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Found 69 papers, 27 papers with code
Sparse Data Generation Using Diffusion Models
Sparse data is ubiquitous, appearing in numerous domains, from economics and recommender systems to astronomy and biomedical sciences.
Multi-level Supervised Contrastive Learning
Contrastive learning is a well-established paradigm in representation learning.
Challenging Assumptions in Learning Generic Text Style Embeddings
Recent advancements in language representation learning primarily emphasize language modeling for deriving meaningful representations, often neglecting style-specific considerations.
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced their performance across various graph learning tasks.
SetPINNs: Set-based Physics-informed Neural Networks
Physics-Informed Neural Networks (PINNs) solve partial differential equations using deep learning.
Comgra: A Tool for Analyzing and Debugging Neural Networks
Neural Networks are notoriously difficult to inspect.
Anomaly Detection of Tabular Data Using LLMs
Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning.
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
Without any specific training for the diseases, our best-performing model reliably detected a broad spectrum of infrequent ("anomalous") pathologies with 95. 0% (stomach) and 91. 0% (colon) AUROC and generalized across scanners and hospitals.
Interpretable Tensor Fusion
Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type.
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
Reimagining Anomalies: What If Anomalies Were Normal?
Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous.
Labeling Neural Representations with Inverse Recognition
Deep Neural Networks (DNNs) demonstrate remarkable capabilities in learning complex hierarchical data representations, but the nature of these representations remains largely unknown.
Evaluating Dynamic Topic Models
There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs).
Text Style Transfer Evaluation Using Large Language Models
This suggests that LLMs could be a feasible alternative to human evaluation and other automated metrics in TST evaluation.
A Call for Standardization and Validation of Text Style Transfer Evaluation
Text Style Transfer (TST) evaluation is, in practice, inconsistent.
Deep Anomaly Detection on Tennessee Eastman Process Data
Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.
Deep Anomaly Detection under Labeling Budget Constraints
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection.
Zero-Shot Anomaly Detection via Batch Normalization
Anomaly detection (AD) plays a crucial role in many safety-critical application domains.
Ranked #1 on
Unsupervised Anomaly Detection
on AnoShift
Unsupervised Anomaly Detection
zero-shot anomaly detection
+1
Ordinal Regression for Difficulty Estimation of StepMania Levels
As is common in popular games, there is a large number of community-designed levels.
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings
We study inductive matrix completion (matrix completion with side information) under an i. i. d.
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings
As One Hot encoding of high cardinal dataset invokes the "curse of dimensionality", we experiment with GEL encoding and embedding layer for representing categorical attributes.
Ranked #1 on
Anomaly Detection
on Vehicle Claims
Training Normalizing Flows from Dependent Data
Normalizing flows are powerful non-parametric statistical models that function as a hybrid between density estimators and generative models.
Raising the Bar in Graph-level Anomaly Detection
Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks.
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images
We find that standard classifiers and semi-supervised one-class methods trained to discern between normal samples and relatively few random natural images are able to outperform the current state of the art on an established AD benchmark with ImageNet.
Ranked #1 on
Anomaly Detection
on One-class CIFAR-10
(using extra training data)
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
We propose a strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models.
Detecting Anomalies within Time Series using Local Neural Transformations
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.
A systematic approach to random data augmentation on graph neural networks
On the theoretical side, among other results, we formally prove that under natural conditions all instantiations of our framework are universal.
Fine-grained Generalization Analysis of Inductive Matrix Completion
In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in \textit{inductive matrix completion}: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of $O(rd^2)$ to $\widetilde{O}(d^{3/2}\sqrt{r})$, where $d$ is the dimension of the side information and $r$ is the rank.
Learning Interpretable Concept Groups in CNNs
We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept groups, each of which is trained to learn a single visual concept.
Explainability Requires Interactivity
When explaining the decisions of deep neural networks, simple stories are tempting but dangerous.
Explaining Bayesian Neural Networks
Bayesian approaches such as Bayesian Neural Networks (BNNs) so far have a limited form of transparency (model transparency) already built-in through their prior weight distribution, but notably, they lack explanations of their predictions for given instances.
Fine-grained Generalization Analysis of Structured Output Prediction
Existing generalization analysis implies generalization bounds with at least a square-root dependency on the cardinality $d$ of the label set, which can be vacuous in practice.
