268 citations · 470 across the 19 of their papers we have counts for
22 papers
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
Kim A. Nicoli, Christopher J. Anders, Tobias Hartung +3
We study the consequences of mode-collapse of normalizing flows in the context of lattice field theory. Normalizing flows allow for independent sampling. For this reason, it is hop…
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows
Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima +1
We propose an algorithm to estimate the path-gradient of both the reverse and forward Kullback-Leibler divergence for an arbitrary manifestly invertible normalizing flow. The resul…
Path-Gradient Estimators for Continuous Normalizing Flows
Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima +1
Recent work has established a path-gradient estimator for simple variational Gaussian distributions and has argued that the path-gradient is particularly beneficial in the regime i…
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
Kim A. Nicoli, Christopher Anders, Lena Funcke +5
Estimating the free energy, as well as other thermodynamic observables, is a key task in lattice field theories. Recently, it has been pointed out that deep generative models can b…
Optimal Sampling Density for Nonparametric Regression
Danny Panknin, Klaus Robert Müller, Shinichi Nakajima
We propose a novel active learning strategy for regression, which is model-agnostic, robust against model mismatch, and interpretable. Assuming that a small number of initial sampl…
Langevin Cooling for Domain Translation
Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek +1
Domain translation is the task of finding correspondence between two domains. Several Deep Neural Network (DNN) models, e.g., CycleGAN and cross-lingual language models, have shown…
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks
Kirill Bykov, Marina M. -C. Höhne, Klaus-Robert Müller +2
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…
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
Thomas Schnake, Oliver Eberle, Jonas Lederer +4
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explain…
Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies
Alexander Bauer, Shinichi Nakajima
Considering the worst-case scenario, junction tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees. Unfortunately, its main…
Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff +3
We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling p…
Towards Best Practice in Explaining Neural Network Decisions with LRP
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima +3
Within the last decade, neural network based predictors have demonstrated impressive - and at times super-human - capabilities. This performance is often paid for with an intranspa…
Black-Box Decision based Adversarial Attack with Symmetric -stable Distribution
Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller +2
Developing techniques for adversarial attack and defense is an important research field for establishing reliable machine learning and its applications. Many existing methods emplo…
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
Kim Nicoli, Pan Kessel, Nils Strodthoff +3
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. We show…
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
Jacob Kauffmann, Grégoire Montavon, Luiz Alberto Lima +3
Detecting and explaining anomalies is a challenging effort. This holds especially true when data exhibits strong dependencies and single measurements need to be assessed and analyz…
Tight Bound of Incremental Cover Trees for Dynamic Diversification
Hannah Marienwald, Wikor Pronobis, Klaus-Robert Müller +1
Dynamic diversification---finding a set of data points with maximum diversity from a time-dependent sample pool---is an important task in recommender systems, web search, database…
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller +2
Adversarial attacks on deep learning models have compromised their performance considerably. As remedies, a lot of defense methods were proposed, which however, have been circumven…
Optimizing for Measure of Performance in Max-Margin Parsing
Alexander Bauer, Shinichi Nakajima, Nico Görnitz +1
Many statistical learning problems in the area of natural language processing including sequence tagging, sequence segmentation and syntactic parsing has been successfully approach…
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs
Alexander Bauer, Shinichi Nakajima, Nico Görnitz +1
Markov random fields (MRFs) are a powerful tool for modelling statistical dependencies for a set of random variables using a graphical representation. An important computational pr…
SynsetRank: Degree-adjusted Random Walk for Relation Identification
Shinichi Nakajima, Sebastian Krause, Dirk Weissenborn +3
In relation extraction, a key process is to obtain good detectors that find relevant sentences describing the target relation. To minimize the necessity of labeled data for refinin…
Sharing Hash Codes for Multiple Purposes
Wikor Pronobis, Danny Panknin, Johannes Kirschnick +6
Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissim…
Sparse Probit Linear Mixed Model
Stephan Mandt, Florian Wenzel, Shinichi Nakajima +3
Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a conti…
Insights from Classifying Visual Concepts with Multiple Kernel Learning
Alexander Binder, Shinichi Nakajima, Marius Kloft +5
Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is…