Search Results for author:
Found 154 papers, 66 papers with code
How simple can you go? An off-the-shelf transformer approach to molecular dynamics
While our model exhibits runaway energy increases on larger structures, we show approximately energy-conserving NVE simulations for a range of small structures.
Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise Ratio
A similar tendency is observed in image generation, where our approach with a uniform diffusion time grid performs comparably to the highly tailored EDM sampler.
Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications.
Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions.
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine.
Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks
Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data.
Aligning Machine and Human Visual Representations across Abstraction Levels
Deep neural networks have achieved success across a wide range of applications, including as models of human behavior in vision tasks.
Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
When modeling physical properties of molecules with machine learning, it is desirable to incorporate $SO(3)$-covariance.
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abstraction for explanations, often in the form of heatmaps highlighting single or multiple input features.
The Clever Hans Effect in Unsupervised Learning
Unsupervised learning has become an essential building block of AI systems.
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
To address these challenges, we introduce a robust machine learning and explainable AI (XAI) framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components.
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.
MambaLRP: Explaining Selective State Space Sequence Models
Recent sequence modeling approaches using selective state space sequence models, referred to as Mamba models, have seen a surge of interest.
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) -- a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian.
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognostication.
Molecular relaxation by reverse diffusion with time step prediction
As a remedy, we propose MoreRed, molecular relaxation by reverse diffusion, a conceptually novel and purely statistical approach where non-equilibrium structures are treated as noisy instances of their corresponding equilibrium states.
XpertAI: uncovering model strategies for sub-manifolds
In regression, explanations need to be precisely formulated to address specific user queries (e. g.\ distinguishing between `Why is the output above 0?'
Manipulating Feature Visualizations with Gradient Slingshots
Deep Neural Networks (DNNs) are capable of learning complex and versatile representations, however, the semantic nature of the learned concepts remains unknown.
RudolfV: A Foundation Model by Pathologists for Pathologists
Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research.
Getting aligned on representational alignment
These questions pertaining to the study of representational alignment are at the heart of some of the most promising research areas in contemporary cognitive science, neuroscience, and machine learning.
Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
An ML based analysis of these tables helps to unveil important facets of the spatio-temporal evolution of knowledge and innovation in the field of mathematical astronomy in the period, as taught at European universities.
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations.
Set Learning for Accurate and Calibrated Models
Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization.
An XAI framework for robust and transparent data-driven wind turbine power curve models
Alongside this paper, we publish a Python implementation of the presented framework and hope this can guide researchers and practitioners alike toward training, selecting and utilizing more transparent and robust data-driven wind turbine power curve models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected.
Mark My Words: Dangers of Watermarked Images in ImageNet
The utilization of pre-trained networks, especially those trained on ImageNet, has become a common practice in Computer Vision.
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
Explanations often take the form of a heatmap identifying input features (e. g. pixels) that are relevant to the model's decision.
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
Kernel machines have sustained continuous progress in the field of quantum chemistry.
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
To address shortcomings of this test, we start by observing an experimental gap in the ranking of explanation methods between randomization-based sanity checks [1] and model output faithfulness measures (e. g. [25]).
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive.
Self-Supervised Training with Autoencoders for Visual Anomaly Detection
We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold.
Diffeomorphic Counterfactuals with Generative Models
Counterfactuals can explain classification decisions of neural networks in a human interpretable way.
DORA: Exploring Outlier Representations in Deep Neural Networks
Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal representations.
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods.
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)
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes.
Automatic Identification of Chemical Moieties
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
XAI for Transformers: Better Explanations through Conservative Propagation
Transformers have become an important workhorse of machine learning, with numerous applications.
Ranked #6 on
Question Answering
on NewsQA
(using extra training data)
Explainable Artificial Intelligence (XAI)
Question Answering
Automated Dissipation Control for Turbulence Simulation with Shell Models
The application of machine learning (ML) techniques, especially neural networks, has seen tremendous success at processing images and language.
Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks
Molecular dynamics simulations are a cornerstone in science, allowing to investigate from the system's thermodynamics to analyse intricate molecular interactions.
Toward Explainable AI for Regression Models
In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks.
Scrutinizing XAI using linear ground-truth data with suppressor variables
It has been demonstrated that some saliency methods can highlight features that have no statistical association with the prediction target (suppressor variables).
Explainable Artificial Intelligence (XAI)
Feature Importance
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models.
Evaluating deep transfer learning for whole-brain cognitive decoding
Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e. g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data.
Inverse design of 3d molecular structures with conditional generative neural networks
The rational design of molecules with desired properties is a long-standing challenge in chemistry.
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.
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy
Our results indicate that models initialized from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
However, common ML losses do not give strong guarantees on how consistently the ML model performs for different domains, in particular, whether the model performs well on a domain at the expense of its performance on another domain.
BIGDML: Towards Exact Machine Learning Force Fields for Materials
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof.
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields.
Towards Robust Explanations for Deep Neural Networks
Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks.
Machine learning of solvent effects on molecular spectra and reactions
We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction.
Machine Learning Force Fields
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods.
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.
