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.
Proposes RDASS for better Korean text summarization evaluation.
Automatic question generation is an important problem in natural language processing. In this paper we propose a novel adaptive copying recurrent neural network model to tackle the problem of question generation from sentences and paragraphs. The proposed model adds a copying mechanism component onto a bidirectional LS…
Transforms web content for better visibility in AI-driven search engines.
Steady progress has been made in abstractive summarization with attention-based sequence-to-sequence learning models. In this paper, we propose a new decoder where the output summary is generated by conditioning on both the input text and the latent topics of the document. The latent topics, identified by a topic model…
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to multi-turn dialogue by modifying the state-of-the-art hredGAN architecture. To achieve this, we introduce an additional input modality into the encoder and decoder of hredGAN to capture other attributes such a…
System detects financial misinformation and generates clear explanations.
Reward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions. Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and…
New framework assesses LM uncertainty without thresholding.
Automated system extracts medication regimens from medical conversations.
In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization. Through jointly attending to topics and word-level alignment, o…
Automatic summarization of natural language is a current topic in computer science research and industry, studied for decades because of its usefulness across multiple domains. For example, summarization is necessary to create reviews such as this one. Research and applications have achieved some success in extractive …
Paper introduces methods to automatically generate SOAP notes from patient-physician conversations.
We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simpl…
Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses. These issues can attributed to reasons including (1) short-range model architectures that capture limited temporal dependencies, (2) limitations of the maximum likel…
Generative models' evaluation scores can be misleading, leading to inflated grades.
Improves score estimation for noised targets using known clean scores.
This work improves likelihood of score-based diffusion ODEs using high-order denoising score matching.
Study compares multivariate scoring rules for distribution forecasts.
A new method improves data generation quality by correcting score mismatches.
The rapid growth of text data has motivated the development of machine-learning based automatic text summarization strategies that concisely capture the essential ideas in a larger text. This study aimed to devise an extractive summarization method for A-133 Single Audits, which assess if recipients of federal grants a…
New scoring rules improve probabilistic classification model evaluation.
LLMs struggle with zero-shot annotation tasks due to model-internalized priors.
Extends denoising and score estimation to energy models via Tweedie's formula.
Mixed-SCORE+ improves community detection in weak signal networks.
Examining ESG scoring method for reliability.
New scoring rules for multivariate distributions and level sets.
Classifies intrinsically linked tournaments by their score sequences.
Score matching is a popular method for estimating unnormalized statistical models. However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions. We show this difficulty can be mitigated by projecting the scores onto random…
Improved score matching methods for estimating score functions and Hessians without high dimensionality.
The statistical leverage scores of a complex matrix record the degree of alignment between col and the coordinate axes in . These score are used in random sampling algorithms for solving certain numerical linear algebra problems. In this paper we present a max-plus algebr…
Traditional sequence-to-sequence (seq2seq) models and other variations of the attention-mechanism such as hierarchical attention have been applied to the text summarization problem. Though there is a hierarchy in the way humans use language by forming paragraphs from sentences and sentences from words, hierarchical mod…
Optimal score function estimation via empirical risk minimization
New ESGM scores include a 'Missing' pillar to account for unpublished ESG data.
The NL score optimizes speaker recognition tasks.
Regularizes attention scores in vision transformers using bootstrapping.
Null-Calibrated Conformal Selection via Target-Membership Scores
A framework for sensitivity measures using scoring functions.
Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions. In this paper, we study two issues that have not been completely resolved for score matching. First, we provide a formal link between maxim…
The paper establishes bounds for score-matching in causal discovery and generative modeling.
This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify…
In political redistricting, the compactness of a district is used as a quantitative proxy for its fairness. Several well-established, yet competing, notions of geographic compactness are commonly used to evaluate the shapes of regions, including the Polsby-Popper score, the convex hull score, and the Reock score, and t…
Paper bridges score estimation to parameter and density estimation in DDPMs.
In statistical analysis, measuring a score of predictive performance is an important task. In many scientific fields, appropriate scores were tailored to tackle the problems at hand. A proper score is a popular tool to obtain statistically consistent forecasts. Furthermore, a mathematical characterization of the proper…
Survival regression method improves log-likelihood scores.
The paper investigates the convergence of Vendi scores under finite samples and introduces a truncated version for better performance.
This paper introduces and develops a novel variable importance score function in the context of ensemble learning and demonstrates its appeal both theoretically and empirically. Our proposed score function is simple and more straightforward than its counterpart proposed in the context of random forest, and by avoiding …
Study compares different scoring rules for machine-learned weather forecasts, finding scale-awareness improves forecast realism.
Score-fPINN tackles high-dimensional FPL equations using fractional score functions.