The preceding three decades have seen the emergence, rise, and proliferation of machine learning (ML). From half-recognised beginnings in perceptrons, neural nets, and decision trees, algorithms that extract correlations (that is, patterns) from a set of data points have broken free from their origin in computational c…
On-device research index
arXiv research
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.
168,695 papers · 148 categories
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48 results for “realism”
The paper discusses quantifying realism in generated images.
problem Designing functions to reliably distinguish realistic data from unrealistic data.
method Drawing on insights from algorithmic information theory, the paper introduces the notion of a universal critic.
result A good generative model alone is insufficient to solve the problem of realism quantification.
New insights into image compression trade-offs with private randomness.
problem Trade-off between compression rate and perceptual quality in image compression.
method Characterization of rate-distortion trade-off with private randomness under different realism constraints.
result Encoder private randomness is not useful if compression rate is below source entropy, even with limited common and decoder private randomness.
Develops a new model for controllable and realistic traffic simulation.
problem Lack of models that offer both controllability and realism in traffic simulation.
method Guided Conditional Diffusion (CTG) model using diffusion modeling and differentiable logic.
result Improves controllability-realism tradeoff over strong baselines.
With success on controlled tasks, generative models are being increasingly applied to humanitarian applications [1,2]. In this paper, we focus on the evaluation of a conditional generative model that illustrates the consequences of climate change-induced flooding to encourage public interest and awareness on the issue.…
The paper evaluates and improves uncertainty estimates in neural networks for safety-critical applications.
problem Quantifying uncertainty in neural networks for safety-critical systems.
method Proposes a statistical test for evaluating uncertainty realism in neural networks and transfers a classification architecture to image-to-image tasks.
result The variational U-Net architecture significantly improves uncertainty realism in image-to-image tasks compared to a plain model.
Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systemscs.AI
LLM-based trading systems vary in execution realism and reproducibility.
problem LLM-based trading systems vary in execution realism and reproducibility.
method Reproducibility audit of 30 trade-relevant primary studies.
result LLM-based trading systems vary in execution realism and reproducibility.
New coding theorem shows achievable rate matches theoretical limit.
problem Unknown existence of encoders and decoders for RDPF.
method Used stochastic, variable-length codes to prove RDPF achievable.
result Achievable rate matches theoretical rate-distortion-perception function.
We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populatio…
CounteRGAN generates realistic, actionable counterfactuals for machine learning models.
problem Creating realistic and actionable counterfactuals for machine learning models.
method Applying Residual GANs to improve counterfactual realism and actionability.
result CounteRGAN produces counterfactuals with improved realism and actionability, achieving real-time applicability.
Examines optimal risk sharing with realistic risk attitudes, finding risk seeking in certain subdomains.
problem Optimal risk sharing with empirically realistic risk attitudes.
method Allows for risk-seeking agents, generalizes expected utility, and uses counter-monotonic improvement theorem.
result First empirical results on optimal risk sharing with realistic risk attitudes.
Extends driving model to control agent behavior in simulations.
problem Simulate realistic driving behavior for autonomous systems.
method Introduces Control-ITRA method to influence agent behavior through waypoint assignment and target speed modulation.
result Demonstrates controllable, infraction-free trajectories while preserving realism.
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustnessq-fin.TR
This paper uses CGANs to simulate and improve trading agent performance in limit order books.
problem Improving trading agent performance in limit order book environments.
method Investigates conditional generative models (CGANs) for order book simulation and adversarial attacks to enhance realism and robustness.
result CGANs can be improved to better simulate real market conditions and are more robust to adversarial attacks.
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneously trained to assess the realism of the agent's samples, either uncond…
Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.
problem Challenging traditional concepts of cause-effect relations in quantum mechanics.
method Introducing a general framework to estimate causal influences without interventions or classical/quantum assumptions.
result Every pure bipartite entangled state violates classical bounds on causal influence, negating the idea that Bell inequalities are the only signature of incompatibility.
Data assimilation for subsurface flow using latent diffusion models shows that ensemble Kalman methods may overestimate posterior uncertainty, while Monte Carlo sampling is more reliable.
problem Data assimilation for subsurface flow
method Ensemble Kalman smoother and Markov chain Monte Carlo sampling
result Monte Carlo sampling is more reliable than ensemble Kalman methods
DiffVolume generates realistic volume snapshots for LOBs.
problem Generating high-dimensional volume snapshots in LOBs is challenging.
method Conditional Diffusion model for volume generation.
result DiffVolume outperforms in realism, counterfactual generation, and downstream prediction.
SFAG generates realistic financial data that passes trading tests.
problem Financial generative models often produce unrealistic and unstable trading outcomes.
method Introduces SFAG, a GAN variant that aligns stylized facts and optimizes with adversarial loss.
result SFAG generates synthetic data that preserves stylized facts and supports robust trading strategies.
Diffusion-GAN uses diffusion to improve GAN training stability and realism.
problem Stability and realism issues in training GANs.
method Diffusion-GAN employs a forward diffusion chain to generate Gaussian-mixture distributed instance noise, with adaptive diffusion process and timestep-dependent discriminator.
result Diffusion-GAN produces more realistic images with higher stability and data efficiency.
SocialInteractionGAN generates realistic human interactions from low-dimensional data.
problem Generating realistic human interactions from limited data.
method Adversarial architecture with a recurrent encoder-decoder generator and dual-stream discriminator.
result SocialInteractionGAN produces high-quality action sequences of interacting people.
