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Found 79 papers, 36 papers with code
TFPred: Learning Discriminative Representations from Unlabeled Data for Few-Label Rotating Machinery Fault Diagnosis
Recent advances in intelligent rotating machinery fault diagnosis have been enabled by the availability of massive labeled training data.
BDAN: Mitigating Temporal Difference Across Electrodes in Cross-Subject Motor Imagery Classification via Generative Bridging Domain
Based on the presented issue, a novel bridging domain adaptation network (BDAN) is proposed, aiming to minimise the data distribution difference across sessions in the aspect of the electrode, thus improving and enhancing model performance.
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities.
Enhancing Retinal Vascular Structure Segmentation in Images With a Novel Design Two-Path Interactive Fusion Module Model
Precision in identifying and differentiating micro and macro blood vessels in the retina is crucial for the diagnosis of retinal diseases, although it poses a significant challenge.
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
Additionally, DPA models user preferences as directions (i. e., unit vectors) in the reward space to achieve user-dependent preference control.
Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
We assess LLMs' zero-shot performance in creating domain-specific concept graphs and introduce TutorQA, a new expert-verified NLP-focused benchmark for scientific graph reasoning and QA.
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment
We consider the problem of multi-objective alignment of foundation models with human preferences, which is a critical step towards helpful and harmless AI systems.
BATON: Aligning Text-to-Audio Model with Human Preference Feedback
With the development of AI-Generated Content (AIGC), text-to-audio models are gaining widespread attention.
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
A total of 80 simulations were evaluated for the accuracy of initial diagnosis, top differential diagnosis and final two differential diagnoses.
FourCastNeXt: Optimizing FourCastNet Training for Limited Compute
FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements.
EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification
The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4. 02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios.
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data
A gene-level GNN is established to adaptively learn gene-gene interactions and cell representations via the self-attention mechanism, and a cell-level GNN builds on the cell-cell graph that is constructed from the cell representations generated by the gene-level GNN.
Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification
To address these issues in an end-to-end manner, we model the brain network as a directed acyclic graph (DAG) to discover direct causal connections between brain regions and propose Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer effective connectivity and classify rs-fMRI time series by learning brain representations based on nonlinear structural equation model.
Toward Real Text Manipulation Detection: New Dataset and New Solution
With the surge in realistic text tampering, detecting fraudulent text in images has gained prominence for maintaining information security.
An Unsupervised Machine Learning Scheme for Index-Based CSI Feedback in Wi-Fi
We explore several methods that consider different representations of the data in the candidate set.
Enhanced Index-Based Feedback Overhead Reduction for WLANs
Compressed beamforming algorithm is used in the current Wi-Fi standard to reduce the beamforming feedback overhead (BFO).
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.
A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems
Accurate and quick identification of high-impedance faults is critical for the reliable operation of distribution systems.
Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models
In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages.
GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models
In this work, we propose Goal-conditioned Offline Planning (GOPlan), a novel model-based framework that contains two key phases: (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies.
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
Notably, under the assumption of single policy coverage and the knowledge of $\zeta$, our proposed algorithm achieves a suboptimality bound that is worsened by an additive factor of $\mathcal{O}(\zeta (C(\widehat{\mathcal{F}},\mu)n)^{-1})$ due to the corruption.
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
Offline reinforcement learning (RL) presents a promising approach for learning reinforced policies from offline datasets without the need for costly or unsafe interactions with the environment.
Can Text-based Knowledge Graph Completion Benefit From Zero-Shot Large Language Models?
We found that (1) without fine-tuning, LLMs have the capability to further improve the quality of entity text descriptions.
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
Our research demonstrates the effectiveness of using UMLS-augmented LLMs and highlights the potential application value of LLMs in in medical question-answering.
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
Experimental results illustrate the superiority of RO2O in facilitating stable offline-to-online learning and achieving significant improvement with limited online interactions.
UniHead: Unifying Multi-Perception for Detection Heads
More precisely, our approach (1) introduces deformation perception, enabling the model to adaptively sample object features; (2) proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range dependencies, thereby achieving global perception; and (3) devises a Cross-task Interaction Transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks.
Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample Vicinity
Moreover, SVG generates a uniform distribution and stabilizes the training process of models.
Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
Educational materials such as survey articles in specialized fields like computer science traditionally require tremendous expert inputs and are therefore expensive to create and update.
Neural Networks for Programming Quantum Annealers
We explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer.
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools.
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
In this paper, we study out-of-distribution (OOD) generalization of offline GCRL both theoretically and empirically to identify factors that are important.
Accelerating Batch Active Learning Using Continual Learning Techniques
A major problem with Active Learning (AL) is high training costs since models are typically retrained from scratch after every query round.
ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification
Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classification.
BoxSnake: Polygonal Instance Segmentation with Box Supervision
Box-supervised instance segmentation has gained much attention as it requires only simple box annotations instead of costly mask or polygon annotations.
MGA: Medical generalist agent through text-guided knowledge transformation
To this end, we propose a novel medical generalist agent, MGA, that can address three kinds of common clinical tasks via clinical reports knowledge transformation.
SSGD: A smartphone screen glass dataset for defect detection
Interactive devices with touch screen have become commonly used in various aspects of daily life, which raises the demand for high production quality of touch screen glass.
Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution
The appealing features of RSD-OA include that: (1) RSD-OA is invariant to visual distractions, as it is conditioned on the predefined subsequent action sequence without task-irrelevant information from transition dynamics, and (2) the reward sequence captures long-term task-relevant information in both rewards and transition dynamics.
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions
To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space.
Learning Pseudo-Relations for Cross-domain Semantic Segmentation
Domain adaptive semantic segmentation aims to adapt a model trained on labeled source domain to the unlabeled target domain.
Dual adaptive training of photonic neural networks
Photonic neural network (PNN) is a remarkable analog artificial intelligence (AI) accelerator that computes with photons instead of electrons to feature low latency, high energy efficiency, and high parallelism.
On Distribution Grid Optimal Power Flow Development and Integration
Due to changes in electric distribution grid operation, new operation regimes have been recommended.
ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization
The ReLoc framework mainly consists of an image restoration module and a tampering localization module.
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids
Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness.
A Survey on Over-the-Air Computation
In such scenarios, information theoretical results show that harnessing the interference in a multiple access channel for computation, i. e., over-the-air computation (OAC), can provide a significantly higher achievable computation rate than separating communication and computation tasks.
Over-the-Air Computation over Balanced Numerals
In this study, a digital over-the-air computation (OAC) scheme for achieving continuous-valued gradient aggregation is proposed.
Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping
We validate our insight on a range of RL tasks and show its improvement over baselines: (1) In offline RL, the conservative exploitation leads to improved performance based on off-the-shelf algorithms; (2) In online continuous control, multiple value functions with different shifting constants can be used to tackle the exploration-exploitation dilemma for better sample efficiency; (3) In discrete control tasks, a negative reward shifting yields an improvement over the curiosity-based exploration method.
Resilience assessment and improvement for electric power transmission systems against typhoon disasters: A data-model hybrid driven approach
In response to the damage to electric power transmission systems caused by typhoon disasters in coastal areas, a planning-targeted resilience assessment framework that considers the impact of multiple factors is established to accurately find the weak links of the transmission system and improve the system resilience.
DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis
Our experiments and ablation studies verify that (i) the proposed DSCA could outperform existing state-of-the-art methods in cancer prognosis, by an average C-Index improvement of around 4. 6%; (ii) our DSCA network is more efficient in computation -- it has more learnable parameters (6. 31M vs. 860. 18K) but less computational costs (2. 51G vs. 4. 94G), compared to a typical existing multi-resolution network.
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
Offline reinforcement learning (RL) provides a promising direction to exploit massive amount of offline data for complex decision-making tasks.
Adaptive Online Learning of Quantum States
In the fundamental problem of shadow tomography, the goal is to efficiently learn an unknown $d$-dimensional quantum state using projective measurements.
UniInst: Unique Representation for End-to-End Instance Segmentation
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e. g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions.
Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions
Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios.
All-optical graph representation learning using integrated diffractive photonic computing units
Photonic neural networks perform brain-inspired computations using photons instead of electrons that can achieve substantially improved computing performance.
ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer
The vanilla self-attention mechanism inherently relies on pre-defined and steadfast computational dimensions.
Intelligent Feedback Overhead Reduction (iFOR) in Wi-Fi 7 and Beyond
The IEEE 802. 11 standard based wireless local area networks (WLANs) or Wi-Fi networks are critical to provide internet access in today's world.
RDP-Net: Region Detail Preserving Network for Change Detection
Moreover, current CNN models are heavy in parameters, which prevents their deployment on edge devices such as UAVs.
Change Detection
Change detection for remote sensing images
+1
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL
In this paper, we revisit the theoretical property of GCSL -- optimizing a lower bound of the goal reaching objective, and extend GCSL as a novel offline goal-conditioned RL algorithm.
Radiomic biomarker extracted from PI-RADS 3 patients support more eìcient and robust prostate cancer diagnosis: a multi-center study
Prostate Imaging Reporting and Data System (PI-RADS) based on multi-parametric MRI classi\^ees patients into 5 categories (PI-RADS 1-5) for routine clinical diagnosis guidance.
MHER: Model-based Hindsight Experience Replay
Replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method, model-based relabeling (MBR).
Two-Stage Stochastic Optimization via Primal-Dual Decomposition and Deep Unrolling
Then we propose a PDD-based stochastic successive convex approximation (PDD-SSCA) algorithmic framework to find KKT solutions for two-stage stochastic optimization problems.
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
Deep reinforcement learning methods have shown great performance on many challenging cooperative multi-agent tasks.
Ranked #1 on
SMAC+
on Off_Superhard_parallel
Unveiling personnel movement in a larger indoor area with a non-overlapping multi-camera system
Surveillance cameras are widely applied for indoor occupancy measurement and human movement perception, which benefit for building energy management and social security.
Customized quantum annealing schedules
The ground state of the Ising Hamiltonian encodes the solution to the computational problem of interest, and the state overlap with this ground state gives the success probability of the annealing protocol.
Quantum Physics
Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning
Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency.
Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method
In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances.
Gaussian Processes
Robotics
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks
In the variational E-step, graph topology is optimized by approximating the posterior probability distribution of the latent adjacency matrix with a neural network learned from node embeddings.
Annotation-efficient deep learning for automatic medical image segmentation
Automatic medical image segmentation plays a critical role in scientific research and medical care.
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification
Therefore, it can relieve the over-smoothing issue and learn effective node representations over graphs with homophily or heterophily.
Experimental Quantum Generative Adversarial Networks for Image Generation
For the first time, we experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
In this work, we enforce behavior regularization on learned policy as a general approach to offline RL, combined with a deterministic context encoder for efficient task inference.
Extended lifetime of respiratory droplets in a turbulent vapour puff and its implications on airborne disease transmission
Employing Lagrangian statistics, we demonstrate that the turbulent humid respiratory puff engulfs the small droplets, leading to many orders of magnitude increase in their lifetimes, implying that they can be transported much further during the respiratory events than the large ones.
Fluid Dynamics Applied Physics Computational Physics Medical Physics Physics and Society
A Locating Model for Pulmonary Tuberculosis Diagnosis in Radiographs
Significance: We first make full use of the feature extraction ability of CNNs in TB diagnostic field and make exploration in localization of TB, when the previous works focus on the weaker task of healthy-sick subject classification.
Learning Target-oriented Dual Attention for Robust RGB-T Tracking
RGB-Thermal object tracking attempt to locate target object using complementary visual and thermal infrared data.
Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity
In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification.
Robust Matrix Completion State Estimation in Distribution Systems
It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data.
Pedestrian Attribute Recognition: A Survey
We also review some popular network architectures which have been widely applied in the deep learning community.
Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning
In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL).
Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking
To refine the states of the target and re-track the target when it is back to view from heavy occlusion and out of view, we elaborately design a novel subnetwork to learn the target-driven visual attentions from the guidance of both visual and natural language cues.
Video Segmentation via Multiple Granularity Analysis
We introduce a Multiple Granularity Analysis framework for video segmentation in a coarse-to-fine manner.
