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 predicts crypto-currency price collapses using standard deviation.
Crypto crashes show no consistent early warning signal, suggesting they are abrupt shocks rather than critical transitions.
The principal aim of this work is the evidence on empirical way that catastrophic bifurcation breakdowns or transitions, proceeded by flickering phenomenon, are present on notoriously significant and unpredictable financial markets. Overall, in this work we developed various metrics associated with catastrophic bifurca…
This thesis optimizes neuromorphic systems by slowing down their dynamics, improving performance.
Different technological domains have significantly different rates of performance improvement. Prior theory indicates that such differing rates should influence the relative speed of diffusion of the products embodying the different technologies since improvement in performance during the diffusion process increases th…
Although gradient descent (GD) almost always escapes saddle points asymptotically [Lee et al., 2016], this paper shows that even with fairly natural random initialization schemes and non-pathological functions, GD can be significantly slowed down by saddle points, taking exponential time to escape. On the other hand, g…
The paper analyzes why Gaussianization slows down with higher dimensions and proposes a solution.
GRAC improves reinforcement learning by self-guiding and self-regularizing.
When applied to training deep neural networks, stochastic gradient descent (SGD) often incurs steady progression phases, interrupted by catastrophic episodes in which loss and gradient norm explode. A possible mitigation of such events is to slow down the learning process. This paper presents a novel approach to contro…
Gradient descent slows significantly in over-parameterized single neuron learning.
A new sampler tackles critical phenomena by leveraging scale invariance.
Study uses DNM theory to detect early warning signals of market instability.
Reservoir computing predicts rare critical transitions in complex systems.
Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an -ball around the obs…
The paper proves unique blow-down for critical points of a Yang-Mills-Higgs functional.
We study the maximum mean discrepancy (MMD) in the context of critical transitions modelled by fast-slow stochastic dynamical systems. We establish a new link between the dynamical theory of critical transitions with the statistical aspects of the MMD. In particular, we show that a formal approximation of the MMD near …
The study uses Gaussian mixture models to estimate pipe wall thickness from partial scans.
L-CNNs approximate gauge actions, revealing fixed points with no lattice artifacts.
The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the s…
Machine learning detects regime shifts in online game-experiments with high accuracy.
The paper studies invariant complex manifolds in holomorphic slow-fast systems.
Machine learning finds a compact fixed point action for SU(3) gauge theory.
Near a birth-death critical point in a one-parameter family of gradient flows, there are precisely two Morse critical points of index difference one on the birth side. This paper gives a self-contained proof of the folklore theorem that these two critical points are joined by a unique gradient trajectory up to time-shi…
Paper analyzes convergence of FedAvg on non-iid data and provides theoretical guarantees.
Dealing with the shear size and complexity of today's massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern distributed computing environments is that some of the worker nodes may run slow. These nodes a.k.a.~str…
Measures of wealth and production have been found to scale superlinearly with the population of a city. Therefore, it makes economic sense for humans to congregate together in dense settlements. A recent model of population dynamics showed that population growth can become superexponential due to the superlinear scalin…
A new gradient descent method speeds up in flat regions and slows in steep directions.
Gradient method converges locally linearly for overparameterized Gaussian mixtures.
PrecGD restores linear convergence in over-parameterized nonconvex matrix factorization.
A critical and challenging problem in reinforcement learning is how to learn the state-action value function from the experience replay buffer and simultaneously keep sample efficiency and faster convergence to a high quality solution. In prior works, transitions are uniformly sampled at random from the replay buffer o…
Empirical data of supermarket sales show stylised facts that are similar to stock markets, with a broad (truncated) Levy distribution of weekly sales differences in the baseline sales [R.D. Groot, Physica A 353 (2005) 501]. To investigate the cause of this, the influence of social interactions and advertisements are st…
Forecast future volatilities and correlations based on current trends.
Improved sampling for gauge theory with SNFs.
This study improves convergence of two-timescale SA under Markovian noise in reinforcement learning.
Develops variational inference for Neyman-Scott processes for faster sampling.
New method estimates and samples high-dimensional probability distributions avoiding optimization and approximation curse.
Proposes CLRS-Text, a new benchmark for evaluating LM reasoning capabilities.
Accelerates optimal transport computation by 10x with spectral insights.
Learning the distribution of natural images is one of the hardest and most important problems in machine learning. The problem remains open, because the enormous complexity of the structures in natural images spans all length scales. We break down the complexity of the problem and show that the hierarchy of structures …
New method detects close contacts to prevent SARS-CoV-2 spread.
We study the dependence of volatility on the stock price in the stochastic volatility framework on the example of the Heston model. To be more specific, we consider the conditional expectation of variance (square of volatility) under fixed stock price return as a function of the return and time. The behavior of this fu…
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this lim…
RLHC uses multiple critics at different levels to enhance RL performance.
Research finds investors may lose from more diverse workplaces.
We argue that the word ``critical'' in the title is not purely literary. Based on our and other previous work on nonlinear complex dynamical systems, we summarize present evidence, on the Oct. 1929, Oct. 1987, Oct. 1987 Hong-Kong, Aug. 1998 global market events and on the 1985 Forex event, for the hypothesis advanced f…
We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period Jan 1996 - Jul 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globaliz…
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…
Study reveals conditions for neural networks to forget learned features.