arXiv research

GANSynth uses GANs to efficiently synthesize high-fidelity audio.

Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently shown that Bayesian dropout procedure not only improves generalization but also leads to extremely sparse neural architectures by automatica…

Differentially private log-location-scale regression models improve privacy in statistical analysis.

SGD's escape rate depends on log loss barrier, not linear loss barrier.

We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data dimension scales. In particular, we consider a general approach where changes are detected by comparing the distribution of the log-likelihood of …

The complete part of the earthquake frequency-magnitude distribution (FMD), above completeness magnitude mc, is well described by the Gutenberg-Richter law. The parameter mc however varies in space due to the seismic network configuration, yielding a convoluted FMD shape below max(mc). This paper investigates the shape…

We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose log intensities are given by linear combinations of latent functions drawn from Gaussian process priors. The combination coefficients are als…

Improved DDPMs achieve high log-likelihoods and sample quality with fewer passes.

FAST algorithm maximizes submodular functions efficiently.

Deep learning compresses L-values for QAM symbols, reducing memory and improving performance.

A hypothesis that the financial log-periodicity, cascading self-similarity through various time scales, carries signatures of a law is pursued. It is shown that the most significant historical financial events can be classified amazingly well using a single and unique value of the preferred scaling factor lambda=2, whi…

We consider the problem of recovering a signal $\mathbf{x}^* \in \mathbf{R}^n$, from magnitude-only measurements $y_i = |\left\langle\mathbf{a}_i,\mathbf{x}^*\right\rangle|$ for $i=[m]$. Also called the phase retrieval, this is a fundamental challenge in bio-,astronomical imaging and speech processing. The problem abov…

Acoustic scene classification is the task of identifying the scene from which the audio signal is recorded. Convolutional neural network (CNN) models are widely adopted with proven successes in acoustic scene classification. However, there is little insight on how an audio scene is perceived in CNN, as what have been d…

A new sampler for complex discrete distributions efficiently updates all variables in parallel.

Improved regret bounds for Tsallis-INF in adversarial bandits and corruptions.

Novel algorithm PSO improves density estimation for multimodal data.

Coherent uncertainty quantification is a key strength of Bayesian methods. But modern algorithms for approximate Bayesian posterior inference often sacrifice accurate posterior uncertainty estimation in the pursuit of scalability. This work shows that previous Bayesian coreset construction algorithms---which build a sm…

Changepoint detection is a central problem in time series and genomic data. For some applications, it is natural to impose constraints on the directions of changes. One example is ChIP-seq data, for which adding an up-down constraint improves peak detection accuracy, but makes the optimization problem more complicated.…

DAGMA learns DAGs faster and more accurately using log-determinant acyclicity.

New algorithm discovers causal graphs efficiently from observational data.

We consider the problem of approximate Bayesian inference in log-supermodular models. These models encompass regular pairwise MRFs with binary variables, but allow to capture high-order interactions, which are intractable for existing approximate inference techniques such as belief propagation, mean field, and variants…

New denoisers improve signal recovery from noisy data without knowing noise distribution.

DisCoveR efficiently discovers declarative process models from event logs.

Improved regret bound for adversarial MDPs with linear function approximation.

Magnitude homology reveals that graphs can have torsion subgroups.

Paper introduces differentiable sorting and ranking with $O(n \log n)$ time complexity.

Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small sub…

Improved sample complexity bounds for neural networks with weight magnitude constraints.

Pathfinder uses quasi-Newton optimization for variational inference.

The log-periodic power law (LPPL) is a model of asset prices during endogenous bubbles. A major open issue is to verify the presence of LPPL in price sequences and to estimate the LPPL parameters. Estimation is complicated by the fact that daily LPPL returns are typically orders of magnitude smaller than measured price…

A new method for efficient BNC parameter estimation outperforms HDP smoothing.

Compress++ speeds up distribution compression to near-linear time.

Defines magnitude for length spaces with measures, agreeing with finite spaces' magnitude.

Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction. Fitting a GP to function values and derivatives at $n$ points in $d$ dimensions requires linear solves and log determinants with an ${n(d+1) \times n(d…

CRBMs improve financial regime detection with PCD and free energy analysis.

New algorithm reconstructs sparse networks in subquadratic time.

We show that there is a common mode of origin for the power laws observed in two different models: (i) the Pareto law for the distribution of money among the agents with random saving propensities in an ideal gas-like market model and (ii) the Gutenberg-Richter law for the distribution of overlaps in a fractal-overlap …

We consider the problem of online nonparametric regression with arbitrary deterministic sequences. Using ideas from the chaining technique, we design an algorithm that achieves a Dudley-type regret bound similar to the one obtained in a non-constructive fashion by Rakhlin and Sridharan (2014). Our regret bound is expre…

Study on bias-variance trade-off in hierarchical models with higher-order interactions.

This research quantifies neural networks using magnitude, a topological invariant.

Magnitude of manifolds linked to Riesz energies and beta functions.

Lookahead pruning extends single-layer optimization to multi-layer, outperforming magnitude-based pruning.

New training method for ReLU networks achieves optimal weight size for memorization.

Magnitude is not continuous but may be stable for most finite metric spaces.

Magnitude of Euclidean domains predicts Willmore energy in odd dimensions.

BiGG model efficiently generates sparse graphs with reduced complexity.

Are expansions and recessions more likely to end as their magnitude increases? In this paper we apply parametric hazard models to investigate this issue in a sample of 16 countries from 1881 to 2000. For the total sample we find evidence of positive magnitude dependence for recessions, while for expansions we are not a…

Magnitude study on manifolds using fractional Laplacian.