SURF steers scalarization weights to uniformly traverse the Pareto front.
problem Non-uniform coverage of the Pareto front when using scalarization weights.
method Geometric analysis and CDF mapping to select weights for uniform coverage.
result SURF converges to uniform Pareto front coverage under provable conditions.
SURF simplifies distribution estimation with simple, robust, and fast algorithms.
problem Efficient and accurate distribution estimation in statistics and machine learning.
method Piecewise polynomial approximation using empirical probability interpolation and divide-and-conquer merging.
result Surpassing state-of-the-art algorithms in efficiency and accuracy, SURF estimates distributions robustly and quickly.
A method to optimize deep networks by sequentially minimizing risk functions.
problem Optimizing deep networks during training to find global optima.
method Surfing: Iterative optimization over incrementally trained deep networks.
result The method can find global optima and improve compressed sensing performance.
Paper proposes a robust audio classification method against adversarial attacks.
problem Adversarial attacks can fool machine learning models into making incorrect predictions.
method Proposes a novel SVM-based approach using DWT and SURF features.
result The proposed method provides a good balance between accuracy and resilience against adversarial attacks.
DeepBark improves tree bark re-identification accuracy.
problem Challenging illuminations make tree bark hard to re-identify.
method Used a large dataset of 2,400 bark images to train DeepBark and SqueezeBark.
result DeepBark achieves 87.2% mAP in retrieving relevant bark images.
The paper matches features in images using centro-affine invariants and heat flow.
problem Feature matching in images with invariant algorithms.
method Developed an invariant algorithm using centro-affine invariants and heat flow.
result The algorithm compares favorably with existing feature matching methods.
We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From …
Introduces NCDawareRank, a new ranking framework for networks.
problem The overlooked teleportation component in Random Surfer model.
method Exploits network meta-information and higher-order structural organization.
result NCDawareRank preserves PageRank's mathematical structure and computational characteristics.
Gradient-flow helps find good minima in complex models.
problem Understanding why gradient-based algorithms work in non-convex optimization.
method Kac-Rice analysis and gradient-flow from statistical physics.
result Gradient-flow finds good global minima in the presence of many spurious local minima.
Episodes of market crashes have fascinated economists for centuries. Although many academics, practitioners and policy makers have studied questions related to collapsing asset price bubbles, there is little consensus yet about their causes and effects. This review and essay evaluates some of the hypotheses offered to …
Paper proposes a novel unsupervised feature learning approach for environmental sound classification.
problem Classifying environmental sounds without labeled data.
method Cycle-consistent GAN for high-level data augmentation, unsupervised feature learning, codebook construction.
result Improves classification rate by 3.51% to 14.34% compared to state-of-the-art classifiers.
ChebLieNet uses Lie groups to create invariant spectral graph networks.
problem Handling anisotropic data in graph neural networks.
method Develops anisotropic convolutional layers on Lie groups with Riemannian metrics.
result Demonstrates the effectiveness of balancing equivariance and invariance.
Deep learning improves nearshore bathymetry estimation from sparse data.
problem Estimating nearshore bathymetry from limited and often sparse data.
method Deep Neural Network (DNN) and Conditional Generative Adversarial Network (cGAN) for posterior estimates; Kriging for comparison.
result DNN-based methods outperform traditional Kriging in predicting nearshore bathymetry with sharp gradients.
New mapping class group actions on Hochschild complexes for modular categories.
problem Understanding actions of mapping class groups on Hochschild complexes of modular categories.
method Construction of a symmetric monoidal functor with excision property.
result Homotopy coherent projective action of mapping class groups on Hochschild complexes.