Generative Adversarial Networks simulate realistic market interactions.
problem Lack of agent-level historical data limits market simulation realism.
method Conditional Generative Adversarial Networks (CGANs) trained on real data.
result CGAN-based synthetic market generator outperforms previous methods in market responsiveness and realism.
On the sensitivity of machine-learned probabilistic weather forecast models to scale-aware scoring rulesphysics.ao-ph
Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
problem Improving the accuracy of machine-learned probabilistic weather forecasts.
method Comparison of scoring rules (CRPS, fair global energy score, graph energy score) and analysis of their impact on forecast field spectra.
result Scale-awareness improves forecast realism, particularly in the tropics.
Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an altern…
Data augmentation is essential for medical research to increase the size of training datasets and achieve better results. In this work, we experiment three GAN architectures with different loss functions to generate new brain MRIs. The results show the importance of hyperparameter tuning and the use of mini-batch simil…
The history of research in finance and economics has been widely impacted by the field of Agent-based Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of physics and chemistry for example, the field of ACE has also received…
GUIDE-VAE generates user-guided data with improved realism and performance.
problem Generating data points for multi-user datasets while considering user information.
method Conditional generative model that integrates user embeddings and a pattern dictionary-based covariance composition.
result GUIDE-VAE outperforms conventional VAEs in multi-user settings, especially under data imbalance.
Optimizes credit index option hedging with reinforcement learning.
problem Finding the best strategy for hedging credit index options.
method Applied reinforcement learning with TRVO algorithm in a realistic setting.
result The derived hedging strategy outperforms traditional methods.
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion modelscs.LG
Study flaws in generative model evaluation metrics, especially for diffusion models.
problem Flaws in existing metrics for evaluating generative models, particularly for diffusion models.
method Systematic study of generative models, human perception experiments, and analysis of feature extractors.
result State-of-the-art perceptual realism of diffusion models is not reflected in commonly reported metrics.
DeDUCE efficiently generates realistic counterfactuals for image classifiers.
problem Generating accurate counterfactual explanations for large image classifiers.
method Developed a new algorithm using spectral normalization to efficiently generate counterfactuals.
result Our algorithm consistently produces counterfactuals closer to the original inputs with comparable realism.
Image normalization is a critical step in medical imaging. This step is often done on a per-dataset basis, preventing current segmentation algorithms from the full potential of exploiting jointly normalized information across multiple datasets. To solve this problem, we propose an adversarial normalization approach for…
Generative Adversarial Networks (GANs) can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different ob…
Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via co…
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing. Furthermore, simulation is important for validation of hand-coded trading strategies and for testing hypotheses about market structure. A challenge, however, concerns the robus…
Machine learning improves financial stress testing in Indian markets.
problem Conventional stress testing limitations in Indian financial markets.
method Dimensionality reduction, latent factor modeling, Variational Autoencoders, Monte Carlo simulation.
result Improved flexibility, robustness, and realism in financial stress testing.
New framework identifies causal models with arbitrary interventions, improving realism.
problem Identify causal models with realistic interventions.
method Theoretical framework for identifying causal models with arbitrary interventions.
result Identify causal models with arbitrary interventions, up to a higher-level abstraction.
FRAME (Filters, Random fields, And Maximum Entropy) is an energy-based descriptive model that synthesizes visual realism by capturing mutual patterns from structural input signals. The maximum likelihood estimation (MLE) is applied by default, yet conventionally causes the unstable training energy that wrecks the gener…
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize imag…
This paper presents an evaluation framework that attempts to quantify the "degree of realism" of simulated financial time series, whatever the simulation method could be, with the aim of discover unknown characteristics that are not being properly reproduced by such methods in order to improve them. For that purpose, t…
Pseudo rehearsal uses non-photo-realistic images to save resources without sacrificing performance.
problem Catastrophic forgetting in neural networks when learning new tasks.
method Synthetically generate non-photo-realistic images to rehearse previous tasks.
result Non-photo-realistic images can be used for rehearsal without sacrificing performance and significantly reduce resource consumption.
Risk-averse reinforcement learning optimizes option hedging.
problem Optimizing option hedging under risk aversion and realistic market conditions.
method Applied Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment.
result The derived hedging strategy outperforms Black & Scholes and is robust to market variations.
Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all rel…
Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musical…
Escalation, timing and severity of insurgent and terrorist events: Toward a unified theory of future threatsphysics.soc-ph
I present a unified discussion of several recently published results concerning the escalation, timing and severity of violent events in human conflicts and global terrorism, and set them in the wider context of real-world and cyber-based collective violence and illicit activity. I point out how the borders distinguish…
Adjoint Matching improves flow and diffusion models with reward fine-tuning.
problem Improving generative models with reward fine-tuning.
method Casting reward fine-tuning as stochastic optimal control (SOC) and enforcing a specific noise schedule.
result Adjoint Matching outperforms existing SOC algorithms.
CriticSMC improves planning efficiency in constrained environments.
problem Planning with hard constraints in dynamic environments.
method Sequential Monte Carlo with learned heuristic factors.
result CriticSMC reduces collision rates with low computational cost.
We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done…
Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a \emph{distribution} of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector,…
Stylized Facts and Market Microstructure: An In-Depth Exploration of German Bond Futures Marketq-fin.ST
Study reveals stylized facts in German bond futures markets.
problem Understanding market dynamics in German bond futures.
method Analyzed tick-by-tick data of four German bond futures contracts.
result Uncovered commonalities and unique characteristics across different futures.