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06/06/2023
ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory
Large language models (LLMs) with memory are computationally universal. ...
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05/16/2023
Maybe Only 0.5 Training Data Instruction Tuning
Instruction tuning for large language models (LLMs) has gained attention...
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05/02/2023
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
The prompt-based learning paradigm, which bridges the gap between pre-tr...
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04/21/2023
Better Sign Language Translation with Monolingual Data
Sign language translation (SLT) systems, which are often decomposed into...
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10/10/2022
Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation
Past works on multimodal machine translation (MMT) elevate bilingual set...
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07/21/2022
ProMix: Combating Label Noise via Maximizing Clean Sample Utility
The ability to train deep neural networks under label noise is appealing...
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03/14/2022
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis
The aspect-based sentiment analysis (ABSA) is a fine-grained task that a...
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01/22/2022
PiCO: Contrastive Label Disambiguation for Partial Label Learning
Partial label learning (PLL) is an important problem that allows each tr...
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11/23/2021
Boosting Neural Machine Translation with Dependency-Scaled Self-Attention Network
The neural machine translation model assumes that syntax knowledge can b...
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12/22/2020
Multi-shot NAS for Discovering Adversarially Robust Convolutional Neural Architectures at Targeted Capacities
Convolutional neural networks (CNNs) are vulnerable to adversarial examp...
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08/17/2020
PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
The dominant approach for music representation learning involves the dee...
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06/24/2020
Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning
Accurate knowledge of the distribution system topology and parameters is...
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05/31/2020
Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning
This paper proposes a data-driven distributed voltage control approach b...
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05/31/2020
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
This paper proposes a data-driven approach for optimal power flow (OPF) ...
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05/11/2020
Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation
Power system dynamic state estimation (DSE) remains an active research a...
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11/14/2017
Prediction Under Uncertainty with Error-Encoding Networks
In this work we introduce a new framework for performing temporal predic...
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11/10/2016
Disentangling factors of variation in deep representations using adversarial training
We introduce a conditional generative model for learning to disentangle ...
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09/11/2016
Energy-based Generative Adversarial Network
We introduce the "Energy-based Generative Adversarial Network" model (EB...
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09/04/2015
Character-level Convolutional Networks for Text Classification
This article offers an empirical exploration on the use of character-lev...
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06/08/2015