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Found 26 papers, 16 papers with code
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.
Benchmarking Large Language Models As AI Research Agents
Can we build AI research agents to perform these long-horizon tasks?
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
Collaboration by leveraging the shared semantic information plays a crucial role in overcoming the perception capability 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.
Ranked #7 on
Code Generation
on HumanEval
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.
SequentialPointNet: A strong frame-level parallel point cloud sequence network for 3D action recognition
The key to our approach is to divide the main modeling operations into frame-level units executed in parallel, which greatly improves the efficiency of modeling point cloud sequences. Moreover, we propose to flatten the point cloud sequence into a new point data type named hyperpoint sequence that preserves the complete spatial structure of each frame.
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.