Langevin Cooling for Domain Translation
Domain translation is the task of finding correspondence between two domains.
Fairwashing Explanations with Off-Manifold Detergent
Explanation methods promise to make black-box classifiers more transparent.
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)
The Clever Hans Effect in Anomaly Detection
The 'Clever Hans' effect occurs when the learned model produces correct predictions based on the 'wrong' features.
Anomaly Detection
Explainable Artificial Intelligence (XAI)
+1
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.
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions, i. e. by identifying groups of edges that jointly contribute to the prediction.
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."
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective coarse-grained model.
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic.
Automatic Identification of Types of Alterations in Historical Manuscripts
In addition to the findings based on the digital scholarly edition Berlin Intellectuals, we present a general framework for the analysis of text genesis that can be used in the context of other digital resources representing document variants.
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI.
Building and Interpreting Deep Similarity Models
Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'.
Autonomous robotic nanofabrication with reinforcement learning
Here, we present a strategy to work around both obstacles, and demonstrate autonomous robotic nanofabrication by manipulating single molecules.
Forecasting Industrial Aging Processes with Machine Learning Methods
Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant.
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models
Based on a recent technique - Spectral Relevance Analysis - we propose the following technical contributions and resulting findings: (a) a scalable quantification of artifactual and poisoned classes where the machine learning models under study exhibit CH behavior, (b) several approaches denoted as Class Artifact Compensation (ClArC), which are able to effectively and significantly reduce a model's CH behavior.
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs.
Explainable Artificial Intelligence (XAI)
Model Compression
+2
Machine learning for molecular simulation
Machine learning (ML) is transforming all areas of science.
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints.
Towards Explainable Artificial Intelligence
Deep learning models are at the forefront of this development.
Explaining and Interpreting LSTMs
While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved.
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
Deep learning has recently gained popularity in digital pathology due to its high prediction quality.
Deep Transfer Learning For Whole-Brain fMRI Analyses
Even further, the pre-trained DL model variant is already able to correctly decode 67. 51% of the cognitive states from a test dataset with 100 individuals, when fine-tuned on a dataset of the size of only three subjects.
Explanations can be manipulated and geometry is to blame
Explanation methods aim to make neural networks more trustworthy and interpretable.
From Clustering to Cluster Explanations via Neural Networks
Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features.
Deep Semi-Supervised Anomaly Detection
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
Black-Box Decision based Adversarial Attack with Symmetric $α$-stable Distribution
Many existing methods employ Gaussian random variables for exploring the data space to find the most adversarial (for attacking) or least adversarial (for defense) point.
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
In this comment on "Solving Statistical Mechanics Using Variational Autoregressive Networks" by Wu et al., we propose a subtle yet powerful modification of their approach.
Robust and Communication-Efficient Federated Learning from Non-IID Data
Federated Learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a centralized server.
Local Function Complexity for Active Learning via Mixture of Gaussian Processes
Inhomogeneities in real-world data, e. g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of challenges for statistical inference.
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior.
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy.
Chemical Physics Computational Physics Data Analysis, Statistics and Probability
Automating the search for a patent's prior art with a full text similarity search
The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality.
Entropy-Constrained Training of Deep Neural Networks
We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle.
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
Computational Physics
Learning representations of molecules and materials with atomistic neural networks
Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio.
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models
We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.
Explaining the Unique Nature of Individual Gait Patterns with Deep Learning
Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems.
iNNvestigate neural networks!
The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods.
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions.
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
Detecting and explaining anomalies is a challenging effort.
Quantum-chemical insights from interpretable atomistic neural networks
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers.
Understanding Patch-Based Learning by Explaining Predictions
We apply the deep Taylor / LRP technique to understand the deep network's classification decisions, and identify a "border effect": a tendency of the classifier to look mainly at the bordering frames of the input.
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Adversarial attacks on deep learning models have compromised their performance considerably.
Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles
Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both.
Compact and Computationally Efficient Representation of Deep Neural Networks
These new matrix formats have the novel property that their memory and algorithmic complexity are implicitly bounded by the entropy of the matrix, consequently implying that they are guaranteed to become more efficient as the entropy of the matrix is being reduced.
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general.
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
The proposed One-Class DTD is applicable to a number of common distance-based SVM kernels and is able to reliably explain a wide set of data anomalies.
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science.
Chemical Physics
SchNet - a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.
Ranked #7 on
Formation Energy
on Materials Project
Formation Energy
Chemical Physics
Materials Science
An Empirical Study on The Properties of Random Bases for Kernel Methods
Kernel machines as well as neural networks possess universal function approximation properties.
Optimizing for Measure of Performance in Max-Margin Parsing
Many statistical learning problems in the area of natural language processing including sequence tagging, sequence segmentation and syntactic parsing has been successfully approached by means of structured prediction methods.
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks.
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images.
Minimizing Trust Leaks for Robust Sybil Detection
Sybil detection is a crucial task to protect online social networks (OSNs) against intruders who try to manipulate automatic services provided by OSNs to their customers.
