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.
Algorithm recovers spike direction from modulo-reduced measurements in high dimensions.
Study identifies three quantization regimes for ReLU networks.
This paper introduces GLT for better input data representation in BNN and proposes a compact topology with block pruning.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
FasterVoiceGrad speeds up VC by 6-7x with novel distillation.
We propose a flexible framework that deals with both singer conversion and singers vocal technique conversion. The proposed model is trained on non-parallel corpora, accommodates many-to-many conversion, and leverages recent advances of variational autoencoders. It employs separate encoders to learn disentangled latent…
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…
CycleGAN-VC3 improves CycleGAN-VCs for mel-spectrogram conversion.
A new algorithm for conversational recommendation systems using dueling bandits in GLMs.
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.
Improved conversion rate prediction in online advertising using self-supervised pre-training.
A fast voice conversion method using diffusion models.
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…
Extracting relevant information from medical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation. We frame the pro…
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 …
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…
Emerging resistive random-access memory (ReRAM) has recently been intensively investigated to accelerate the processing of deep neural networks (DNNs). Due to the in-situ computation capability, analog ReRAM crossbars yield significant throughput improvement and energy reduction compared to traditional digital methods.…
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of lowresolution ADCs could largely red…
Innovative ball bearing converts rotary to reciprocating motion.
New method converts conventional ANNs to SNNs with minimal loss and efficiency.
Recent increases in basic food prices are severely impacting vulnerable populations worldwide. Proposed causes such as shortages of grain due to adverse weather, increasing meat consumption in China and India, conversion of corn to ethanol in the US, and investor speculation on commodity markets lead to widely differin…
Let be a homeomorphism between hyperbolic surfaces with finite topology. If is homotopic to a holomorphic map, then every closed geodesic in is at least as long as the corresponding geodesic in , by the Schwarz Lemma. The converse holds trivially when and are disks or annuli, and it holds…
This paper recovers smooth functions from noisy modulo samples using a three-stage strategy.
Group discussions are essential for organizing every aspect of modern life, from faculty meetings to senate debates, from grant review panels to papal conclaves. While costly in terms of time and organization effort, group discussions are commonly seen as a way of reaching better decisions compared to solutions that do…
New algorithm speeds up user preference learning in conversational contexts.
We propose a flexible framework for spectral conversion (SC) that facilitates training with unaligned corpora. Many SC frameworks require parallel corpora, phonetic alignments, or explicit frame-wise correspondence for learning conversion functions or for synthesizing a target spectrum with the aid of alignments. Howev…
Many-to-Many VTN improves voice conversion across multiple speakers.
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms natur…