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.
MAC combines models without locking them, improving ensemble performance.
SBT model uses randomized sharding and sub-models to improve Bayesian Additive Regression Trees.
Predicting smartphone users activity using WiFi fingerprints has been a popular approach for indoor positioning in recent years. However, such a high dimensional time-series prediction problem can be very tricky to solve. To address this issue, we propose a novel deep learning model, the convolutional mixture density r…
We propose doubly nested network(DNNet) where all neurons represent their own sub-models that solve the same task. Every sub-model is nested both layer-wise and channel-wise. While nesting sub-models layer-wise is straight-forward with deep-supervision as proposed in \cite{xie2015holistically}, channel-wise nesting has…
DVERGE diversifies adversarial vulnerabilities to enhance robust ensemble models.
Fictitious play with reinforcement learning is a general and effective framework for zero-sum games. However, using the current deep neural network models, the implementation of fictitious play faces crucial challenges. Neural network model training employs gradient descent approaches to update all connection weights, …
Improves parallel deep model performance by restructuring and pruning.
With an eye toward understanding complexity control in deep learning, we study how infinitesimal regularization or gradient descent optimization lead to margin maximizing solutions in both homogeneous and non-homogeneous models, extending previous work that focused on infinitesimal regularization only in homogeneous mo…
A new framework for efficient Bayesian network inference.
SuperNet speeds up neural network ensembling by training a single DNN for various phases.
Hidden regular variation is a sub-model of multivariate regular variation and facilitates accurate estimation of joint tail probabilities. We generalize the model of hidden regular variation to what we call hidden domain of attraction. We exhibit examples that illustrate the need for a more general model and discuss de…
Multi-step-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multi-step-ahead…
Optimizes biomanufacturing processes with a new digital twin calibration method.
New methods for scalable inference in modular models with misspecified sub-models.
Word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. Training embeddings over huge corpora is computationally expensive because the input is typically sequentially processed and parameters are synchronously updated. Distr…
This paper introduces a deep learning ensemble forecasting model using Dirichlet process.
Efficiently builds diverse sub-model ensembles for robust self-supervised learning.
We present a set of models relevant for predicting various aspects of intra-day trading volume for equities and showcase them as an ensemble that projects volume in unison. We introduce econometric methods for predicting total and remaining daily volume, intra-day volume profile (u-curve), close auction volume and spec…
This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (or Gaussian process regression) is often considered to tackle such a problem but the method suffers from its computational b…
Large models collapse epistemic uncertainty, challenging traditional wisdom.
New method for analyzing learning dynamics in singular models.
In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code. Ludwig implements a novel approach to deep learning model building based on two main abstractions: data types and declarative confi…
Federated Learning is introduced to protect privacy by distributing training data into multiple parties. Each party trains its own model and a meta-model is constructed from the sub models. In this way the details of the data are not disclosed in between each party. In this paper we investigate the model interpretation…
This paper improves ASR performance by aligning frames more accurately.
Unified model for market dynamics, linking price and order flow.
This paper tackles learning functions on manifolds using parallel distributed learning.
This work proposes the Bregman-Tweedie classification model and analyzes the domain structure of the extended exponential function, an extension of the classic generalized exponential function with additional scaling parameter, and related high-level mathematical structures, such as the Bregman-Tweedie loss function an…
This study evaluates the robustness of transformation-based ensemble defense against evasion attacks.
Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILEs. In this study, we introduce a new robust architecture, Generative Examination Networks GEN, base…
Estimates hybrid dynamical systems with polynomial expansions and Markovian switching.
This paper proposes a representational model for grid cells. In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector. Each component of the vector is a unit or a cell. The mode…
RankNet forecasts car racing positions with improved accuracy and stability.
Image of an entity can be defined as a structured and dynamic representation which can be extracted from the opinions of a group of users or population. Automatic extraction of such an image has certain importance in political science and sociology related studies, e.g., when an extended inquiry from large-scale data i…
A new method for high-dimensional functional regression reduces multicollinearity and improves interpretability.
Paper addresses online identification and clustering for mixed linear regression models.
A real-time federated neural architecture search approach reduces costs and improves performance.
The paper analyzes convergence rates of softmax gating in MoE models.
Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.
CoCoAFusE fuses expert predictions to model complex patterns with interpretability and uncertainty.
Paper addresses global convergence of MLR estimation under weak data conditions.
Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange of India, over a period of four …
Automated method creates compact chemical models from detailed ones, reducing complexity and improving accuracy.
TMLE improves IPM estimation for ecological population dynamics.