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 converts echocardiography views using adversarial models.
Public debates are a common platform for presenting and juxtaposing diverging views on important issues. In this work we propose a methodology for tracking how ideas flow between participants throughout a debate. We use this approach in a case study of Oxford-style debates---a competitive format where the winner is det…
Abstract: Surveying connections between ML and Control Theory.
The paper analyzes how disturbances affect the convergence of algorithms in complex systems.
The paper explores how missing data problems are related to causal inference.
LineMVGNN improves AML detection by integrating multi-view graph learning.
Cochran, Orr and Teichner introduced --eta--invariants to detect highly non--trivial examples of non slice knots. Using a recent theorem by Lück and Schick we show that their metabelian --eta--invariants can be viewed as the limit of finite dimensional unitary representations. We recall a ribbon obstruction t…
A multiplicatively closed, horizontal foliation on a Lie groupoid may be viewed as a "pseudoaction" on the base manifold . A pseudoaction generates a pseudogroup of transformations of in the same way an ordinary Lie group action generates a transformation group. Infinitesimalizing a pseudoaction, one obtains the…
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
Framework converts singer identity and vocal technique from non-parallel corpora.
FasterVoiceGrad speeds up VC by 6-7x with novel distillation.
Automated system extracts medication regimens from medical conversations.
Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this pap…
FedConPE improves conversational recommender systems efficiency and privacy.
Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. However, the learning speed of the traditional contextual bandit algorithms is often slow due to the need for extensive exploration. This poses…
The goal of online display advertising is to entice users to "convert" (i.e., take a pre-defined action such as making a purchase) after clicking on the ad. An important measure of the value of an ad is the probability of conversion. The focus of this paper is the development of a computationally efficient, accurate, a…
The common view that our creativity is what makes us uniquely human suggests that incorporating research on human creativity into generative deep learning techniques might be a fruitful avenue for making their outputs more compelling and human-like. Using an original synthesis of Deep Dream-based convolutional neural n…
CycleGAN-VC3 improves CycleGAN-VCs for mel-spectrogram conversion.
A new algorithm for conversational recommendation systems using dueling bandits in GLMs.
This work bridges two views of feature learning in neural networks.
The paper extracts structured data from physician-patient conversations, reducing clerical burden.
Schedule-free SGD is optimal for nonconvex optimization problems.
Improved autoencoder for F0-consistent voice conversion.
Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, singer identification, and voice conversion. In this paper, we propose an intuitive spectrogram-based model for source separation by adapting U-…
Improved conversion rate prediction in online advertising using self-supervised pre-training.
A fast voice conversion method using diffusion models.
A method corrects feedback shift in predicting conversion rates with delayed feedback.
Proposes a new voice conversion model that preserves pitch patterns.
We present a voice conversion solution using recurrent sequence to sequence modeling for DNNs. Our solution takes advantage of recent advances in attention based modeling in the fields of Neural Machine Translation (NMT), Text-to-Speech (TTS) and Automatic Speech Recognition (ASR). The problem consists of converting be…
The paper provides a converse to linking theorems for graphs in 3-space and higher dimensions.
This paper proposes a voice conversion (VC) method using sequence-to-sequence (seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also the pitch contour and duration of input speech. The proposed method, called ConvS2S-VC, has three key features. First, it uses a model with a fully…
We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective SAR model. In this paper, we propose methods to effectively leverage abundant unlabeled …
This paper explores how LLMs can improve pipeline-based conversational agents.
A new framework converts EEG signals between subjects and tasks.
Unified approach for conversational recommendation by integrating attributes and items.
Neuro-symbolic system tackles conversational AI's need for natural, broad-ranging dialogue.
Estimates conversion probabilities from click sequences with privacy constraints.
Study finds neural dialog models struggle with conversational tasks.
Deep learning improves conversational recommender systems.
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ( hr), designed the task to represent the real word scenario, and explored several alignment approaches to iteratively improve data qua…
Operator fields in the bundle of Dirac spinors and their conversion to spatial fields are considered. Some commutator equations are studied with the use of the conversion technique.
In this paper we propose a multi-state model for the evaluation of the conversion option contract. The multi-state model is based on age-indexed semi-Markov chains that are able to reproduce many important aspects that influence the valuation of the option such as the duration problem, the time non-homogeneity and the …
Prospective display advertising poses a great challenge for large advertising platforms as the strongest predictive signals of users are not eligible to be used in the conversion prediction systems. To that end efforts are made to collect as much information as possible about each user from various data sources and to …
Hypersymplectic structures with torsion on Lie algebroids are investigated. We show that each hypersymplectic structure with torsion on a Lie algebroid determines three Nijenhuis morphisms. From a contravariant point of view, these structures are twisted Poisson structures. We prove the existence of a one-to-one corres…
MathChat uses LLM agents to solve challenging math problems through conversational problem-solving.
In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker. The proposed method makes use of both parallel and non-parallel utterances from the source and target simultaneously during training. This approach ca…
Innovative ball bearing converts rotary to reciprocating motion.
The problem to accurately and parsimoniously characterize random series of events (RSEs) present in the Web, such as e-mail conversations or Twitter hashtags, is not trivial. Reports found in the literature reveal two apparent conflicting visions of how RSEs should be modeled. From one side, the Poissonian processes, o…