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.
This study identifies sentence relationships in legal transcripts.
We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal…
Study finds managers' tenure and education influence their choice between in-court and out-of-court restructuring.
Bi-LSTM classifies legal documents for Brazil's supreme court.
Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions…
Paper presents datasets from European Court of Human Rights judgments for classification studies.
What happens when the Supreme Court of the United States decides a case impacting one or more publicly-traded firms? While many have observed anecdotal evidence linking decisions or oral arguments to abnormal stock returns, few have rigorously or systematically investigated the behavior of equities around Supreme Court…
Recent advances in high-throughput cDNA sequencing (RNA-Seq) technology have revolutionized transcriptome studies. A major motivation for RNA-Seq is to map the structure of expressed transcripts at nucleotide resolution. With accurate computational tools for transcript reconstruction, this technology may also become us…
End-to-end ASR error detection using audio-transcript entailment.
TF-MoDISco finds transcription factor motifs from genomic data.
Adversarial learning improves music transcription accuracy.
siRF identifies transcription factor binding near enhancers in flies.
Computational approaches to transcription factor binding site identification have been actively researched for the past decade. Negative examples have long been utilized in de novo motif discovery and have been shown useful in transcription factor binding site search as well. However, understanding of the roles of nega…
Inverse Drum Machine separates drum mixes using transcription and synthesis.
Researchers predict NBA player salaries using machine learning, avoiding overfitting.
Motivation: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions, and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms…
Paper proposes M2H-GAN to improve speech theme identification.
Study on supply chain networks using wire transfers in Brazil.
Machine learning predicts ECHR judgments on human rights violations.
Study of SK-N-AS cells' response to methamidophos using transcriptomics.
A new ranking model uses nonnegative matrix factorization for tennis players.
Generative AI predicts economic activity from corporate transcripts.
Study detects SLI in children from spontaneous narrative transcripts.
In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled train…
Model earnings call transcripts for better stock price prediction.
We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and frames. Our model predicts pitch onset events and then uses those predictions to condition framewise pitch predictions. During inference, we r…
The regulatory process of Drosophila is thoroughly studied for understanding a great variety of biological principles. While pattern-forming gene networks are analysed in the transcription step, post-transcriptional events (e.g. translation, protein processing) play an important role in establishing protein expression …
To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations. The external conditions are mapped into the cell through the activation of special proteins called transcription factors (TFs). Due to the difficult …
Many of the recent approaches to polyphonic piano note onset transcription require training a machine learning model on a large piano database. However, such approaches are limited by dataset availability; additional training data is difficult to produce, and proposed systems often perform poorly on novel recording con…
Study analyzes deep learning models for financial sentiment in earnings calls.
We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our…
Neural networks predict TED Talk ratings from transcripts, removing bias.
Formulates LGFO to measure fair ML systems using legal signals.
Research shows higher damages may encourage more disclosure in corporate disputes.
New task aligns molecular structure with gene expression changes.
Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data, and have been widely used in two main tasks of Automatic Music Transcription (AMT): note segmentation, i.e. identifying the played notes after a multi-pitch estimation, and sequential post-processing, i.e. correcting note segmentation using tr…
The study constructs and shows isotopy of high-dimensional Legendrian spheres.
The paper improves ASR accuracy using semi-supervised learning and dropout.
Predicts customer call intent for auto dealerships using CNN.
Neural network generates music scores directly from polyphonic audio.
Model predicts active and passive cosponsorship motivations in Congress.
This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings. The translation-invariant network discussed in this paper, which combines a traditional filterbank with a convolutional neural network, was the top-performin…
Study combines speaker verification and voice trigger detection in a single network.
Automatic Music Transcription (AMT) consists in automatically estimating the notes in an audio recording, through three attributes: onset time, duration and pitch. Probabilistic Latent Component Analysis (PLCA) has become very popular for this task. PLCA is a spectrogram factorization method, able to model a magnitude …
Anonymization reduces economic signal extraction from financial texts.
We use automatic speech recognition to assess spoken English learner pronunciation based on the authentic intelligibility of the learners' spoken responses determined from support vector machine (SVM) classifier or deep learning neural network model predictions of transcription correctness. Using numeric features produ…
Framework ranks sectors influenced by Indian Union Budgets.
Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model stud…