Fine-grained Generalization Analysis of Vector-valued Learning
In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size.
Extreme Multi-Label Classification
General Classification
+3
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Data transformations (e. g. rotations, reflections, and cropping) play an important role in self-supervised learning.
Sharper Generalization Bounds for Pairwise Learning
Pairwise learning refers to learning tasks with loss functions depending on a pair of training examples, which includes ranking and metric learning as specific examples.
A Unifying Review of Deep and Shallow Anomaly Detection
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.
Input Hessian Regularization of Neural Networks
Regularizing the input gradient has shown to be effective in promoting the robustness of neural networks.
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events
To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE).
Ranked #19 on
Anomaly Detection
on CUHK Avenue
Explainable Deep One-Class Classification
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away.
Ranked #5 on
Anomaly Detection
on One-class ImageNet-30
(using extra training data)
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks
Explainable AI (XAI) aims to provide interpretations for predictions made by learning machines, such as deep neural networks, in order to make the machines more transparent for the user and furthermore trustworthy also for applications in e. g. safety-critical areas.
Rethinking Assumptions in Deep Anomaly Detection
Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous."
Orthogonal Inductive Matrix Completion
We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization.
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes.
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification
However, deep SVDD suffers from hypersphere collapse -- also known as mode collapse, if the architecture of the model does not comply with certain architectural constraints, e. g. the removal of bias terms.
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
Despite the wide success of deep neural networks (DNN), little progress has been made on end-to-end unsupervised outlier detection (UOD) from high dimensional data like raw images.
Two-sample Testing Using Deep Learning
We propose a two-sample testing procedure based on learned deep neural network representations.
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
We study the variance of the REINFORCE policy gradient estimator in environments with continuous state and action spaces, linear dynamics, quadratic cost, and Gaussian noise.
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text
There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words.
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
Norm-based generalisation bounds for multi-class convolutional neural networks
This holds even when formulating the bounds in terms of the $L^2$-norm of the weight matrices, where previous bounds exhibit at least a square-root dependence on the number of classes.
Deep One-Class Classification
Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.
Ranked #34 on
Anomaly Detection
on One-class CIFAR-10
Scalable Generalized Dynamic Topic Models
First, we extend the class of tractable priors from Wiener processes to the generic class of Gaussian processes (GPs).
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
We propose a scalable stochastic variational approach to GP classification building on Polya-Gamma data augmentation and inducing points.
Anomaly Detection with Generative Adversarial Networks
Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.
Bayesian Nonlinear Support Vector Machines for Big Data
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points.
Data-dependent Generalization Bounds for Multi-class Classification
In this paper, we study data-dependent generalization error bounds exhibiting a mild dependency on the number of classes, making them suitable for multi-class learning with a large number of label classes.
Distributed Optimization of Multi-Class SVMs
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way.
Feature Importance Measure for Non-linear Learning Algorithms
MFI is general and can be applied to any arbitrary learning machine (including kernel machines and deep learning).
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning
We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), using which we establish sharp excess risk bounds for MTL in terms of distribution- and data-dependent versions of the Local Rademacher Complexity (LRC).
Sparse Probit Linear Mixed Model
Formulated as models for linear regression, LMMs have been restricted to continuous phenotypes.
Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis
We present a general regularization-based framework for Multi-task learning (MTL), in which the similarity between tasks can be learned or refined using $\ell_p$-norm Multiple Kernel learning (MKL).
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
This paper studies the generalization performance of multi-class classification algorithms, for which we obtain, for the first time, a data-dependent generalization error bound with a logarithmic dependence on the class size, substantially improving the state-of-the-art linear dependence in the existing data-dependent generalization analysis.
Localized Multiple Kernel Learning---A Convex Approach
We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure.
Probabilistic Clustering of Time-Evolving Distance Data
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points.
Localized Complexities for Transductive Learning
We show two novel concentration inequalities for suprema of empirical processes when sampling without replacement, which both take the variance of the functions into account.
Learning Kernels Using Local Rademacher Complexity
We use the notion of local Rademacher complexity to design new algorithms for learning kernels.
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning
We derive an upper bound on the local Rademacher complexity of Lp-norm multiple kernel learning, which yields a tighter excess risk bound than global approaches.
Efficient and Accurate Lp-Norm Multiple Kernel Learning
Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations and hence support interpretability.
