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 intrinsic motivation for synergistic tasks in reinforcement learning.
Proposes PEID for analyzing synergistic causation in complex systems.
Enhances graph classification with multiple graphs.
Paper proposes a new framework to mine synergistic formulaic alphas for better stock trend forecasting.
Learning representations with diversified information remains as an open problem. Towards learning diversified representations, a new approach, termed Information Competing Process (ICP), is proposed in this paper. Aiming to enrich the information carried by feature representations, ICP separates a representation into …
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural languag…
Identification of informative variables in an information system is often performed using simple one-dimensional filtering procedures that discard information about interactions between variables. Such approach may result in removing some relevant variables from consideration. Here we present an R package MDFS (MultiDi…
This paper proposes a method to gain extra supervision via multi-task learning for multi-modal video question answering. Multi-modal video question answering is an important task that aims at the joint understanding of vision and language. However, establishing large scale dataset for multi-modal video question answeri…
Unsupervised domain adaptation aims to transfer and adapt knowledge learned from a labeled source domain to an unlabeled target domain. Key components of unsupervised domain adaptation include: (a) maximizing performance on the target, and (b) aligning the source and target domains. Traditionally, these tasks have eith…
Study compares DL models for medical image segmentation, finds synergistic ensemble strategies improve performance.
Study shows corporate governance improves stock liquidity with noise traders' participation.
A method for collecting human supervision that combines rules and instance labels.
TLMG4Eth combines language and graph models for Ethereum fraud detection.
A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital f…
We propose a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local traj…
Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computat…
We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regressio…
Kriformer uses graph transformers to estimate data in sparse sensor areas.
Isometry pursuit identifies orthonormal submatrices from wide matrices.
A measure of neural complexity quantifies how hard it is to access information across neurons.
In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT). NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more…
SPINN optimizes neural network inference on devices and cloud.
New method evaluates multiple social disparities using machine learning.
Robust optimization and statistical robustness improve robot navigation policies.
Proposes using continuum percolation to analyze data manifolds and improve generative models.
Proposes a new batch selection method for multi-label classification.
Motivated by applications in protein function prediction, we consider a challenging supervised classification setting in which positive labels are scarce and there are no explicit negative labels. The learning algorithm must thus select which unlabeled examples to use as negative training points, possibly ending up wit…
A method to improve LLMs by automating the construction of a mixture of expert prompts.
We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform…
Theoretical analysis of data quality and synergies in LLMs.
A new diffusion sampling method combines Krylov subspace and diffusion models for faster and more efficient inverse problems.
SURGIN uses generative models to infer subsurface flow data efficiently.
A new method optimizes diffusion models with recursive likelihood ratios.
Study improves drug synergy prediction using ensemble learning.
NDM incorporates geometric structure into neural networks for better optimization and interpretability.
PhysVarMix predicts diverse urban trajectories with physics constraints.
Hypersolvers enable fast continuous-depth models for practical applications.
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture were n…
Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalit…
AntMan compresses RNNs for faster inference with minimal accuracy loss.
The ever-increasing demand from mobile Machine Learning (ML) applications calls for evermore powerful on-chip computing resources. Mobile devices are empowered with heterogeneous multi-processor Systems-on-Chips (SoCs) to process ML workloads such as Convolutional Neural Network (CNN) inference. Mobile SoCs house sever…
Data mining revealed a cluster of economic, psychological, social and cultural indicators that in combination predicted corruption and wealth of European nations. This prosperity syndrome of self-reliant citizens, efficient division of labor, a sophisticated scientific community, and respect for the law, was clearly di…
This dissertation advances scalable Gaussian processes using iterative methods and pathwise conditioning.
Framework evaluates the impact of prior knowledge in deep learning models.
We consider the "partial information decomposition" (PID) problem, which aims to decompose the information that a set of source random variables provide about a target random variable into separate redundant, synergistic, union, and unique components. In the first part of this paper, we propose a general framework for …
The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and specialized hardware. Despite the indisputable success, the reliance on huge amounts of labeled data and specialized hardware can be a limiti…
DARL uses DDPMs to generate synthetic market crash scenarios for robust portfolio optimization.
New method improves autofocus in CBCT scans by 93%.