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.
Paper introduces scalable neural architecture for solving NP-hard problems.
Tensor logic aims to unify AI types with scalable and transparent features.
New neural KB representation speeds up reasoning with large symbolic knowledge bases.
Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical d…
The paper discusses scalable learning for wireless data-driven systems.
Survey examines distillation methods for large language models.
SE-RRMs solve structured problems like Sudoku and ARC-AGI by enforcing symbol equivariance.
Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentio…
New method calibrates LLMs for safety-critical tasks with scalable Bayesian inference.
Fractured Sampling improves LLM reasoning efficiency by truncating CoT trajectories.
Enhances large language models' reasoning through simpler off-policy reinforcement learning.
A-NeSI scales approximate inference for probabilistic neurosymbolic learning.
uGMM-NN integrates probabilistic reasoning into neural networks.
KM-GPT automates IPD reconstruction from KM plots with high accuracy and scalability.
G1 uses RL to enhance LLMs' graph reasoning, improving performance on diverse tasks.
In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue. On one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we als…
Mid-training improves RL by identifying compact action abstractions.
PyFi uses adversarial agents to train VLMs on financial image understanding.
Study evaluates scalability and real-world impact of disentangled representations.
Novel method diagnoses large language models' reasoning abilities.
FinZero improves financial time series forecasting accuracy with multimodal modeling.
Formalizes weak and strong verification for LLMs, controlling errors without assumptions.
Federated framework learns causal states to predict counterfactuals without centralizing data.
Graph embedding learns low-dimensional representations for nodes in a graph and effectively preserves the graph structure. Recently, a significant amount of progress has been made toward this emerging research area. However, there are several fundamental problems that remain open. First, existing methods fail to preser…
Raven's Progressive Matrices are one of the widely used tests in evaluating the human test taker's fluid intelligence. Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models. Our empirical re…
This paper uses LLMs for causal discovery with active learning and dynamic scoring to improve efficiency and fairness.
New diffusion models improve counterfactual image generation with semantic control.
A new algorithm speeds up sparse regression for discovering equations from data.
The mutual information is a core statistical quantity that has applications in all areas of machine learning, whether this is in training of density models over multiple data modalities, in maximising the efficiency of noisy transmission channels, or when learning behaviour policies for exploration by artificial agents…
Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approximating kernel is selected, the quality of the finite sample estimator may be greatly affected by the choice of kernel. Furthermore, when direc…
INFUSER improves reasoning by co-evolving a generator and solver with adaptive curriculum.
A scalable graphical method is presented for selecting, and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is proceeded by construction of an information gra…
INFUSER improves reasoning by self-evolving with a generator and solver that co-learn from unstructured documents.
Study benchmarks LLMs in portfolio optimization tasks.
ResCP uses reservoir computing to create efficient, scalable time series prediction intervals.
Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference…
Survey of financial foundation models for diverse applications.
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices. With increasingly complex neural network architectures and heterogeneous device characteristics, finding a reasonable placement is extremely challenging even for do…
Natural language processing often involves computations with semantic or syntactic graphs to facilitate sophisticated reasoning based on structural relationships. While convolution kernels provide a powerful tool for comparing graph structure based on node (word) level relationships, they are difficult to customize and…
We address the problem of continual learning in multi-task Gaussian process (GP) models for handling sequential input-output observations. Our approach extends the existing prior-posterior recursion of online Bayesian inference, i.e.\ past posterior discoveries become future prior beliefs, to the infinite functional sp…
BCD Nets use variational inference to estimate DAGs with uncertainty.
When predictive models are used to support complex and important decisions, the ability to explain a model's reasoning can increase trust, expose hidden biases, and reduce vulnerability to adversarial attacks. However, attempts at interpreting models are often ad hoc and application-specific, and the concept of interpr…
Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in …
Unified framework for non-Euclidean CPD under scalable stochastic mirror descent.
The proliferation of automated inference algorithms in Bayesian statistics has provided practitioners newfound access to fast, reproducible data analysis and powerful statistical models. Designing automated methods that are also both computationally scalable and theoretically sound, however, remains a significant chall…
It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate an inference procedure for it. For this approach to be practical, it is important…
Neural networks are becoming a popular tool for solving many real-world problems such as object recognition and machine translation, thanks to its exceptional performance as an end-to-end solution. However, neural networks are complex black-box models, which hinders humans from interpreting and consequently trusting th…
CogScale benchmarks AI architectures for sequential processing.