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Found 18 papers, 8 papers with code
CODA: Temporal Domain Generalization via Concept Drift Simulator
In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the "concept drift".
GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length
Our method progressively increases the training length throughout the pretraining phase, thereby mitigating computational costs and enhancing efficiency.
Graph Mixup with Soft Alignments
We conduct systematic experiments to show that S-Mixup can improve the performance and generalization of graph neural networks (GNNs) on various graph classification tasks.
Editable Graph Neural Network for Node Classifications
However, model editing for graph neural networks (GNNs) is rarely explored, despite GNNs' widespread applicability.
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation.
Data-centric Artificial Intelligence: A Survey
Artificial Intelligence (AI) is making a profound impact in almost every domain.
Weight Perturbation Can Help Fairness under Distribution Shift
In this paper, we first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and weight perturbation.
Retiring $Δ$DP: New Distribution-Level Metrics for Demographic Parity
Unfortunately, in this paper, we reveal that the fairness metric $\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: i) zero-value $\Delta DP$ does not guarantee zero violation of demographic parity, ii) $\Delta DP$ values can vary with different classification thresholds.
Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising
In this work, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a specific state and market environment via a sequential decision process.
Towards Generating Adversarial Examples on Mixed-type Data
The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues.
DIVISION: Memory Efficient Training via Dual Activation Precision
Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs).
G-Mixup: Graph Data Augmentation for Graph Classification
To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i. e., graphon) of different classes of graphs.
BED: A Real-Time Object Detection System for Edge Devices
DNNs have been an effective tool for data processing and analysis.
Geometric Graph Representation Learning via Maximizing Rate Reduction
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification.
FMP: Toward Fair Graph Message Passing against Topology Bias
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i. e., message passing) behind GNNs inducing unfairness issue remains unknown.
G-Mixup: Graph Augmentation for Graph Classification
To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i. e., graphon) of different classes of graphs.
Generalized Demographic Parity for Group Fairness
We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing.
An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Instance-dependent label noise (IDN) widely exists in real-world datasets and usually misleads the training of deep neural networks.
