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 portfolio selection with exogenous and endogenous transaction costs using deep learning.
Study multi-agent RL in OTC markets, learning from agents' interactions.
DL2 uses deep learning to optimize resource allocation in DL clusters.
Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keyword-based queries. We propose a reinforcement-learning-driven translation model framework able to 1) learn the translation from NL expressions to que…
Optimizes investment model using LSTM for better risk control.
Proposes a method to quantify the reliability of salient regions in deep learning models using p-values.
Acceleration of machine learning research in healthcare is challenged by lack of large annotated and balanced datasets. Furthermore, dealing with measurement inaccuracies and exploiting unsupervised data are considered to be central to improving existing solutions. In particular, a primary objective in predictive model…
Paper develops a hybrid DNN approach for RUL prediction with adaptive drift.
Framework prevents data leakage in mobile cloud DNNs.
Machine-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. Often viewed as a black-box, it is non-obvious to determine when a model will make a safe decision and when it will make an erron…
Refined BN-S model improves crude oil hedging with machine learning.
This work tackles maintenance planning with deep reinforcement learning under uncertainty.
In the domain of algorithmic music composition, machine learning-driven systems eliminate the need for carefully hand-crafting rules for composition. In particular, the capability of recurrent neural networks to learn complex temporal patterns lends itself well to the musical domain. Promising results have been observe…
PTBCC improves accuracy in multi-class annotation aggregation by learning from prototype confusion matrices.
Extends return extrapolation to nonlinear, asymmetric functions under stochastic volatility.
Neural networks struggle with TSP beyond small instances, requiring new approaches.
We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.
In e-commerce, content quality of the product catalog plays a key role in delivering a satisfactory experience to the customers. In particular, visual content such as product images influences customers' engagement and purchase decisions. With the rapid growth of e-commerce and the advent of artificial intelligence, tr…
The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, assessing the performance of such generative models is notoriously difficult. Metrics that are typically used to assess the performance of such generative models are the perc…
We present a machine learning based method for noise classification using a low-power and inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine and k-nearest neighbors) for noise classification. We evaluate…
Machine learning improves financial stress testing in Indian markets.
A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the…
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine l…
Heuristic algorithms such as simulated annealing, Concorde, and METIS are effective and widely used approaches to find solutions to combinatorial optimization problems. However, they are limited by the high sample complexity required to reach a reasonable solution from a cold-start. In this paper, we introduce a novel …
Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as -greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish the well …
evo-RL combines evolutionary computation with reinforcement learning for better adaptability.
Hybrid ML ensemble predicts market risk and generates alpha.
Study improves self-driving safety in dynamic environments.
New method uses product embeddings to predict bundle success.
Machine learning identifies potential drugs for COVID-19.
Robot science discovers new materials faster.
Interpretability of ML models improves healthcare decisions.
Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking based feature selection by any criterion, we …
GQML uses symmetries from representation theory to improve quantum machine learning.
This paper studies recursive ensembles driven by Fibonacci updates, improving learning dynamics.
SAMS-VAE models cellular perturbations using sparse additive mechanisms.
We present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes. The robot explores its environment both via interactive learning and goal…
Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by non-specific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically cha…
Probabilistic deep learning uses neural networks and models to handle uncertainty.
Improves deep learning robustness by considering task and model.
Deep-RLS uses deep learning to improve PCA for better source separation.
How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical w…
Deep learning methods improve overlapping speaker separation across languages and noise.
SDF adapts Deep Forest for evolving data streams with active learning.
Deep SSMs use neural networks to identify complex systems.
This paper presents a basic property of region dividing of ReLU (rectified linear unit) deep learning when new layers are successively added, by which two new perspectives of interpreting deep learning are given. The first is related to decision trees and forests; we construct a deep learning structure equivalent to a …
The great success of deep learning shows that its technology contains profound truth, and understanding its internal mechanism not only has important implications for the development of its technology and effective application in various fields, but also provides meaningful insights into the understanding of human brai…
DSCF-Net learns deep features for clustering with robustness and locality preservation.