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Found 17 papers, 5 papers with code
Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data
The MiMa model employs an encoder-decoder transformer structure, with two encoders for processing multivariate data from both datasets and a decoder for forecasting weather variables over short time horizons.
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning
In this paper, we introduce a novel bilateral backdoor to fill in the missing pieces of the puzzle in the cross-modal backdoor and propose a generalized invisible backdoor framework against cross-modal learning (BadCM).
Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model
Observing the lack of a benchmark for model training and evaluation over the MGSC task, we establish a benchmark with aligned masks and captions in multi-granularity using our customized automated annotation pipeline.
Towards Robust Vision Transformer via Masked Adaptive Ensemble
This design enables our ViT architecture to achieve a better trade-off between standard accuracy and robustness.
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative training of machine learning models over decentralized devices without exposing their local data.
An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions
Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices.
Higher-order Structure Based Anomaly Detection on Attributed Networks
Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks.
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of their data distributions.
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
Further, we, for the first time, formulate the formalized adversarial training of deep hashing into a unified minimax optimization under the guidance of the generated mainstay codes.
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer
In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops.
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
Federated learning (FL) has been widely deployed to enable machine learning training on sensitive data across distributed devices.
Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization
However, practical deployment of FL over mobile devices is very challenging because (i) conventional FL incurs huge training latency for mobile devices due to interleaved local computing and communications of model updates, (ii) there are heterogeneous training data across mobile devices, and (iii) mobile devices have hardware heterogeneity in terms of computing and communication capabilities.
Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking
This paper proposes a Hierarchical Multi-Interest Co-Network (HCN) to capture users' diverse interests in the coarse-grained ranking stage.
Accelerating Serverless Computing by Harvesting Idle Resources
Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions.
Improving Sequential Recommendation Consistency with Self-Supervised Imitation
In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.
Multiple-Input Multiple-Output Fusion Network For Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) has attracted consid- erable attention recently, which trains models with data from seen classes and tests on data from both seen and unseen classes.
Asymptotics of solutions with a compactness property for the nonlinear damped Klein-Gordon equation
We consider the nonlinear damped Klein-Gordon equation \[ \partial_{tt}u+2\alpha\partial_{t}u-\Delta u+u-|u|^{p-1}u=0 \quad \text{on} \ \ [0,\infty)\times \mathbb{R}^N \] with $\alpha>0$, $2 \le N\le 5$ and energy subcritical exponents $p>2$.
Analysis of PDEs
