Search Results for author:
Found 12 papers, 5 papers with code
An efficient, provably exact, practical algorithm for the 0-1 loss linear classification problem
We demonstrate the effectiveness of this algorithm on synthetic and real-world datasets, showing optimal accuracy both in and out-of-sample, in practical computational time.
CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation
The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation.
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection
Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection.
Dynamic programming by polymorphic semiring algebraic shortcut fusion
Dynamic programming (DP) is an algorithmic design paradigm for the efficient, exact solution of otherwise intractable, combinatorial problems.
Kamino: Constraint-Aware Differentially Private Data Synthesis
Existing differentially private data synthesis methods aim to generate useful data based on applications, but they fail in keeping one of the most fundamental data properties of the structured data -- the underlying correlations and dependencies among tuples and attributes (i. e., the structure of the data).
Databases Cryptography and Security
Electron pairing in the pseudogap state revealed by shot noise in copper-oxide junctions
In the quest to understand high-temperature superconductivity in copper oxides, a vigorous debate has been focused on the pseudogap - a partial gap that opens over portions of the Fermi surface in the 'normal' state above the bulk critical temperature ($T_{c}$).
Superconductivity Mesoscale and Nanoscale Physics Strongly Correlated Electrons
Quantum correlation alignment for unsupervised domain adaptation
Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels.
Quantum subspace alignment for domain adaptation
The classifier trained on the aligned labelled data set can be transferred to the unlabelled data set to predict the target labels.
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy
In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE) method in which sample size is gradually increasing to quickly obtain a solution whose empirical loss is under satisfactory statistical accuracy.
APEx: Accuracy-Aware Differentially Private Data Exploration
Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets.
Databases
Distributed Hessian-Free Optimization for Deep Neural Network
Training deep neural network is a high dimensional and a highly non-convex optimization problem.
Dual Free Adaptive Mini-batch SDCA for Empirical Risk Minimization
This work is motivated by recent work of Shai Shalev-Shwartz on dual free SDCA method, however, we allow a non-uniform selection of "dual" coordinates in SDCA.
