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.
Study derives error decay rates for kernel classification under source and capacity conditions.
The paper derives a new theorem for predicting batches of data.
The paper constructs optimal confidence bands for kernel gradient flow estimators.
New learning rates derived for Tikhonov-regularized problems without kernel assumptions.
GD outperforms ridge regression and SGD in linear regression problems.
Accurately predicting the future health of batteries is necessary to ensure reliable operation, minimise maintenance costs, and calculate the value of energy storage investments. The complex nature of degradation renders data-driven approaches a promising alternative to mechanistic modelling. This study predicts the ch…
Study excess capacity in neural networks using Rademacher complexity.
Study proves inequalities for mass-capacity on curved spaces.
New complete panel dataset for LMICs helps analyze innovation and development.
In this paper we address the following question, given a face representation, how many identities can it resolve? In other words, what is the capacity of the face representation? A scientific basis for estimating the capacity of a given face representation will not only benefit the evaluation and comparison of differen…
Adding noise controls capacity of function compositions.
In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We investigate a class of spectral/regularized algorithms, including ridge regression, principal component regression, and gradient methods. We pro…
The energy transition is well underway in most European countries. It has a growing impact on electric power systems as it dramatically modifies the way electricity is produced. To ensure a safe and smooth transition towards a pan-European electricity production dominated by renewable sources, it is of paramount import…
Symplectic fillings of prequantization bundles are shown to be disk bundles under certain conditions.
We estimate Radon-Nikodym derivatives using regularization in reproducing kernel Hilbert spaces.
Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory ta…
Study capacity constraints in continual learning with a simple model.
We conduct an axiomatic study of the problem of estimating the strength of a known causal relationship between a pair of variables. We propose that an estimate of causal strength should be based on the conditional distribution of the effect given the cause (and not on the driving distribution of the cause), and study d…
A note proves the binary perceptron's capacity is less than 0.847.
Domain adaptation (DA) is an important and emerging field of machine learning that tackles the problem occurring when the distributions of training (source domain) and test (target domain) data are similar but different. Current theoretical results show that the efficiency of DA algorithms depends on their capacity of …
Study on risk measures using distorted Choquet integrals with random distortions.
Study on uniquely determining thermal properties from boundary temperature and heat flux measurements.
Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance …
Researchers analyze neural process architectures and their representational capacities.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
New method disentangles sources of different timescales in planetary seismic data.
The paper connects mass, harmonic functions, and capacity in asymptotically flat 3-manifolds.
New analysis tightens memory capacity of Hopfield models using spherical codes.
We use drifted Brownian motion in warped product model spaces as comparison constructions to show -hyperbolicity of a large class of submanifolds for . The condition for -hyperbolicity is expressed in terms of upper support functions for the radial sectional curvatures of the ambient space and for the rad…
Scattering networks maximize separation on low-dimensional data.
Successful implementation of California's Renewable Portfolio Standard (RPS) mandating 33 percent renewable energy generation by 2020 requires inclusion of a robust strategy to mitigate increased risk of energy deficits (blackouts) due to short time-scale (sub 1 hour) intermittencies in renewable energy sources. Of the…
This work extends the randomized shortest paths (RSP) model by investigating the net flow RSP and adding capacity constraints on edge flows. The standard RSP is a model of movement, or spread, through a network interpolating between a random-walk and a shortest-path behavior [30, 42, 49]. The framework assumes a unit f…
Modeling alignment as resource-limited cognitive processes, researchers derive performance bounds.
Maximizes capacity of extensions with fixed boundary data.
CWAN tackles multi-source heterogeneous domain adaptation with conditional weighting.
Paper applies theorem to find optimal investment boundary in stochastic capacity expansion.
The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice…
The paper defines capacities for minimal graphs over manifolds and proves the half-space property.
The paper bridges spectral and spatial graph convolutions, improving model capacity and transferability.
We address the problem of one-to-many mappings in supervised learning, where a single instance has many different solutions of possibly equal cost. The framework of conditional variational autoencoders describes a class of methods to tackle such structured-prediction tasks by means of latent variables. We propose to in…
Following the recent work on capacity allocation, we formulate the conjecture that the shattering problem in deep neural networks can only be avoided if the capacity propagation through layers has a non-degenerate continuous limit when the number of layers tends to infinity. This allows us to study a number of commonly…
New technique for multiple-source adaptation without density estimation.
CPAS uses machine learning to plan hospital resources for COVID-19.
Compared with shallow domain adaptation, recent progress in deep domain adaptation has shown that it can achieve higher predictive performance and stronger capacity to tackle structural data (e.g., image and sequential data). The underlying idea of deep domain adaptation is to bridge the gap between source and target d…
In the paper we give necessary and sufficient conditions for the Jensen inequality to hold for the generalized Choquet integral with respect to a pair of capacities. Next, we apply obtained result to the theory of risk aversion by providing the assumptions on utility function and capacities under which an agent is risk…
Paper improves learning rates for GSC loss functions using iterated Tikhonov regularization.
Agents trained with deep reinforcement learning algorithms are capable of performing highly complex tasks including locomotion in continuous environments. We investigate transferring the learning acquired in one task to a set of previously unseen tasks. Generalization and overfitting in deep reinforcement learning are …
This paper deals with a multichannel audio source separation problem under underdetermined conditions. Multichannel Non-negative Matrix Factorization (MNMF) is one of powerful approaches, which adopts the NMF concept for source power spectrogram modeling. This concept is also employed in Independent Low-Rank Matrix Ana…