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.
MedGraph learns patient visit embeddings from EMRs, capturing both attributes and temporal sequences.
A new model for sequential memory using temporal predictive coding.
Improved topic modeling captures temporal relationships in speech.
Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticit…
New model captures patient-level EHR data efficiently.
A new SNN learning algorithm for energy-efficient VLSI circuits.
We explore whether useful temporal neural generative models can be learned from sequential data without back-propagation through time. We investigate the viability of a more neurocognitively-grounded approach in the context of unsupervised generative modeling of sequences. Specifically, we build on the concept of predi…
TNC learns time series representations by leveraging temporal neighborhoods.
We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic tr…
We model GitHub interactions as a temporal knowledge graph for software engineering questions.
Spatial separation of suspended particles based on contrast in their physical or chemical properties forms the basis of various biological assays performed on lab-on-achip devices. To electronically acquire this information, we have recently introduced a microfluidic sensing platform, called Microfluidic CODES, which c…
Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well…
HECT tests climate model outputs for reproducibility.
Enhances understanding of patient healthcare journeys using self-attention.
Temporal information impacts only a fraction of time series datasets, skewing benchmark evaluations.
New training algorithm enhances SNNs for temporal signal processing.
We propose and analyze a novel framework for learning sparse representations, based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach provides a flexible framework for incorporating feature similarity or temporal information present in data sets, via non-parametric kernel sm…
Paper tackles temporal overfitting in wind power curve modeling.
DGRCL integrates dynamic and static graph relations for financial market prediction.
Paper proposes a novel approach to improve spatiotemporal precipitation forecasts.
We describe the submission of the Quo Vadis team to the Traffic4cast competition, which was organized as part of the NeurIPS 2019 series of challenges. Our system consists of a temporal regression module, implemented as 2d convolutions, augmented with spatio-temporal biases. We have found that using biases i…
A3T-GCN improves traffic forecasting by capturing spatial and temporal dependencies.
Deep learning predicts Bitcoin spot price movements from order books.
A new model uses neural networks to efficiently learn multivariate temporal point processes.
Conventional modeling approaches have found limitations in matching the increasingly detailed neural network structures and dynamics recorded in experiments to the diverse brain functionalities. On another approach, studies have demonstrated to train spiking neural networks for simple functions using supervised learnin…
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate s…
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record. Accurately capturing the list of medications currently taken by a …
Paper tackles estimating initial conditions of spatio-temporal processes from sparse data.
This paper presents, evaluates, and discusses a new software tool to automatically build Dynamic Bayesian Networks (DBNs) from ordinary differential equations (ODEs) entered by the user. The DBNs generated from ODE models can handle both data uncertainty and model uncertainty in a principled manner. The application, na…
This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i.e. a marker. In this paper, we describe the factorial marked point processes w…
LVTINO improves high-definition video restoration with consistent temporal details.
Deep learning framework detects emotions from EEG data.
Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification…
Model learns disentangled representations from natural videos.
CODE learns ODE dynamics from sparse data, outperforming neural and kernel methods.
We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification. We propose to adopt kernel machines and employ graph kernels that define a kernel dot product between two graphs. This enables us to take advantage of spatio-temporal information to captu…
There is a consensus that human and non-human subjects experience temporal distortions in many stages of their perceptual and decision-making systems. Similarly, intertemporal choice research has shown that decision-makers undervalue future outcomes relative to immediate ones. Here we combine techniques from informatio…
Predictive Sparse Manifold Transform learns dynamic video sequences.
Hybrid model improves geopolitical conflict forecasting.
New model recommends stocks considering individual preferences and diversification.
This paper reviews and benchmarks DVAEs for sequential data.
New method models MTPP without predefined intensity functions.
Paper proposes HGTAN for better stock trend prediction.
Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp. Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future. This pa…
Paper proposes a CNN-based method for estimating intra frame bits and quality.
Online DEM improves tracking of latent states in dynamic systems.
Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity resea…
T2FSNN improves deep SNNs by reducing spikes and latency.