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.
Tag2Vec learns tag representations in hybrid networks with semantic and hierarchical information.
Study interprets deep learning for LHC jet tagging.
Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web. While researchers have studied user behavior on the platforms in a variety of contexts, there is relatively little insight into important by-products of user behavior that also encode knowledge. Here, we analyze and model the macro…
In this paper, a new deep reinforcement learning based augmented general sequence tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence tagging model and a deep reinforcement learning (DRL) based augmented tagger. The augmented tagger helps improve system performance …
GTI network learns linguistic features for multi-task sequence tagging.
This paper creates a tagging system for paintings using historical descriptions.
Paper introduces a noisy-labeled audio tagging challenge.
Method learns audio embeddings with contextualized tags.
New neural network layer handles OOV words in NLP tasks without pre-training.
Improved POS tagging for Twitter data with limited annotations.
In this paper we consider two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. These are both basic, yet foundational preprocessing steps in applications such as text re-use detection. Nevertheless, they are generally complicated by the considerable orthographic variation which is ty…
Community detection in graphs has been extensively studied both in theory and in applications. However, detecting communities in hypergraphs is more challenging. In this paper, we propose a tensor decomposition approach for guaranteed learning of communities in a special class of hypergraphs modeling social tagging sys…
In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant (p < 10^-4) differences for state-of-the-art systems. For …
CrossFilter tackles noisy labels in audio tagging.
New method uses artificial data to improve biomedical text tagging.
CDSA uses self-attention to impute missing values in multivariate, geo-tagged time series data.
Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto…
Paper proposes set-valued prediction for historical POS tagging.
This paper automates tagging programming challenge descriptions.
Forward Automatic Differentiation (AD) is a technique for augmenting programs to compute derivatives. The essence of Forward AD is to attach perturbations to each number, and propagate these through the computation. When derivatives are nested, the distinct derivative calculations, and their associated perturbations, m…
New HMC method handles features in POS tagging, outperforming MEMM.
The paper extends influence functions to sequence tagging tasks for better model interpretability.
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon targe…
This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platform as "Freesound General-Purpose Audio Tagging Challenge". The goal of the task is to build an audio tagging system that can recognize the c…
Proposes COALA method for learning audio representations aligned with tags.
Quantum GNNs outperform classical GNNs in jet tagging.
To date, there have been massive Semi-Structured Documents (SSDs) during the evolution of the Internet. These SSDs contain both unstructured features (e.g., plain text) and metadata (e.g., tags). Most previous works focused on modeling the unstructured text, and recently, some other methods have been proposed to model …
Deep learning classifies animal behavior from wearable accelerometers.
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential approach to this task is taken by using an ordered sequence of jet constituents …
New method predicts target genres from source genres, unifying music tag systems.
We study the cluster categories arising from marked surfaces (with punctures and non-empty boundaries). By constructing skewed-gentle algebras, we show that there is a bijection between tagged curves and string objects. Applications include interpreting dimensions of as intersection numbers of ta…
ECN framework improves training on noisy structured labels.
Quantum analysis tags news for sentiment and entities.
MuLan links music audio to natural language tags.
Identifying the flavour of neutral mesons production is one of the most important components needed in the study of time-dependent violation. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present an inclusive flavour-tagging algorithm as an upgrade of…
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved di…
New loss function improves classification accuracy.
Enhances multi-tag classification using low-dimensional vector representations and virtual data.
New method tags malware for better understanding and identification.
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
New method aggregates nodes in sparse graphical models.
New algorithm estimates task affinities without repeated training, improving model performance and efficiency.
A multi-task learning model for slot tagging in biomedical domains.
Deep neural network predicts semantic labels for source code.
State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by usi…
In this paper, we explore the ways to improve POS-tagging using various types of auxiliary losses and different word representations. As a baseline, we utilized a BiLSTM tagger, which is able to achieve state-of-the-art results on the sequence labelling tasks. We developed a new method for character-level word represen…
Twitmo analyzes geo-tagged Twitter data for topic modeling and visualization.
A method for semi-supervised sound event detection using teacher-student learning.