Search Results for author:
Found 51 papers, 23 papers with code
Energy-based Generative Adversarial Network
We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions.
Character-level Convolutional Networks for Text Classification
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.
Ranked #16 on
Sentiment Analysis
on Yelp Fine-grained classification
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach
In this paper, considering scenarios with capacity budget, we aim to discover adversarially robust architecture at targeted capacities.
Contrastive Label Disambiguation for Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
Disentangling factors of variation in deep representations using adversarial training
During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class.
Prediction Under Uncertainty with Error-Encoding Networks
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
ProMix: Combating Label Noise via Maximizing Clean Sample Utility
To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples.
Ranked #1 on
Learning with noisy labels
on CIFAR-10N-Worst
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models
While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention.
PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE).
Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation
Thus, in this work, we introduce IKD-MMT, a novel MMT framework to support the image-free inference phase via an inversion knowledge distillation scheme.
Ranked #2 on
Multimodal Machine Translation
on Multi30K
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth.
CAME: Contrastive Automated Model Evaluation
In this work, we propose Contrastive Automatic Model Evaluation (CAME), a novel AutoEval framework that is rid of involving training set in the loop.
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis
The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval-2014 Task-4
(using extra training data)
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Energy-based Automated Model Evaluation
The core of the MDE is to establish a meta-distribution statistic, on the information (energy) associated with individual samples, then offer a smoother representation enabled by energy-based learning.
iDAG: Invariant DAG Searching for Domain Generalization
In this work, we first characterize that this failure of conventional ML models in DG is attributed to an inadequate identification of causal structures.
Towards Cross-Table Masked Pretraining for Web Data Mining
Tabular data pervades the landscape of the World Wide Web, playing a foundational role in the digital architecture that underpins online information.
Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility
With most of the related literature in the era of LLM uncharted, we propose an automated workflow that copes with an upscaled number of queries/responses.
Controllable Textual Inversion for Personalized Text-to-Image Generation
The recent large-scale generative modeling has attained unprecedented performance especially in producing high-fidelity images driven by text prompts.
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"
In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts.
Stacked What-Where Auto-encoders
The objective function includes reconstruction terms that induce the hidden states in the Deconvnet to be similar to those of the Convnet.
Latent Processes Identification From Multi-View Time Series
Understanding the dynamics of time series data typically requires identifying the unique latent factors for data generation, \textit{a. k. a.
Better Sign Language Translation with Monolingual Data
Sign language translation (SLT) systems, which are often decomposed into video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through the pivot gloss, heavily relies on the availability of large-scale parallel G2T pairs.
Disentangling factors of variation in deep representation using adversarial training
The only available source of supervision during the training process comes from our ability to distinguish among different observations belonging to the same category.
Prediction Under Uncertainty with Error Encoding Networks
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework.
BIG-bench Machine Learning
Physics-informed machine learning
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 based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm.
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 required to achieve good voltage controls, but this is difficult to obtain in practice.
Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment
Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO.
Systems and Control Systems and Control
Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation
Power system dynamic state estimation (DSE) remains an active research area.
A robust extended Kalman filter for power system dynamic state estimation using PMU measurements
Simulations carried out on the IEEE 39-bus test system reveal that our robust extended Kalman filter exhibits good tracking capabilities under Gaussian process and observation noise while suppressing observation outliers, even in position of leverage.
VeniBot: Towards Autonomous Venipuncture with Automatic Puncture Area and Angle Regression from NIR Images
For the automation, we focus on the positioning part and propose a Dual-In-Dual-Out network based on two-step learning and two-task learning, which can achieve fully automatic regression of the suitable puncture area and angle from near-infrared(NIR) images.
VeniBot: Towards Autonomous Venipuncture with Semi-supervised Vein Segmentation from Ultrasound Images
In the modern medical care, venipuncture is an indispensable procedure for both diagnosis and treatment.
Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network
However, succeeding researches pointed out that limited by the uncontrolled nature of attention computation, the NMT model requires an external syntax to capture the deep syntactic awareness.
Robust Data-Driven Linear Power Flow Model with Probability Constrained Worst-Case Errors
To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model.
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
Our method does not require external triggers and ensures correct labeling of poisoned samples, improving the stealthy nature of the backdoor attack.
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions.
ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory
Large language models (LLMs) with memory are computationally universal.
TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate.
Resilient Model-Free Asymmetric Bipartite Consensus for Nonlinear Multi-Agent Systems against DoS Attacks
In this letter, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi-agent systems with both cooperative and antagonistic interactions under denial-of-service (DoS) attacks.
Towards Domain-Specific Features Disentanglement for Domain Generalization
Distributional shift between domains poses great challenges to modern machine learning algorithms.
Deep Reinforcement Learning-Enabled Adaptive Forecasting-Aided State Estimation in Distribution Systems with Multi-Source Multi-Rate Data
Distribution system state estimation (DSSE) is paramount for effective state monitoring and control.
Revisiting the Knowledge Injection Frameworks
Briefly, the core of this technique is rooted in an ideological emphasis on the pruning and purification of the external knowledge base to be injected into LLMs.
Voltage-Dependent Electromechanical Wave Propagation Modeling for Dynamic Stability Analysis in Power Systems
Analyzing these characteristics enables the assessment of the impacts of EMW on the performance of the protection system.
Cyber-Physical Testbed Integrating RTAC with RTDS for Game-Theoretic Topology Control Under Load Altering Attacks
This paper introduces a cyber-physical testbed that integrates the Real-Time Digital Simulator (RTDS) with the Real-Time Automation Controller (RTAC) to enhance cybersecurity in electrical distribution networks.
3D-PreMise: Can Large Language Models Generate 3D Shapes with Sharp Features and Parametric Control?
Recent advancements in implicit 3D representations and generative models have markedly propelled the field of 3D object generation forward.
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process
Abstract—This paper addresses the distribution system state estimation (DSSE) with unknown topology change.
Scalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach
To address this challenge, our research presents a novel framework that harnesses the potential of Deep Reinforcement Learning (DRL), specifically utilizing the Importance Weighted Actor-Learner Architecture (IMPALA) algorithm, executed on the RAY platform.
RECOST: External Knowledge Guided Data-efficient Instruction Tuning
When it comes to datasets synthesized by LLMs, a common scenario in this field, dirty samples will even be selected with a higher probability than other samples.
Pre-Trained Model Recommendation for Downstream Fine-tuning
As a fundamental problem in transfer learning, model selection aims to rank off-the-shelf pre-trained models and select the most suitable one for the new target task.
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models
The fast advance of the image generation community has attracted attention worldwide.
