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Found 33 papers, 20 papers with code
BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis
To the best of our knowledge, BIGCity is the first model capable of handling both trajectories and traffic states for diverse heterogeneous tasks.
The application of GPT-4 in grading design university students' assignment and providing feedback: An exploratory study
This study employs an iterative research approach in developing a Custom GPT with the aim of achieving more reliable results and testing whether it can provide design students with constructive feedback.
Exploring the Use of ChatGPT for a Systematic Literature Review: a Design-Based Research
Based on a SLR published, this study used ChatGPT to conduct a SLR of the same 33 papers in a design-based approach, to see what the differences are by comparing the reviews' results, and to answer: To what extent can ChatGPT conduct SLR?
KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition
In addition, we present a D-Hyperpoint KANsMixer module, which is recursively applied to nested groupings of D-Hyperpoints to learn the action discrimination information and creatively integrates Kolmogorov-Arnold Networks (KAN) to enhance spatio-temporal interaction within D-Hyperpoints.
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and reduce hallucinations.
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities.
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
To address the gap, we develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Relational Knowledge Bases.
An Improved Artificial Fish Swarm Algorithm for Solving the Problem of Investigation Path Planning
Our objective is to minimize costs, and to achieve this, we propose a chaotic artificial fish swarm algorithm based on multiple population differential evolution (DE-CAFSA).
Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression
In addition, most approaches ignore the spatial and channel redundancy.
MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
A central aspect of machine learning research is experimentation, the process of designing and running experiments, analyzing the results, and iterating towards some positive outcome (e. g., improving accuracy).
DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition
Graph Convolutional Networks (GCNs) have been widely used in skeleton-based human action recognition.
Enhancing Nucleus Segmentation with HARU-Net: A Hybrid Attention Based Residual U-Blocks Network
Additionally, we introduce a post-processing method that combines the target information and target contours to distinguish overlapping nuclei and generate an instance segmentation image.
A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology Classification
To address this issue, we propose a Voting-Stacking ensemble strategy, which employs three Inception networks as base learners and integrates their outputs through a voting ensemble.
Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception
Collaborative perception by leveraging the shared semantic information plays a crucial role in overcoming the individual limitations of isolated agents.
Med-Flamingo: a Multimodal Medical Few-shot Learner
However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness
Language model training in distributed settings is limited by the communication cost of gradient exchanges.
PRODIGY: Enabling In-context Learning Over Graphs
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.
Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
Despite recent success in large language model (LLM) reasoning, LLMs struggle with hierarchical multi-step reasoning tasks like generating complex programs.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Few-shot Relational Reasoning via Connection Subgraph Pretraining
Our pretrained model can then be directly applied to target few-shot tasks on without the need for training few-shot tasks.
Array Camera Image Fusion using Physics-Aware Transformers
We demonstrate a physics-aware transformer for feature-based data fusion from cameras with diverse resolution, color spaces, focal planes, focal lengths, and exposure.
Scatter Ptychography
Coherent illumination reflected by a remote target may be secondarily scattered by intermediate objects or materials.
Real-time 3D human action recognition based on Hyperpoint sequence
Instead of capturing spatio-temporal local structures, SequentialPointNet encodes the temporal evolution of static appearances to recognize human actions.
Edge Proposal Sets for Link Prediction
Here, we demonstrate how simply adding a set of edges, which we call a \emph{proposal set}, to the graph as a pre-processing step can improve the performance of several link prediction algorithms.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences
We employ depth motion images (DMI) as the templates to generate the multi-scale static representation of actions.
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
Node Classification on Non-Homophilic (Heterophilic) Graphs
Node Property Prediction
Better Set Representations For Relational Reasoning
Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope.
AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning.
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
We show that we can select a layer of the source model to perturb without any knowledge of the target models while achieving high transferability.
Intermediate Level Adversarial Attack for Enhanced Transferability
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models.
Multispectral Image Intrinsic Decomposition via Subspace Constraint
In this paper, a new Multispectral Image Intrinsic Decomposition model (MIID) is presented to decompose the shading and reflectance from a single multispectral image.
Multispectral Image Intrinsic Decomposition via Low Rank Constraint
In this paper, a Low Rank Multispectral Image Intrinsic Decomposition model (LRIID) is presented to decompose the shading and reflectance from a single multispectral image.
