A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Sharp boundaries for detecting dense subhypergraphs established.
Stochastic partition models tailor a product space into a number of rectangular regions such that the data within each region exhibit certain types of homogeneity. Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions. To allevia…
ViCE uses superpixels to enhance self-supervised learning for better dense visual embeddings.
Heavy-tailed distributions are frequently used to enhance the robustness of regression and classification methods to outliers in output space. Often, however, we are confronted with "outliers" in input space, which are isolated observations in sparsely populated regions. We show that heavy-tailed stochastic processes (…
Crowdsourcing is a strategy to categorize data through the contribution of many individuals. A wide range of theoretical and algorithmic contributions are based on the model of Dawid and Skene [1]. Recently it was shown in [2,3] that, in certain regimes, belief propagation is asymptotically optimal for data generated f…
Rectangular Bounding Process (RBP) improves partitioning efficiency in multi-dimensional spaces.
We show that discrete synaptic weights can be efficiently used for learning in large scale neural systems, and lead to unanticipated computational performance. We focus on the representative case of learning random patterns with binary synapses in single layer networks. The standard statistical analysis shows that this…
This work introduces a novel nonparametric density index defined on graphs, the Sum-over-Forests (SoF) density index. It is based on a clear and intuitive idea: high-density regions in a graph are characterized by the fact that they contain a large amount of low-cost trees with high outdegrees while low-density regions…
A new deep metric learning method pulls embeddings towards dense clusters to improve classification accuracy.
VSPS creates flexible prediction regions for multi-target regression with guaranteed coverage.
End-to-end image super-resolution using Attention-based DenseNet with residual deconvolution.
In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost-function. It is not well understood how learning occurs in these systems, in particular how they avo…
Neural network accuracy improves with denser training samples.
Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations where sensors are installed. Moreover, the traffic evolves over time due to the influences of weather, events, holidays, etc. Existing soluti…
Proposes a new method for ensembling neural subnetworks.
We present formulae for computing the Yamada polynomial of spatial graphs obtained by replacing edges of plane graphs, such as cycle-graphs, theta-graphs, and bouquet-graphs, by spatial parts. As a corollary, it is shown that zeros of Yamada polynomials of some series of spatial graphs are dense in a certain region in …
DNAS disentangles neural architecture search for better interpretability and performance.
Effective estimates for lattice orbits in homogeneous spaces.
OLALA automates document layout annotation by selecting ambiguous regions for labeling.
Canary optimizes VaR-constrained RL problems with a conservative bound using Cantelli's inequality.
Diverse sampling improves kernel methods' performance in sparse regions.
Deep convolutional neural networks have become a key element in the recent breakthrough of salient object detection. However, existing CNN-based methods are based on either patch-wise (region-wise) training and inference or fully convolutional networks. Methods in the former category are generally time-consuming due to…
SleepNet detects sleep disorders using neural networks trained on PSG data.
The paper proves properties of strain tensors on surfaces with changing Gauss curvature.
Minimal variations guide unsupervised learning for better downstream tasks.
Applying deep learning methods to mammography assessment has remained a challenging topic. Dense noise with sparse expressions, mega-pixel raw data resolution, lack of diverse examples have all been factors affecting performance. The lack of pixel-level ground truths have especially limited segmentation methods in push…
Very recently Ben Andrews and Haizhong Li showed that every embedded cmc torus in the three dimensional sphere is axially symmetric. There is a two-parametric family of axially symmetric cmc surfaces; more precisely, for every real number H and every C > 2 (H+\sqrt{1+H^2}) there is an axially symmetry surface Σ_{H,C} w…
CW-EDMD improves prediction accuracy by learning local Koopman models for different state-space regions.
A scalable graph-based SSL method for large-scale data with few labels.
Paper proposes efficient methods for clustering and signal recovery in high-dimensional data with block structures.
We develop a new modeling framework for Inter-Subject Analysis (ISA). The goal of ISA is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. It has important applications in neuroscience to explore the functional connectivity between brain regions under natural …
The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high d…
Stochasticity and limited precision of synaptic weights in neural network models are key aspects of both biological and hardware modeling of learning processes. Here we show that a neural network model with stochastic binary weights naturally gives prominence to exponentially rare dense regions of solutions with a numb…
Neural samplers such as variational autoencoders (VAEs) or generative adversarial networks (GANs) approximate distributions by transforming samples from a simple random source---the latent space---to samples from a more complex distribution represented by a dataset. While the manifold hypothesis implies that the densit…
Generic Hitchin representations generate dense subgroups.
Quantum annealers aim at solving non-convex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists in designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable qua…
Minimal DAMs can recognize patterns in high noise, even with minimal data.
New lattices in higher dimensions have dense surface subgroups.
Classifies rank-one submanifolds in Euclidean space.
We discuss dense embeddings of surface groups and fully residually free groups in topological groups. We show that a compact topological group contains a nonabelian dense free group of finite rank if and only if it contains a dense surface group. Also, we obtain a characterization of those Lie groups which admit a dens…
Analyzes packing of circles in bounded and unbounded planes using mathematical formulas.
Gradient boosting with randomized trees reduces discontinuities and complexity.
New loss function improves adversarial robustness without sacrificing standard accuracy.
New algorithm predicts spatio-temporal events with improved accuracy.
WiGS improves active learning for regression by dynamically selecting informative samples.
Bi-Lipschitz rigidity theorem for dense subgroups of algebraic groups.
The study finds conditions for certain groups to be dense in a specific mathematical space.
New representations of hyperbolic 3-manifold groups into larger groups.