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data.
Against Membership Inference Attack: Pruning is All You Need
The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularity, especially on mobile devices.
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning
In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee.
Calibrating the Adaptive Learning Rate to Improve Convergence of ADAM
Theoretically, we provide a new way to analyze the convergence of AGMs and prove that the convergence rate of \textsc{Adam} also depends on its hyper-parameter $\epsilon$, which has been overlooked previously.
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure.
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Edge Attention-based Multi-Relational Graph Convolutional Networks
The proposed GCN model, which we call edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution.
A Survey on Multi-View Clustering
With advances in information acquisition technologies, multi-view data become ubiquitous.
VIGAN: Missing View Imputation with Generative Adversarial Networks
Especially, when certain samples miss an entire view of data, it creates the missing view problem.
Classification of Neurological Gait Disorders Using Multi-task Feature Learning
An advanced machine learning method, multi-task feature learning (MTFL), is used to jointly train classification models of a subject's gait in three classes, post-stroke, PD and healthy gait.
A Sparse Interactive Model for Matrix Completion with Side Information
We prove that when the side features can span the latent feature space of the matrix to be recovered, the number of observed entries needed for an exact recovery is $O(\log N)$ where $N$ is the size of the matrix.
On Multiplicative Multitask Feature Learning
We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers.
