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Found 52 papers, 7 papers with code
Large Language Models Overcome the Machine Penalty When Acting Fairly but Not When Acting Selfishly or Altruistically
We design three types of LLMs: (i) Cooperative, aiming to assist its human associate; (ii) Selfish, focusing solely on maximizing its self-interest; and (iii) Fair, balancing its own and collective interest, while slightly prioritizing self-interest.
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training
NEST-RQ employs causal encoders with only left context and uses next token prediction (NTP) as the training task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
Forensic pathology is critical in determining the cause and manner of death through post-mortem examinations, both macroscopic and microscopic.
A3S: A General Active Clustering Method with Pairwise Constraints
Active clustering aims to boost the clustering performance by integrating human-annotated pairwise constraints through strategic querying.
URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering
To address these limitations, we propose a novel Unified and Robust Representation Learning for Incomplete Multi-View Clustering (URRL-IMVC).
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report
In this competition, the organizers provided the multi-perspective camera images and standard-definition (SD) maps to explore the boundaries of scene reasoning capabilities.
From Redundancy to Relevance: Enhancing Explainability in Multimodal Large Language Models
To explore the interaction process between image and text in complex reasoning tasks, we introduce the information flow method to visualize the interaction mechanism.
A Blueprint Architecture of Compound AI Systems for Enterprise
Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases.
Data-driven Coordinated AC/DC Control Strategy for Frequency Safety
CEFC coordinates both the initiation and control amount of emergency DC power support (EDCPS) and traditional load shedding.
Dynamic Modeling and Stability Analysis for Repeated LVRT Process of Wind Turbine Based on Switched System Theory
A new type of voltage oscillation phenomenon induced by repeated low voltage ride-through (LVRT) of the wind turbine has been observed, threatening the safe and stable operation of such power systems.
LDTR: Transformer-based Lane Detection with Anchor-chain Representation
Despite recent advances in lane detection methods, scenarios with limited- or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated driving.
Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas
Third, we review the emergent field of leveraging AI agents to enhance cooperation among humans.
Mixed strategy approach destabilizes cooperation in finite populations with clustering coefficient
Our research goes beyond existing studies by revealing that the differences in cooperation levels between these strategic approaches are not confined to finite populations; they also depend on the clustering coefficients of these populations.
Concise and Organized Perception Facilitates Reasoning in Large Language Models
Exploiting large language models (LLMs) to tackle reasoning has garnered growing attention.
Forensic Histopathological Recognition via a Context-Aware MIL Network Powered by Self-Supervised Contrastive Learning
Forensic pathology is critical in analyzing death manner and time from the microscopic aspect to assist in the establishment of reliable factual bases for criminal investigation.
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data
Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data.
A Dynamic Equivalent Method for PMSG Based Wind Farms Under Asymmetrical Faults
In this paper, a three-machine equivalent method applicable to asymmetrical faults is proposed considering the operating wind speed and fault severity.
A Data-driven Under Frequency Load Shedding Scheme in Power Systems
Under frequency load shedding (UFLS) constitutes the very last resort for preventing total blackouts and cascading events.
Self-Learning Symmetric Multi-view Probabilistic Clustering
Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution.
CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection
A lane instance is first responded by the heat-map on the U-shaped curved guide line at global semantic level, thus the corresponding features of each lane are aggregated at the response point.
Ranked #1 on
Lane Detection
on CurveLanes
(Recall metric)
On Fast-Converged Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems under Transient Security Constraints
The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid.
Locating the Sources of Sub-synchronous Oscillations Induced by the Control of Voltage Source Converters Based on Energy Structure and Nonlinearity Detection
The oscillation phenomena associated with the control of voltage source converters (VSCs) are widely concerning, and locating the source of these oscillations is crucial to suppressing them; therefore, this paper presents a locating scheme, based on the energy structure and nonlinearity detection.
Exit options sustain altruistic punishment and decrease the second-order free-riders, but it is not a panacea
Altruistic punishment, where individuals incur personal costs to punish others who have harmed third parties, presents an evolutionary conundrum as it undermines individual fitness.
