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.
Deep learning quantifies butterfly phenotypes, validating evolutionary theory.
Model learns disease self-representations for drug repositioning.
Development of interpretable machine learning models for clinical healthcare applications has the potential of changing the way we understand, treat, and ultimately cure, diseases and disorders in many areas of medicine. These models can serve not only as sources of predictions and estimates, but also as discovery tool…
Transformers capture combinatorial tasks with bounded error and logarithmic sample dependence.
CCVAE captures label characteristics in VAEs for better representation learning.
Machine learning improves joint default assessment by capturing non-linear dependencies.
We investigate the ability of popular flow based methods to capture tail-properties of a target density by studying the increasing triangular maps used in these flow methods acting on a tractable source density. We show that the density quantile functions of the source and target density provide a precise characterizat…
Diffusion Transformer captures spatial-temporal dependencies in sequential data.
As one of the important functions of the intelligent transportation system (ITS), supply-demand prediction for autonomous vehicles provides a decision basis for its control. In this paper, we present two prediction models (i.e. ARLP model and Advanced ARLP model) based on two system environments that only the current d…
Model shows how Ethereum can capture MEV from block construction, but centralization remains a concern.
Automates hair color digitization using imaging and deep learning.
Action-bisimulation learns long-horizon controllability for reinforcement learning.
Study on ion travel time on curved surfaces.
Geometric method captures rare topics and temporal alignment in co-author networks.
IRM fails to capture natural invariances on simple problems.
LSTM improves cross-network recommendations by capturing user preference changes and irregular time intervals.
Lattice formulation captures Atiyah-Patodi-Singer index.
Enhanced rotation prediction improves SSL models by capturing both shape and texture information.
A new model captures variability in time series data.
Study uses LLMs to automate data insights discovery.
Estimates population size using capture-recapture designs with binary indicators.
Auto-regressive diffusion models improve capturing conditional dependence in data.
This paper examines to what degree current deep learning architectures for image caption generation capture spatial language. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the…
FluxLayer solves cross-chain liquidity fragmentation for better MEV capture.
Model captures SPX and VIX volatility surfaces and skew-stickiness ratio.
One of the main challenges of deep learning tools is their inability to capture model uncertainty. While Bayesian deep learning can be used to tackle the problem, Bayesian neural networks often require more time and computational power to train than deterministic networks. Our work explores whether fully Bayesian netwo…
A new model captures complex event data using attention and Fourier kernels.
We seek to better understand the difference in quality of the several publicly released embeddings. We propose several tasks that help to distinguish the characteristics of different embeddings. Our evaluation of sentiment polarity and synonym/antonym relations shows that embeddings are able to capture surprisingly nua…
A new neural network captures and explains trajectory patterns.
Proposes NDIG model to capture bitcoin volatility and option pricing.
A body of recent work in modeling neural activity focuses on recovering low-dimensional latent features that capture the statistical structure of large-scale neural populations. Most such approaches have focused on linear generative models, where inference is computationally tractable. Here, we propose fLDS, a general …
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We propose Mahé, a novel approach to provide Model-agnostic hierarchical éxplanations of how powerful machine learning models, such as deep ne…
Bayesian model captures mean and variance of response variables.
In a market system, regulations are designed to prevent or rectify market failures that inhibit fair exchange, such as monopoly or transactions with hidden costs. Because regulations reduce profits to those possessing unfair advantage, these advantaged corporations (whether individuals, companies, or other collective o…
New method estimates animal density using acoustic data, accounting for unknown call identities.
Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood. Recent theoretical work suggests that the concept of space can be grounded by capturing invariants induced by the structure of space in an ag…
Representation learning is a central challenge across a range of machine learning areas. In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems. Most prior work on representation learning has focused on gene…
Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood. Recent theoretical works suggest that the Euclidean structure of space induces invariants in an agent's raw sensorimotor experience. We hypot…
Method captures fabric mechanics from depth images without expensive setups.
A novel hypergraph partitioning method using tensor eigenvalue decomposition captures super-dyadic interactions.
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph…
ResGCN detects anomalies in attributed networks by capturing sparsity and nonlinearity.
Self-supervised learning of visual semantics in image games.
Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose deep spatio-temporal models and extreme value theo…
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furtherm…
A novel method captures both micro- and macro-dynamics in temporal networks.
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence - both semantic and syn…
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of generator distributions. As the generators are partially shared between the modeling of different true data distributions, shared ones ca…