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Found 10 papers, 3 papers with code
DELTA: A DEep learning based Language Technology plAtform
In this paper we present DELTA, a deep learning based language technology platform.
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On Loss Functions and Recurrency Training for GAN-based Speech Enhancement Systems
Recent work has shown that it is feasible to use generative adversarial networks (GANs) for speech enhancement, however, these approaches have not been compared to state-of-the-art (SOTA) non GAN-based approaches.
Audio and Speech Processing Sound
Robust Speaker Extraction Network Based on Iterative Refined Adaptation
dataset confirm the superior performance of the proposed method over the network without IRA in terms of SI-SDR and PESQ improvement.
Deep Multi-task Network for Delay Estimation and Echo Cancellation
In this paper, a multi-task network is proposed to address both ref-delay estimation and echo cancellation tasks.
Multi-objective Scheduling of Electric Vehicle Charging/Discharging with Time of Use Tariff
Optimal coordinated charging is a multi-objective optimization problem (MOOP) in nature, with objective functions such as minimum price charging and minimum disruptions to the grid.
Evolutionary Ensemble Learning for Multivariate Time Series Prediction
In this framework, a specific pipeline is encoded as a candidate solution and a multi-objective evolutionary algorithm is applied under different population sizes to produce multiple Pareto optimal sets (POSs).
Multi-task Optimization Based Co-training for Electricity Consumption Prediction
The performance of MTO-CT is evaluated on solving each of these two sets of tasks in comparison to solving each task in the set independently without knowledge sharing under the same settings, which demonstrates the superiority of MTO-CT in terms of prediction accuracy.
RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation
We propose a two-stage method, RuDi, that distills the knowledge of black-box teacher models into rule-based student models.
Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts
In this paper, we argue that different social media posts should consider different modalities for multimodal information extraction.
