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.
New memory allocation scheme improves image generation performance.
This paper tackles federated incremental learning with dynamic memory allocation for improved model performance in non-IID data.
This study reviews techniques to estimate volatility and price Variance Swaps.
NPAS trains neural networks with a fixed parameter budget, improving performance and compactness.
An ideal cognitively-inspired memory system would compress and organize incoming items. The Kanerva Machine (Wu et al, 2018) is a Bayesian model that naturally implements online memory compression. However, the organization of the Kanerva Machine is limited by its use of a single Gaussian random matrix for storage. Her…
Generative profiling improves real-time task timing for varied resource contexts.
Hopfield networks outperform deep-learning methods in portfolio optimization.
SliceOut speeds up deep learning training without sacrificing accuracy.
A new algorithm reduces memory usage for deep learning models.
Study capacity constraints in continual learning with a simple model.
A new memory system handles non-stationary environments by self-sizing and retaining memories.
Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide interest for many applications. Previous work has developed an O(1) Metropolis-Hastings sampling method for each token. However, the performance is far from being optimal due to random accesses to the parameter matrices and fr…
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to support a real-time execution of the considered DL model at the IoT unit level, DL sol…
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…
Demand outstrips available resources in most situations, which gives rise to competition, interaction and learning. In this article, we review a broad spectrum of multi-agent models of competition (El Farol Bar problem, Minority Game, Kolkata Paise Restaurant problem, Stable marriage problem, Parking space problem and …
Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low precision networks have even shown to be more robust to adversarial attacks. However, typical implementations of low precision DNNs use unifor…
TASO optimizes CNN models for memory-constrained devices.
Deep RL optimizes US stock allocations with better performance.
The Brazilian court system is currently the most clogged up judiciary system in the world. Thousands of lawsuit cases reach the supreme court every day. These cases need to be analyzed in order to be associated to relevant tags and allocated to the right team. Most of the cases reach the court as raster scanned documen…
Paper uses RL to solve constrained combinatorial optimization problems.
Optimizes investment model using LSTM for better risk control.
A lightweight model predicts IT system KPIs from historical data.
FLoE adapts LLMs by selectively deploying LoRA adapters based on layer importance and task requirements.
Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big corpora that are best analyzed in parallel and distributed computational environments. Indeed…
Stochastic variational inference (SVI), the state-of-the-art algorithm for scaling variational inference to large-datasets, is inherently serial. Moreover, it requires the parameters to fit in the memory of a single processor; this is problematic when the number of parameters is in billions. In this paper, we propose e…
Hybrid LSTM-PPO optimizes dynamic portfolios with better performance.
Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general ca…
We study soft persistence (existence in subsequent temporal layers of motifs from the initial layer) of motif structures in Triangulated Maximally Filtered Graphs (TMFG) generated from time-varying Kendall correlation matrices computed from stock prices log-returns over rolling windows with exponential smoothing. We ob…
The paper optimizes DIA purchase policies using lifecycle models and asset allocation.
Study optimizes resource allocation in noisy systems for better control.
The paper proposes an asset allocation strategy using the Sortino ratio for better performance.
Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large size problems. Recently different ma…
New method prevents deep learning models from forgetting past tasks.
This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.
This paper examines allocation mechanisms in markets with transfer costs, showing how these costs affect economic efficiency.
Paper introduces a new method for allocating capital based on risk measures from ruin theory.
The aims of this study are twofold. First, we consider an optimal risk allocation problem with non-convex preferences. By establishing an infimal representation for distortion risk measures, we give some necessary and sufficient conditions for the existence of optimal and asymptotic optimal allocations. We will show th…
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection…
Framework uses hindsight regret to audit marketing budget allocations.
New method allocates capital based on tail central moments for financial risk assessment.
Implicit models can match or exceed explicit models with more test-time compute.
The paper explores capital allocation using Euler formula with VaR and ES, revealing non-monotonicity and providing estimation methods.
Capital allocation principles are used in various contexts in which a risk capital or a cost of an aggregate position has to be allocated among its constituent parts. We study capital allocation principles in a performance measurement framework. We introduce the notation of suitability of allocations for performance me…
The financial crisis showed the importance of measuring, allocating and regulating systemic risk. Recently, the systemic risk measures that can be decomposed into an aggregation function and a scalar measure of risk, received a lot of attention. In this framework, capital allocations are added after aggregation and can…
The paper analyzes insurance pricing and capital allocation in imperfect markets.
Optimal resource allocation in censored semi-bandits with unknown thresholds.
New risk-sharing rules induced by capital allocation principles.
We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem to adversarial sce…