Equivalent Inertia Provided by Droop Control of Fast Frequency Regulation Resources
First, an equivalent-scenario-based method is proposed to evaluate the equivalent inertia provided by the droop control, which shows that the droop control with a constant droop coefficient provides time-variant equivalent inertia.
A Dynamic Equivalent Method for PMSG-WTG Based Wind Farms Considering wind Speeds and Fault Severities
In order to reduce the complexity of simulation of power systems including large-scale wind farms, it is critical to develop dynamic equivalent methods for wind farms which are applicable to the expected contingency analysis.
The Design of By-product Hydrogen Supply Chain Considering Large-scale Storage and Chemical Plants: A Game Theory Perspective
Hydrogen, an essential resource in the decarbonized economy, is commonly produced as a by-product of chemical plants.
Data-driven Emergency Frequency Control for Multi-Infeed Hybrid AC-DC System
This paper develops a fully data-driven linear quadratic regulator (LQR) for the HVDC to provide temporal frequency support.
Detection of Nonlinear Behavior in Voltage Source Converter Control in Wind Farms Based on Higher-Order Spectral Analysis
This paper focuses on the voltage source converter (VSC) control systems in wind farms and tries to detect the nonlinear behavior caused by the bilateral or unilateral saturation hard limits based on HOS analysis.
Distributed Emergency Frequency Control Considering Transient Stability Constraints in Multi-Infeed Hybrid AC-DC System
Both of these two control laws can guarantee transient stability constraints, restore system frequency and achieve the defined optimal control objective.
On Mitigating Hard Clusters for Face Clustering
Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images.
Incentive Mechanism Design for Emergency Frequency Control in Multi-Infeed Hybrid AC-DC System
In multi-infeed hybrid AC-DC (MIDC) systems, the emergency frequency control (EFC) with LCC-HVDC systems participating is of vital importance for system frequency stability.
Chance-constrained OPF: A Distributed Method with Confidentiality Preservation
Given the increased percentage of wind power in power systems, chance-constrained optimal power flow (CC-OPF) calculation, as a means to take wind power uncertainty into account with a guaranteed security level, is being promoted.
Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method
This method is based on a high-precision linear power flow model, whose precision is even further improved in this paper by an original correction approach.
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge
For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech.
MPC: Multi-View Probabilistic Clustering
Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the overall clustering performance.
Improving Pseudo-label Training For End-to-end Speech Recognition Using Gradient Mask
In the recent trend of semi-supervised speech recognition, both self-supervised representation learning and pseudo-labeling have shown promising results.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
A fault detection scheme for PV panels in large scale PV stations with complex installation conditions
Then, an orientation independent vector C is developed to eliminate the probability distribution differences of power outputs caused by varying azimuth angles and tilt angles.
VideoFlow: A Framework for Building Visual Analysis Pipelines
The past years have witnessed an explosion of deep learning frameworks like PyTorch and TensorFlow since the success of deep neural networks.
PPG-based singing voice conversion with adversarial representation learning
Singing voice conversion (SVC) aims to convert the voice of one singer to that of other singers while keeping the singing content and melody.
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
The performance of deep learning-based methods strongly relies on the number of datasets used for training.
PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation
Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution.
Ranked #5 on
Unbiased Scene Graph Generation
on Visual Genome
Cannot Predict Comment Volume of a News Article before (a few) Users Read It
We show that the early arrival rate of comments is the best indicator of the eventual number of comments.
Scene-based Factored Attention for Image Captioning
Specifically, the proposed module first embeds the scene concepts into factored weights explicitly and attends the visual information extracted from the input image.
Privacy-Preserving Distributed Parameter Estimation for Probability Distribution of Wind Power Forecast Error
Establishing the joint probability distribution of wind power and the corresponding forecast data of spatially correlated WFs is the foundation for deriving the conditional probability distribution.
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images.
Sharp Attention Network via Adaptive Sampling for Person Re-identification
In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem.
Deep learning and its application to medical image segmentation
However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision.
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks
Deep learning-based methods achieved impressive results for the segmentation of medical images.
Convolutional Neural Knowledge Graph Learning
In this paper, we try to learn more complex connections between entities and relationships.
