Search Results for author:
Found 19 papers, 11 papers with code
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
In this paper, we introduce CoreInfer, an MLP-free adaptive sparse activation inference method based on sentence-level prediction.
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning
Dataset distillation (DD) is an increasingly important technique that focuses on constructing a synthetic dataset capable of capturing the core information in training data to achieve comparable performance in models trained on the latter.
Exploring the Impact of Dataset Bias on Dataset Distillation
Given that there are no suitable biased datasets for DD, we first construct two biased datasets, CMNIST-DD and CCIFAR10-DD, to establish a foundation for subsequent analysis.
Group Distributionally Robust Dataset Distillation with Risk Minimization
However, targeting the training dataset must be thought of as auxiliary in the same sense that the training set is an approximate substitute for the population distribution, and the latter is the data of interest.
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents
This process allows local devices to train smaller surrogate models while enabling the training of a larger global model on the server, effectively minimizing resource utilization.
Efficient Dataset Distillation via Minimax Diffusion
Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity, we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models.
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction
Machine learning plays a vital role in industrial HRI by enhancing the adaptability and autonomy of robots in complex environments.
Towards Building the Federated GPT: Federated Instruction Tuning
This repository offers a foundational framework for exploring federated fine-tuning of LLMs using heterogeneous instructions across diverse categories.
When Do Curricula Work in Federated Learning?
Specifically, we aim to investigate how ordered learning principles can contribute to alleviating the heterogeneity effects in FL.
Neural Routing in Meta Learning
Inspired by the widely recognized finding in neuroscience that distinct parts of the brain are highly specialized for different types of tasks, we aim to improve the model performance of the current meta learning algorithms by selectively using only parts of the model conditioned on the input tasks.
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
Though successful, federated learning presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises.
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces
This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
Classical federated learning approaches yield significant performance degradation in the presence of Non-IID data distributions of participants.
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
The traditional approach in FL tries to learn a single global model collaboratively with the help of many clients under the orchestration of a central server.
Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications
The approach is based on the concept of {\em self-rank}, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation.
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation.
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples
In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data.
A Novel Low-Complexity Framework in Ultra-Wideband Imaging for Breast Cancer Detection
In this research work, a novel framework is pro- posed as an efficient successor to traditional imaging methods for breast cancer detection in order to decrease the computational complexity.
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames
We use the sparsity of residual information in residual frames as the key point in devising our framework.
