arXiv research

In the paper arXiv:1411.4887 [math.AP] it is shown that the set of Riemannian metrics which do not admit global limiting Carleman weights is open and dense, by studying the conformally invariant Weyl and Cotton tensors. In the paper arXiv:1011.2507 [math.DG] it is shown that the set of Riemannian metrics which do not a…

Open AI models affect bond yields differently than closed ones.

PCA reveals a market factor in S&P500 implied volatilities.

Improved text summarization using belief propagation on weighted bipartite graphs.

In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation. This enables us to study the relative importance of each interaction block for property prediction. We demonstrate on both the QM9 and MD17 dataset that their relative weighting depends strongly on t…

A new approach optimizes weights in DLP for better risk-adjusted performance.

Method identifies change points in high-dimensional models using sample weights.

Proposes learning invariances in neural networks using a weight-space approach.

Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing methods for learning under the assumption of structured sparsity rely on prior knowledge on how to weight (or how to penalize) individual subsets…

Proposes overnight volatility model for better market dynamics.

Proposes FMS for more efficient neural network hyperparameter optimization.

The study counts periodic orbits on smooth manifolds, adding ghost orbits for completeness.

This paper classifies strongly nilpotent special multi-flags and their Goursat counterparts.

Dagma-DCE improves causal discovery with interpretable measures and open-source code.

Integrates multiple datasets to solve open set crowdsourcing problems.

BayesBlend blends multiple models' predictions for better insurance loss predictions.

The paper proves barriers to approximating functions with small weights and depth in neural networks.

Novel characterization of augmented balancing weights combining outcome and weighting models.

Neural networks with learned biases can approximate any function.

Bayesian weight priors improve neural network learning of identity relations.

The paper studies Finsler manifolds with a new curvature concept.

The paper discusses rigidity results for inequalities on weighted Riemannian manifolds.

We present an explicit construction of the moduli spaces of rank 2 stable parabolic bundles of parabolic degree 0 over the Riemann sphere, corresponding to "optimum" open weight chambers of parabolic weights in the weight polytope. The complexity of the different moduli space' weight chambers is understood in terms of …

The study introduces anytime learning schedules for large language models without fixed horizons.

Establishes convexity and coercivity of K-energy functional for complex tori.

We study the Yamabe problem on open manifolds of bounded geometry and show that under suitable assumptions there exist Yamabe metrics, i.e. conformal metrics of constant scalar curvature. For that, we use weighted Sobolev embeddings.

Optimizes sample weights for representative data averages.

We prove some old and new isoperimetric inequalities with the best constant using the ABP method applied to an appropriate linear Neumann problem. More precisely, we obtain a new family of sharp isoperimetric inequalities with weights (also called densities) in open convex cones of $\mathbb{R}^n$. Our result applies to…

Optimization of Binarized Neural Networks (BNNs) currently relies on real-valued latent weights to accumulate small update steps. In this paper, we argue that these latent weights cannot be treated analogously to weights in real-valued networks. Instead their main role is to provide inertia during training. We interpre…

New insights into Kähler Ricci solitons and Calabi-Yau cones.

Paper tackles open set domain adaptation by detecting unknown classes.

In importance sampling (IS)-based reinforcement learning algorithms such as Proximal Policy Optimization (PPO), IS weights are typically clipped to avoid large variance in learning. However, policy update from clipped statistics induces large bias in tasks with high action dimensions, and bias from clipping makes it di…

We revisit the classical decision-theoretic problem of weighted expert voting from a statistical learning perspective. In particular, we examine the consistency (both asymptotic and finitary) of the optimal Nitzan-Paroush weighted majority and related rules. In the case of known expert competence levels, we give sharp …

WAFFLe anonymizes federated learning weights to protect data privacy and fairness.

Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not applicable. One of the main strategies is to use pure or hybrid content-based approaches, which usually yield to lower recommendation quality tha…

The capitalization-weighted total relative variation $\sum_{i=1}^d \int_0^\cdot μ_i (t) \mathrm{d} \langle \log μ_i \rangle (t)$ in an equity market consisting of a fixed number $d$ of assets with capitalization weights $μ_i (\cdot)$ is an observable and nondecreasing function of time. If this observable of the market …

Enhances trading signals using image analysis and weighted moving averages.

Paper proposes a robust LPR method using similarity kernels.

New proof shows unique symplectic fillings for certain surface singularity links.

Deep equilibrium models converge globally without explicit computation.

Learning weights in a spiking neural network with hidden neurons, using local, stable and online rules, to control non-linear body dynamics is an open problem. Here, we employ a supervised scheme, Feedback-based Online Local Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking neurons with hidden l…

wd1 improves reasoning in dLLMs by optimizing policies without policy ratios.

New measure defined for Brakke flow, linking classical and new definitions.

A classical result due to Blaschke states that for every analytic self-map $f$ of the open unit disk of the complex plane there exists a Blaschke product $B$ such that the zero sets of $f$ and $B$ agree. In this paper we show that there is an analogue statement for critical sets, i.e. for every analytic self-map $f$ of…

We show how well known rules of back propagation arise from a weighted combination of finite automata. By redefining a finite automata as a predictor we combine the set of all $k$-state finite automata using a weighted majority algorithm. This aggregated prediction algorithm can be simplified using symmetry, and we pro…

The paper proves geometric inequalities for hypersurfaces in weighted manifolds.

KANs replace fixed MLP weights with learnable edge functions, improving accuracy and interpretability.

Online CPD for weighted and directed graphs using RDPG model.