Paper proposes CNN with SIFT for rotation invariant feature extraction.
problem Max-pooling layer discards rotational information, leading to rotation invariance issues.
method Uses SIFT descriptor to capture orientation and spatial relationships.
result Improves feature extraction on MNIST and fashionMNIST datasets.
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.
SIFT reduces training time by selecting samples with approximate losses.
problem Reducing training time by selecting samples with large approximate losses.
method Developed SIFT which uses early exiting to obtain approximate losses with intermediate layer representations for sample selection.
result SIFT achieves significant gains in training time and number of backpropagation steps without optimized implementation.
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a d…
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.
Paper generates synthetic radar signatures for motion classification.
problem Lack of large training datasets for radar-based human activity recognition.
method Adversarial learning for synthetic data generation, kinematic sifting for consistency.
result 93% overall accuracy achieved on diverse aspect angles.
In this paper, a new descriptor selection method for selecting an optimal combination of important descriptors of sulfonamide derivatives data, named self tuned reweighted sampling (STRS), is developed. descriptors are defined as the descriptors with large absolute coefficients in a multivariate linear regression model…
Optimizes atomic descriptors to reduce redundancy and improve machine learning models.
problem Redundant descriptors in atomistic machine learning models increase computational burden and limit model expressivity.
method Employing techniques from pattern recognition, we refine and augment existing atomistic representations to produce optimal sets of descriptors.
result New architectures recognize up to 5-body patterns with low computational cost and high accuracy.
This work characterizes topological descriptors of graph products and their expressive power.
problem Capturing multiscale structural information in graph products using topological descriptors.
method Analysis of various filtrations on graph products, including Euler characteristic and persistent homology.
result Persistent homology of graph products contains more information than individual graphs.
Paper proposes detecting video manipulation using stream descriptors.
problem Misuse of manipulated video content.
method Binary classifiers on multimedia stream descriptors.
result Scalable approach can detect high-quality manipulations.
Informative and discriminative feature descriptors play a fundamental role in deformable shape analysis. For example, they have been successfully employed in correspondence, registration, and retrieval tasks. In the recent years, significant attention has been devoted to descriptors obtained from the spectral decomposi…
Although the recent progress in the deep neural network has led to the development of learnable local feature descriptors, there is no explicit answer for estimation of the necessary size of a neural network. Specifically, the local feature is represented in a low dimensional space, so the neural network should have mo…
ALP outperforms other data descriptors in one-class classification.
problem Challenges in one-class classification using data descriptors.
method Determined optimal default hyperparameters for data descriptors, proposed ALP, evaluated using leave-one-dataset-out procedure.
result ALP outperforms other data descriptors, including IF and SVM.
In order to study the application of artificial intelligence (AI) to dental imaging, we applied AI technology to classify a set of panoramic radiographs using (a) a convolutional neural network (CNN) which is a form of an artificial neural network (ANN), (b) representative image cognition algorithms that implement scal…
We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each …
Wittgenstein's Rule Following evolves datasets by extrapolating structural descriptors.
problem Generating meaningful continuations of evolving datasets.
method Wittgenstein's Rule Following (WRF) uses structural descriptors to extrapolate trajectories and average historical descriptors.
result WRF can generate meaningful continuations of evolving datasets.
Quantitative structure-activity relationship (QSAR) modelling is effective 'bridge' to search the reliable relationship related bioactivity to molecular structure. A QSAR classification model contains a lager number of redundant, noisy and irrelevant descriptors. To address this problem, various of methods have been pr…
Support Vector Machines (SVMs) are powerful learners that have led to state-of-the-art results in various computer vision problems. SVMs suffer from various drawbacks in terms of selecting the right kernel, which depends on the image descriptors, as well as computational and memory efficiency. This paper introduces a n…
Novel method uses image descriptors to harmonize MRI brain volumes across centers.
problem Inconsistencies in MRI brain volume measurements across different centers and scanners.
method Trained a Relevance Vector Machine (RVM) model using image descriptors to harmonize brain volumes.
result Decreases scanner and center variability while preserving measurements for longitudinal studies.
PolyGraph Discrepancy improves graph generative model evaluation.
problem Inability of existing metrics to provide an absolute performance measure and comparability across different graph descriptors.
method Approximates Jensen-Shannon distance using binary classifiers trained to distinguish between real and generated graphs.
result PGD provides a more robust and insightful evaluation compared to MMD metrics.
Counting and classifying blood cells is an important diagnostic tool in medicine. Support Vector Machines are increasingly popular and efficient and could replace artificial neural network systems. Here a method to classify blood cells is proposed using SVM. A set of statistics on images are implemented in C++. The MPE…
Method separates target signal properties from noisy mixtures.
problem Signal recovery from noisy mixtures with specific statistical properties.
method Statistical component separation method using noise samples and matching statistics.
result Method outperforms standard denoising methods in recovering target signal properties.
Combines curvature descriptors with TDA for graph model evaluation.
problem Evaluating graph generative models efficiently and accurately.
method Combines graph curvature descriptors with topological data analysis.
result Robust, expressive descriptors for graph generative models.
Paper introduces stable vectorization for multiparameter PH using signed barcodes.
problem Lack of stable vectorization methods for multiparameter persistent homology.
method Signed barcodes as measures for stable vectorization of MPH.
result Stable feature vectors from signed barcodes improve performance in data science.
A hybrid K-NN and SVM technique improves classification accuracy.
problem Improving classification accuracy in pattern recognition.
method Discriminative nearest neighbour classification combined with SVM.
result The hybrid technique outperforms state-of-the-art methods.
