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Found 30 papers, 11 papers with code
Generative AI for Cel-Animation: A Survey
Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colorization, which demand substantial manual effort, technical expertise, and significant time investment.
Domain-Agnostic Co-Evolution of Generalizable Parallel Algorithm Portfolios
Generalization is the core objective when training optimizers from data.
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models
TRON comprises two main components: (1) a novel conformal score to sample response sets of minimum size, and (2) a nonconformity score to identify high-quality responses based on self-consistency theory, controlling the error rates by two specific risk levels.
PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large Language Models
Emerging large language models (LLMs) offer a new approach to assessing complex communication metrics, with the potential to advance the field through integration into passive sensing and just-in-time intervention systems.
AudioInsight: Detecting Social Contexts Relevant to Social Anxiety from Speech
While previous research has found that the presence of a social interaction can be detected from ambient audio, the nuances within social contexts, which influence how anxiety provoking interactions are, remain largely unexplored.
Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents
This article explores the convergence of connectionist and symbolic artificial intelligence (AI), from historical debates to contemporary advancements.
ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees
Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (LLMs).
Estimating Difficulty Levels of Programming Problems with Pre-trained Model
As the demand for programming skills grows across industries and academia, students often turn to Programming Online Judge (POJ) platforms for coding practice and competition.
Learning Mixture-of-Experts for General-Purpose Black-Box Discrete Optimization
MEGO can be used as a standalone sample-efficient optimizer or in conjunction with existing search methods as an initial solution generator.
Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training
Our findings underscore the efficacy of the FSDT framework in effectively leveraging distributed offline reinforcement learning data to enable powerful multi-type agent decision systems.
Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond
Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering.
INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning
This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting.
P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer
Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance degradation when these agents face new tasks.
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses
In this paper, we investigate the retrieval-augmented generation (RAG) based on Knowledge Graphs (KGs) to improve the accuracy and reliability of Large Language Models (LLMs).
Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference
Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs.
Ranked #1 on
Fine-Grained Urban Flow Inference
on TaxiBJ-P4
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
However, the application of tensor decomposition is largely hindered by the exponential increment of the computational complexity and storage consumption with the size of tensors.
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction
Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making.
DiffuseGAE: Controllable and High-fidelity Image Manipulation from Disentangled Representation
To further explore the latent space of Diff-AE and achieve a generic editing pipeline, we proposed a module called Group-supervised AutoEncoder(dubbed GAE) for Diff-AE to achieve better disentanglement on the latent code.
LPN: Language-guided Prototypical Network for few-shot classification
Few-shot classification aims to adapt to new tasks with limited labeled examples.
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation
Then, we build a pure-MLP architecture called Caterpillar by replacing the convolutional layer with the SPC module in a hybrid model of sMLPNet.
Diversifying the High-level Features for better Adversarial Transferability
Incorporated into the input transformation-based attacks, DHF generates more transferable adversarial examples and outperforms the baselines with a clear margin when attacking several defense models, showing its generalization to various attacks and high effectiveness for boosting transferability.
Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline
To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety.
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work.
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning
Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data.
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning
Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be generalized to different environments during deployment.
Graph Neural Networks in IoT: A Survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on.
Socially-Optimal Mechanism Design for Incentivized Online Learning
Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment.
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares.
Enhance the performance of navigation: A two-stage machine learning approach
We model the traffic flow as a time series and apply XGBoost algorithm to get accurate predictions on future traffic conditions(1st stage).
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion.
