arXiv research

DeepDiff predicts differential gene expression from histone modifications using deep learning.

Improved peak detection in ChIP-seq data reduces over-dispersion.

Many proteoforms - arising from alternative splicing, post-translational modifications (PTMs), or paralogous genes - have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid …

With the wealth of high-throughput sequencing data generated by recent large-scale consortia, predictive gene expression modelling has become an important tool for integrative analysis of transcriptomic and epigenetic data. However, sequencing data-sets are characteristically large, and previously modelling frameworks …

We consider the task of detecting regulatory elements in the human genome directly from raw DNA. Past work has focused on small snippets of DNA, making it difficult to model long-distance dependencies that arise from DNA's 3-dimensional conformation. In order to study long-distance dependencies, we develop and release …

Machine learning algorithms such as linear regression, SVM and neural network have played an increasingly important role in the process of scientific discovery. However, none of them is both interpretable and accurate on nonlinear datasets. Here we present contextual regression, a method that joins these two desirable …

This work improves testing of machine learning model modifications using novel statistical methods.

New method detects RNA modifications without prior training, revealing novel sites.

We define an operation on homology ${B}^4$ which we call an $n$-twist annulus modification. We give a new construction of smoothly slice knots and exotically slice knots via $n$-twist annulus modifications. As an application, we present a new example of a smoothly slice knot with non-slice derivatives. Such examples we…

Study shows stability of locally conformally balanced condition under modifications but not under small deformations.

Proposes simplified SHAP for faster black-box model explanations.

Behavior modification improves prediction accuracy by nudging user behavior.

Paper proposes adversarial modifications for link prediction models to improve robustness and interpretability.

Study of unimodular Sasaki and Vaisman Lie groups, determining all modifications explicitly.

We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational …

Adaptive weighting improves Deep Forest classifier's performance.

Method explains anomaly detection by generating normal modifications.

We study Khovanov homology classes which have state cycle representatives, and examine how they interact with Jacobsson homomorphisms and Lee's map $Φ$. As an application, we describe a general procedure, quasipositive modification, for constructing H-thick knots in rational Khovanov homology. Moreover, we show that sp…

Preserves hyperbolicity in link complements with two moves.

The perceptron's compression is explored and applied to neural networks.

Paper proposes using pairwise feature comparisons to infer modification costs for user recourse.

We propose a modification of the three-manifold invariant based on the use of Euclidean metric values ascribed to the elements of manifold triangulation. We thus obtain a nontrivial invariant that can, in particular, distinguish non-homeomorphic lens spaces.

Max-Cut decision tree improves classification accuracy and reduces computation time.

We briefly review the approach to optimization of portfolios according to the theory of Markowitz and propose a further modification that can improve the outcome of the optimization process. The modification takes account of the entropic contribution from the time series used to compute the parameters in the Markowitz …

In this paper we suggest a modification of the regression-based variance reduction approach recently proposed in Belomestny et al. This modification is based on the stratification technique and allows for a further significant variance reduction. The performance of the proposed approach is illustrated by several numeri…

Kapustin and Witten associate a Hecke modification of a holomorphic bundle over a Riemann surface to a singular monopole on a Riemannian surface times an interval satisfying prescribed boundary conditions. We prove existence and uniqueness of singular monopoles satisfying prescribed boundary conditions for any given He…

We present an axiomatic modification of quaternionic quantum mechanics with a possible-worlds semantics capable of predicting essential "nonquantum" features of an observable universe model - the dimensionality and topology of spacetime, the existence, the signature and a specific form of a metric on it, and certain na…

A new bias score method optimizes fairness in classification.

Label smoothing improves model robustness against misspecification.

Automatic video modification to hide faces while maintaining pose, illumination, and expression.

In this paper, we consider a proper modification $f : \tilde M \to M$ between complex manifolds, and study when a generalized $p-$Kähler property goes back from $M$ to $\tilde M$. When $f$ is the blow-up at a point, every generalized $p-$Kähler property is conserved, while when $f$ is the blow-up along a submanifold, t…

Thompson Sampling remains differentially private with minimal modifications.

Modified LSTM cells iteratively process input, improving model performance.

Proposes a probabilistic method for generating semantically-aware adversarial examples.

We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al. (2000, 1993), Richardson (1996), Colombo et al. (2012), Claassen et al. (2013)). The first step of all these algorithms consists of the PC-algorithm. This algorithm is known to be order-d…

Modified BFGS and LBFGS++ libraries boost performance for non-parallelizable functions.

MCD automates counterfactual design searches for multi-modal tasks.

Enhances statistical mechanics solving using VANs with MCMC or importance sampling.

A stealthy framework injects faults into DNNs to misclassify images without affecting overall accuracy.

We discuss a general framework for cutting constructions and reinterpret in this setting the work on non-Abelian symplectic cuts by Weitsman. We then introduce two analogous non-Abelian modification constructions for hyperkähler manifolds: one modifies the topology significantly, the other gives metric deformations. We…

New groups contactomorphic to stratified ones found.

The two main topics of this text are as follows: Firstly, three modifications of the theorem of Beltrami will be presented for diffeomorphisms between Riemannian manifolds and a space form which preserve the geodesic circles, the geodesic hyperspheres, or the minimal surfaces, respectively. Secondly, it is defined what…

Defines complex manifolds for Khovanov homology computation.

PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at https://github.com/openai/pixel-cnn. Our implementation contains a number of modifications to the original model that both simplify its structure an…

Study shows how to manipulate VAEs for attacks and assess their robustness.

A new method improves continual learning by replaying pseudo data and using orthogonal weight modification.

In this note we show that for the group G = U(N) the space of Hecke modifications of a rank N vector bundle over a Riemann surface C coincides with the moduli space of solutions of certain non-abelian vortex equations over C . Through the recent work of Kapustin and Witten this then leads to an isomorphism between the …

This paper unifies observability notions for colored graphs and identifies graph modifications to improve observability.