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 on bit threads and their locking properties in holographic spacetimes.
Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little resemblance to our real-life experience in day-to-day interactions with the surround…
Deep-Lock secures DNN models with secret keys.
Lock-in, the escalating commitment of decision-makers to an ineffective course of action, has the potential to explain the large cost overruns in large scale transportation infrastructure projects. Lock-in can occur both at the decision-making level (before the decision to build) and at the project level (after the dec…
ESOP uses Bayesian optimization to find optimal lock-down schedules.
Due to space limitations, our submission "Source Separation and Clustering of Phase-Locked Subspaces", accepted for publication on the IEEE Transactions on Neural Networks in 2011, presented some results without proof. Those proofs are provided in this paper.
Training the deep convolutional neural network for computer vision problems is slow and inefficient, especially when it is large and distributed across multiple devices. The inefficiency is caused by the backpropagation algorithm's forward locking, backward locking, and update locking problems. Existing solutions for a…
Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent t…
Study on synchronization in financial markets with time delays.
Study finds TVL doesn't predict cryptocurrency returns.
To help understand the underlying mechanisms of neural networks (NNs), several groups have, in recent years, studied the number of linear regions of piecewise linear functions generated by deep neural networks (DNN). In particular, they showed that can grow exponentially with the number of network paramet…
Study on TVL computation in DeFi protocols, proposing verifiable metrics.
Paper tackles dynamic behavior of variable topology mechanisms, presenting new transition conditions.
New methods combine model predictions to avoid linear mixtures' limitations.
Backpropagation algorithm is indispensable for the training of feedforward neural networks. It requires propagating error gradients sequentially from the output layer all the way back to the input layer. The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully l…
Accelerates optimization in asynchronous systems with sparse updates.
Probabilistic programming aids in automatically dating ice cores, reducing manual error and uncertainty.
Hidden Markov Model helps track asymptomatic carriers in pandemic.
Stochastic gradient descent~(SGD) and its variants have attracted much attention in machine learning due to their efficiency and effectiveness for optimization. To handle large-scale problems, researchers have recently proposed several lock-free strategy based parallel SGD~(LF-PSGD) methods for multi-core systems. Howe…
Study factors affecting liquidity on decentralized exchanges, introducing new metrics.
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder the development of l…
MAC combines models without locking them, improving ensemble performance.
Framework mitigates overfitting in quantitative trading strategies.
Ghost mechanism explains abrupt learning in RNNs, revealing constraints on optimization landscapes.
This study analyzes the effects of lifting lockdowns on Brazil's COVID-19 spread.
Angular measurements are often modeled as circular random variables, where there are natural circular analogues of moments, including correlation. Because a product of circles is a torus, a d-dimensional vector of circular random variables lies on a d-dimensional torus. For such vectors we present here a class of graph…
Study analyzes impacts of COVID-19 on French forestry sector, finds mixed results in supply chain.
Proves Riemannian positive mass theorem with singularities.
OOM-RL uses financial market losses to align AI agents in autonomous systems.
We show that the bordism group of closed 3-manifolds with positive scalar curvature (psc) metrics is trivial by explicit methods. Our constructions are derived from scalar-flat K{ä}hler ALE surfaces discovered by Lock-Viaclovsky. Next, we study psc 4-manifolds with metric singularities along points and embedded circles…
We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. CYCLADES is asynchronous during shared model updates, and requires no memory locking mechanisms, similar to HOGWILD!-type algorithms. Unlike HOGWILD!, CYCLADES introduces no conflicts during the par…
Exchanges acquire excess processing capacity to accommodate trading activity surges associated with zero-sum high-frequency trader (HFT) "duels." The idle capacity's opportunity cost is an externality of low-latency trading. We build a model of decentralized exchanges (DEX) with flexible capacity. On DEX, HFTs acquire …
Backpropagation (BP) is the cornerstone of today's deep learning algorithms, but it is inefficient partially because of backward locking, which means updating the weights of one layer locks the weight updates in the other layers. Consequently, it is challenging to apply parallel computing or a pipeline structure to upd…
We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks. The HSIC bottleneck is an alternative to the conventional cross-entropy loss and backpropagation that has a number of distinct advantages. It mitigates exploding and vanishing gradients, resulting in the ability…
Extending methods first used by Casson, we show how to verify a hyperbolic structure on a finite triangulation of a closed 3-manifold using interval arithmetic methods. A key ingredient is a new theoretical result (akin to a theorem by Neumann-Zagier and Moser for ideal triangulations upon which HIKMOT is based) showin…
This work reveals a new scaling law for optimal design of multirotor aerial vehicles.
Social learning can make financial markets inefficient, but individual learning can fix this.
Study evaluates training programs for unemployed in Belgium using machine learning.
Constant Proportion Portfolio Insurance (CPPI) is an investment strategy designed to give participation in the performance of a risky asset while protecting the invested capital. This protection is however not perfect and the gap risk must be quantified. CPPI strategies are path-dependent and may have American exercise…
EagerPy simplifies writing code for multiple deep learning frameworks.
Study links public concern in Italy to financial markets worldwide.
Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which a…
KLD token adjusts supply based on macroeconomic debt index, creating deflationary effect.
Simplified Khovanov-Rozansky calculus for bipartite knots.
The paper identifies all link projections with isolate-region number one.
We introduce a local move on a link diagram named a region freeze crossing change which is close to a region crossing change, but not the same. We study similarity and difference between region crossing change and region freeze crossing change.
Training a neural network using backpropagation algorithm requires passing error gradients sequentially through the network. The backward locking prevents us from updating network layers in parallel and fully leveraging the computing resources. Recently, there are several works trying to decouple and parallelize the ba…
Regionalization is the task of dividing up a landscape into homogeneous patches with similar properties. Although this task has a wide range of applications, it has two notable challenges. First, it is assumed that the resulting regions are both homogeneous and spatially contiguous. Second, it is well-recognized that l…