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Found 20 papers, 6 papers with code
Reassessing Layer Pruning in LLMs: New Insights and Methods
Layer pruning, as a simple yet effective compression method, removes layers of a model directly, reducing computational overhead.
LLM Unlearning via Loss Adjustment with Only Forget Data
Existing approaches to LLM unlearning often rely on retain data or a reference LLM, yet they struggle to adequately balance unlearning performance with overall model utility.
Improving Data Efficiency via Curating LLM-Driven Rating Systems
Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws.
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond
Furthermore, we design three benchmark datasets focused on label noise detection, label noise learning, and class-imbalanced learning.
Memorization in deep learning: A survey
Deep Learning (DL) powered by Deep Neural Networks (DNNs) has revolutionized various domains, yet understanding the intricacies of DNN decision-making and learning processes remains a significant challenge.
Harnessing Business and Media Insights with Large Language Models
This paper introduces Fortune Analytics Language Model (FALM).
Measuring and Reducing LLM Hallucination without Gold-Standard Answers
FEWL leverages the answers from off-the-shelf LLMs that serve as a proxy of gold-standard answers.
Human-Instruction-Free LLM Self-Alignment with Limited Samples
The key idea is to first retrieve high-quality samples related to the target domain and use them as In-context Learning examples to generate more samples.
Distributionally Robust Post-hoc Classifiers under Prior Shifts
We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors.
Client-side Gradient Inversion Against Federated Learning from Poisoning
For the first time, we show the feasibility of a client-side adversary with limited knowledge being able to recover the training samples from the aggregated global model.
Do humans and machines have the same eyes? Human-machine perceptual differences on image classification
Trained computer vision models are assumed to solve vision tasks by imitating human behavior learned from training labels.
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning.
To Aggregate or Not? Learning with Separate Noisy Labels
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e. g., via crowdsourcing).
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
These observations require us to rethink the treatment of noisy labels, and we hope the availability of these two datasets would facilitate the development and evaluation of future learning with noisy label solutions.
Understanding Generalized Label Smoothing when Learning with Noisy Labels
It was shown that LS serves as a regularizer for training data with hard labels and therefore improves the generalization of the model.
Ranked #15 on
Learning with noisy labels
on CIFAR-10N-Worst
To Smooth or Not? When Label Smoothing Meets Noisy Labels
We provide understandings for the properties of LS and NLS when learning with noisy labels.
Ranked #9 on
Learning with noisy labels
on CIFAR-10N-Random3
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training
In this paper, we introduce DuelGAN, a generative adversarial network (GAN) solution to improve the stability of the generated samples and to mitigate mode collapse.
Ranked #3 on
Image Generation
on Fashion-MNIST
When Optimizing $f$-divergence is Robust with Label Noise
We show when maximizing a properly defined $f$-divergence measure with respect to a classifier's predictions and the supervised labels is robust with label noise.
Ranked #13 on
Learning with noisy labels
on CIFAR-10N-Aggregate
Incentives for Federated Learning: a Hypothesis Elicitation Approach
The success of a credible federated learning system builds on the assumption that the decentralized and self-interested users will be willing to participate to contribute their local models in a trustworthy way.
Sample Elicitation
We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.