Persistent homology provides a new, efficient molecular descriptor for protein dynamics.
problem Designing effective molecular descriptors for high-dimensional MD trajectories.
method Introduced masked Flood complex, a protein-tailored modification of simplicial complexes, for persistent homology.
result Persistent homology-based descriptors are competitive across protein dynamics tasks, including frame-level observable regression and MSM estimation.
DRAGON improves learning for rare classes in unbalanced datasets using class descriptions.
problem Learning rare classes in unbalanced datasets with deep models.
method DRAGON is a late-fusion architecture that corrects bias towards frequent classes and fuses class-descriptions to improve tail-class accuracy.
result DRAGON outperforms state-of-the-art models on new benchmarks for long-tail learning with class descriptors.
The article uses PageRank and persistent homology for scalable graph comparison.
problem Comparing the similarities between complex networks.
method Combines PageRank and persistent homology to compute a scalable graph descriptor.
result Shows the effectiveness of the method on shape mesh datasets.
Covariance and histogram image descriptors provide an effective way to capture information about images. Both excel when used in combination with special purpose distance metrics. For covariance descriptors these metrics measure the distance along the non-Euclidean Riemannian manifold of symmetric positive definite mat…
Framework for interpreting ML models to reveal properties of real-world phenomena.
problem Lack of direct interpretability in modern ML models hinders scientific understanding.
method Developed 'property descriptors' grounded in statistical learning theory.
result Property descriptors can reveal relevant properties of joint probability distributions.
Tiled Squeeze-and-Excite improves channel attention with local spatial context.
problem Improving channel attention mechanisms in neural networks.
method Proposes tiled squeeze-and-excite (TSE) framework for channel attention.
result Local context of 7 rows or columns is sufficient for matching global context performance.
Cloak identifies essential features to preserve prediction privacy without provider collaboration.
problem Discovering the subset of features necessary for a prediction task.
method Gradient-based perturbation maximization method to identify essential features, followed by suppression of the rest using utility-preserving constant values.
result Cloak reduces mutual information between input and sifted representations by 85.01% with negligible utility loss.
Time series forecasting models fail to consistently select the best model across different datasets.
problem Inconsistency in model selection for time series forecasting across varying data regimes.
method Characterized time series using descriptors like trend strength, seasonality, noise level, and temporal dependence. Developed a rule-based selection mechanism to map data regimes to candidate models.
result Rule-based model selection achieves low accuracy, with correct model identification occurring in only a small fraction of cases.
Method embeds numeric tabular datasets into a shared vector space for similarity and retrieval.
problem Lack of meaningful representation for numeric tabular datasets in large language models.
method Structured exploratory data analysis descriptors, sentence transformer embedding, CCA for cross-dataset alignment.
result Total P@1 score of 0.9 across 15 datasets, robust nearest-neighbor retrieval and cluster structure.
A new method learns from multi-modal sequences with external memory.
problem Learning new modes in a dynamic environment without prior knowledge.
method Maintains a neural episodic memory with a Dirichlet Process prior to store mode descriptors and transfers knowledge through retrieval.
result Performs continual learning favorably compared to mainstream approaches.
New DNN boosts image retrieval efficiency.
problem Efficient compression of visual descriptors for large-scale image retrieval.
method Unsupervised multi-codebook quantization in a DNN architecture.
result Significantly outperforms existing methods on visual descriptor datasets.
In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different w…
Graph neural networks outperform fixed molecular descriptors in property prediction.
problem Comparing graph neural networks to fixed molecular descriptors for property prediction.
method Benchmarked graph convolutional neural networks on public and proprietary datasets.
result Graph convolutional model consistently matches or outperforms existing models on both public and proprietary datasets.
New method uses geometric moments for accurate machine learning potentials.
problem Creating high-dimensional potential energy surfaces efficiently.
method Feed-forward neural networks with invariant local molecular descriptors based on geometric moments.
result Accuracy comparable to established models, high efficiency.
Survey on optimizing topological descriptors for machine learning.
problem Optimizing topological priors in machine learning models.
method Minimizing topologically-informed losses using gradient descent.
result Various techniques enable optimization of persistence-based loss functions.
We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the un…
Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amount of unlabeled data, unsupervised feature learning methods are utilized to construct high-level representations that are discriminative en…
Proposes a multi-view architecture for drug-target interaction prediction.
problem Representing compound-target pairs in deep learning models.
method Integrates differentiable and predefined molecular descriptors using an adversarial multi-view architecture.
result Demonstrates potential of the proposed approach on clinically relevant datasets.
A new method for cross-matching fingerprints from different sensors.
problem Inefficiency in cross-matching fingerprints from different sensors.
method Co-occurrence of ridge orientations and Gabor-HoG descriptor fusion with CCA.
result Significant enhancement over state-of-the-art methods.
This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the ene…
Proposes a new model to predict polymer properties by integrating various data types.
problem Inaccurate polymer property prediction due to separate modeling of different data types.
method Multi-modal cascade feature transfer using GCN for chemical structure and molecular descriptors.
result Empirically evaluated model shows higher predictive performance than single-feature approaches.
Training examples are not all equally informative. Active learning strategies leverage this observation in order to massively reduce the number of examples that need to be labeled. We leverage the same observation to build a generic strategy for parallelizing learning algorithms. This strategy is effective because the …
Enhances graph neural networks with spectral and topological information.
problem Improving graph neural networks' expressivity beyond Weisfeiler-Leman hierarchy.
method Integrates spectral information into Persistent Homology diagrams.
result SpectRe is strictly more expressive than PH and spectral information alone.