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.
CWGD measures gradient diversity weighted by curvature, improving SGD convergence.
Riemannian stochastic gradient descent converges faster with increasing batch size.
Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
Learning rate annealing improves robustness in stochastic optimization.
Optimal learning rate schedules derived for various tasks.
The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strate…
Mode connectivity is a recently introduced frame- work that empirically establishes the connected- ness of minima by finding a high accuracy curve between two independently trained models. To investigate the limits of this setup, we examine the efficacy of this technique in extreme cases where the input models are trai…
Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple hil…
Modified cosine distance improves similarity performance in data with variance and correlation.
For many applications it is critical to know the uncertainty of a neural network's predictions. While a variety of neural network parameter estimation methods have been proposed for uncertainty estimation, they have not been rigorously compared across uncertainty measures. We assess four of these parameter estimation m…
Improved MoE performance through perturbing cosine router.
Reverse annealing boosts quantum matrix factorization performance.
Traditionally, multi-layer neural networks use dot product between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded, thus increases the risk of large variance. Large variance of neuron makes the model sensitive to the change o…
Researchers establish bounds and continuity of decomposed Möbius energies using cosine formula.
Cosine schedule is optimal for discrete diffusion models.
Derives hyperbolic laws of cosines and sines with fermionic corrections.
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of t…
Cosine similarity can force points to grow in magnitude, causing convergence issues.
New method uses reinforcement learning to improve Simulated Annealing.
We study the rigidity of polyhedral surfaces using variational principle. The action functionals are derived from the cosine laws. The main focus of this paper is on the cosine law for a non-triangular region bounded by three possibly disjoint geodesics. Several of these cosine laws were first discovered and used by Fe…
New method for European option pricing faster and more robust.
aMCL uses annealing to improve hypothesis diversity in ambiguous tasks.
This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference. QAVB is as easy as SAVB to implement. Experiments revealed QAVB finds a better local …
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…
Mathematical analysis shows annealing prevents mode collapse in Gaussian mixtures.
We introduce a novel framework for adversarial training where the target distribution is annealed between the uniform distribution and the data distribution. We posited a conjecture that learning under continuous annealing in the nonparametric regime is stable irrespective of the divergence measures in the objective fu…
Proposes a method to improve SLMC for multimodal distributions.
Simulated annealing improves candidate optimization for multi-objective Bayesian optimization.
Feedback alignment methods need to be evaluated for accuracy and gradient cosine similarity.
New analysis of annealing paths in sampling and estimation.
AdaAnn optimizes annealing for efficient probability density approximation.
CRAFT improves on existing methods for sampling complex distributions.
This paper tackles noise in raw datasets to improve representation learning efficiency.
T-PSDA improves speaker recognition accuracy on toroidal submanifolds.
We do further investigation in a certain cosine function defined for smooth Minkowski spaces. We prove that such function is symmetric if and only if the referred space is Euclidean, and also that it can be given in terms of the Gateaux derivative of the norm. As an application we use it to study the ratio between the …
We investigate a hybrid quantum-classical solution method to the mean-variance portfolio optimization problems. Starting from real financial data statistics and following the principles of the Modern Portfolio Theory, we generate parametrized samples of portfolio optimization problems that can be related to quadratic b…
The spherical Radon transform on the unit sphere can be regarded as a member of the analytic family of suitably normalized generalized cosine transforms. We derive new formulas for these transforms and apply them to study classes of intersections bodies in convex geometry.
Annealed importance sampling (AIS) is a common algorithm to estimate partition functions of useful stochastic models. One important problem for obtaining accurate AIS estimates is the selection of an annealing schedule. Conventionally, an annealing schedule is often determined heuristically or is simply set as a linear…
CR-AIS improves AIS efficiency by constant rate annealing.
Quantum Annealing Enhanced Reinforcement Learning for Accurate RUL Prediction
The paper analyzes the InfoNCE loss under different temperature schedules using Langevin dynamics.
Study improves sampling from complex distributions using annealed Langevin Monte Carlo.
Researchers analyze a new neural network training method.
We extend the Fourier cosine method to discrete probability distributions, achieving faster convergence rates.
We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse …
Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance. In this paper, we propose a novel method to address this task by training a network to obtain robust and representative features. The intuition is that we directly compare and optimize the cosi…
In this short article, we extend the cosine formula for the Möbius energy to generalized O'Hara energies. The newly derived formula gives us a condition for which the right circle minimizes the energy under the length-constraint. Furthermore, it shows us how far the energy is from the Möbius invariant property.
Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.