Discovering topics in text datasets by visualizing relevant words
When dealing with large collections of documents, it is imperative to quickly get an overview of the texts' contents.
Exploring text datasets by visualizing relevant words
When working with a new dataset, it is important to first explore and familiarize oneself with it, before applying any advanced machine learning algorithms.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
Ranked #1 on
Time Series
on QM9
Methods for Interpreting and Understanding Deep Neural Networks
This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions.
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions.
Learning how to explain neural networks: PatternNet and PatternAttribution
We show that these methods do not produce the theoretically correct explanation for a linear model.
Predicting Pairwise Relations with Neural Similarity Encoders
Matrix factorization is at the heart of many machine learning algorithms, for example, dimensionality reduction (e. g. kernel PCA) or recommender systems relying on collaborative filtering.
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment.
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
We present a deep neural network-based approach to image quality assessment (IQA).
Wasserstein Training of Restricted Boltzmann Machines
This metric between observations can then be used to define the Wasserstein distance between the distribution induced by the Boltzmann machine on the one hand, and that given by the training sample on the other hand.
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation
Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application.
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).
Investigating the influence of noise and distractors on the interpretation of neural networks
Understanding neural networks is becoming increasingly important.
By-passing the Kohn-Sham equations with machine learning
Last year, at least 30, 000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields, ranging from materials science to biochemistry to astrophysics.
Language Detection For Short Text Messages In Social Media
These approaches include the use of the well-known classifiers such as SVM and logistic regression, a dictionary based approach, and a probabilistic model based on modified Kneser-Ney smoothing.
Object Boundary Detection and Classification with Image-level Labels
We propose a novel strategy for solving this task, when pixel-level annotations are not available, performing it in an almost zero-shot manner by relying on conventional whole image neural net classifiers that were trained using large bounding boxes.
Explaining Predictions of Non-Linear Classifiers in NLP
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables.
Identifying individual facial expressions by deconstructing a neural network
We further observe that the explanation method provides important insights into the nature of features of the base model, which allow one to assess the aptitude of the base model for a given transfer learning task.
Interpretable Deep Neural Networks for Single-Trial EEG Classification
With LRP a new quality of high-resolution assessment of neural activity can be reached.
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e. g. an image, down to relevance scores for the single input dimensions of the sample such as subpixels of an image.
Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth
We present an application of the Layer-wise Relevance Propagation (LRP) algorithm to state of the art deep convolutional neural networks and Fisher Vector classifiers to compare the image perception and prediction strategies of both classifiers with the use of visualized heatmaps.
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures.
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems.
Validity of time reversal for testing Granger causality
Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise.
Evaluating the visualization of what a Deep Neural Network has learned
Our main result is that the recently proposed Layer-wise Relevance Propagation (LRP) algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method.
Wasserstein Training of Boltzmann Machines
The Boltzmann machine provides a useful framework to learn highly complex, multimodal and multiscale data distributions that occur in the real world.
Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Accurate approximations to density functionals have recently been obtained via machine learning (ML).
Multi-Target Shrinkage
For the sample mean and the sample covariance as specific instances, we derive conditions under which the optimality of MTS is applicable.
Learning with Algebraic Invariances, and the Invariant Kernel Trick
When solving data analysis problems it is important to integrate prior knowledge and/or structural invariances.
Understanding Machine-learned Density Functionals
Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density.
Robust Spatial Filtering with Beta Divergence
The efficiency of Brain-Computer Interfaces (BCI) largely depends upon a reliable extraction of informative features from the high-dimensional EEG signal.
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures
Analytic shrinkage is a statistical technique that offers a fast alternative to cross-validation for the regularization of covariance matrices and has appealing consistency properties.
Optical Character Recognition
Optical Character Recognition (OCR)
Multiple Kernel Learning for Brain-Computer Interfacing
Combining information from different sources is a common way to improve classification accuracy in Brain-Computer Interfacing (BCI).
Orbital-free Bond Breaking via Machine Learning
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density.
Learning Invariant Representations of Molecules for Atomization Energy Prediction
The accurate prediction of molecular energetics in chemical compound space is a crucial ingredient for rational compound design.
Transferring Subspaces Between Subjects in Brain-Computer Interfacing
Compensating changes between a subjects' training and testing session in Brain Computer Interfacing (BCI) is challenging but of great importance for a robust BCI operation.
Regression for sets of polynomial equations
We propose a method called ideal regression for approximating an arbitrary system of polynomial equations by a system of a particular type.
Layer-wise analysis of deep networks with Gaussian kernels
Deep networks can potentially express a learning problem more efficiently than local learning machines.
Efficient and Accurate Lp-Norm Multiple Kernel Learning
Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations and hence support interpretability.
Subject independent EEG-based BCI decoding
In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for placing an electrode cap.
Estimating vector fields using sparse basis field expansions
We introduce a novel framework for estimating vector fields using sparse basis field expansions (S-FLEX).
Playing Pinball with non-invasive BCI
Compared to invasive Brain-Computer Interfaces (BCI), non-invasive BCI systems based on Electroencephalogram (EEG) signals have not been applied successfully for complex control tasks.
